Journal articles on the topic 'Catastrophic Fault Model'
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1
Moradi, Mehrdad, Bert Van Acker, and Joachim Denil. "Failure Identification Using Model-Implemented Fault Injection with Domain Knowledge-Guided Reinforcement Learning." Sensors 23, no. 4 (February 14, 2023): 2166. http://dx.doi.org/10.3390/s23042166.
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The safety assessment of cyber-physical systems (CPSs) requires tremendous effort, as the complexity of cyber-physical systems is increasing. A well-known approach for the safety assessment of CPSs is fault injection (FI). The goal of fault injection is to find a catastrophic fault that can cause the system to fail by injecting faults into it. These catastrophic faults are less likely to occur, and finding them requires tremendous labor and cost. In this study, we propose a reinforcement learning (RL)-based method to automatically configure faults in the system under test and to find catastrophic faults in the early stage of system development at the model level. The proposed method provides a guideline to utilize high-level domain knowledge about a system model for constructing the reinforcement learning agent and fault injection setup. In this study, we used the system (safety) specification to shape the reward function in the reinforcement learning agent. The reinforcement learning agent dynamically interacted with the model under test to identify catastrophic faults. We compared the proposed method with random-based fault injection in two case studies using MATLAB/Simulink. Our proposed method outperformed random-based fault injection in terms of the severity and number of faults found.
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Li, Zhenhua, Junjie Cheng, and A. Abu-Siada. "Classification and Location of Transformer Winding Deformations using Genetic Algorithm and Support Vector Machine." (Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 14, no. 8 (December 23, 2021): 837–45. http://dx.doi.org/10.2174/2352096514666211026142216.
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Background: Winding deformation is one of the most common faults an operating power transformer experiences over its operational life. Thus, it is essential to detect and rectify such faults at early stages to avoid potential catastrophic consequences to the transformer. At present, methods published in the literature for transformer winding fault diagnosis are mainly focused on identifying fault type and quantifying its extent without giving much attention to the identification of fault location. Methods: This paper presents a method based on a genetic algorithm and support vector machine (GA-SVM) to improve the faults’ classification of power transformers in terms of type and location. In this regard, a sinusoidal sweep signal in the frequency range of 600 kHz to 1MHz is applied to one terminal of the transformer winding. : A mathematical index of the induced current at the head and end of the transformer winding under various fault conditions is used to extract unique features that are fed to a Support Vector Machine (SVM) model for training. Parameters of the SVM model are optimized using a Genetic Algorithm (GA). Results : The effectiveness of mathematical indicators to extract fault type characteristics and the proposed fault classification model for fault diagnosis is demonstrated through extensive simulation analysis for various transformer winding faults at different locations. Conclusion : The proposed model can effectively identify different fault types and determine their location within the transformer winding, and the diagnostic rates of the fault type and fault location are 100% and 90%, respectively.
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Dai, Huatong, Pengzhan Chen, and Hui Yang. "Metalearning-Based Fault-Tolerant Control for Skid Steering Vehicles under Actuator Fault Conditions." Sensors 22, no. 3 (January 22, 2022): 845. http://dx.doi.org/10.3390/s22030845.
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Using reinforcement learning (RL) for torque distribution of skid steering vehicles has attracted increasing attention recently. Various RL-based torque distribution methods have been proposed to deal with this classical vehicle control problem, achieving a better performance than traditional control methods. However, most RL-based methods focus only on improving the performance of skid steering vehicles, while actuator faults that may lead to unsafe conditions or catastrophic events are frequently omitted in existing control schemes. This study proposes a meta-RL-based fault-tolerant control (FTC) method to improve the tracking performance of vehicles in the case of actuator faults. Based on meta deep deterministic policy gradient (meta-DDPG), the proposed FTC method has a representative gradient-based metalearning algorithm workflow, which includes an offline stage and an online stage. In the offline stage, an experience replay buffer with various actuator faults is constructed to provide data for training the metatraining model; then, the metatrained model is used to develop an online meta-RL update method to quickly adapt its control policy to actuator fault conditions. Simulations of four scenarios demonstrate that the proposed FTC method can achieve a high performance and adapt to actuator fault conditions stably.
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Suthar, Venish, Vinay Vakharia, Vivek K. Patel, and Milind Shah. "Detection of Compound Faults in Ball Bearings Using Multiscale-SinGAN, Heat Transfer Search Optimization, and Extreme Learning Machine." Machines 11, no. 1 (December 26, 2022): 29. http://dx.doi.org/10.3390/machines11010029.
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Intelligent fault diagnosis gives timely information about the condition of mechanical components. Since rolling element bearings are often used as rotating equipment parts, it is crucial to identify and detect bearing faults. When there are several defects in components or machines, early fault detection becomes necessary to avoid catastrophic failure. This work suggests a novel approach to reliably identifying compound faults in bearings when the availability of experimental data is limited. Vibration signals are recorded from single ball bearings consisting of compound faults, i.e., faults in the inner race, outer race, and rolling elements with a variation in rotational speed. The measured vibration signals are pre-processed using the Hilbert–Huang transform, and, afterward, a Kurtogram is generated. The multiscale-SinGAN model is adapted to generate additional Kurtogram images to effectively train machine-learning models. To identify the relevant features, metaheuristic optimization algorithms such as teaching–learning-based optimization, and Heat Transfer Search are applied to feature vectors. Finally, selected features are fed into three machine-learning models for compound fault identifications. The results demonstrate that extreme learning machines can detect compound faults with 100% Ten-fold cross-validation accuracy. In contrast, the minimum ten-fold cross-validation accuracy of 98.96% is observed with support vector machines.
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Tian, Jing, Lili Liu, Fengling Zhang, Yanting Ai, Rui Wang, and Chengwei Fei. "Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals." Entropy 22, no. 1 (December 31, 2019): 57. http://dx.doi.org/10.3390/e22010057.
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Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monitor and diagnose inter-shaft bearing faults. With respect to the nonstationary and nonlinear of inter-shaft bearing AE signals, this paper presents a novel fault diagnosis method of inter-shaft bearing called the multi-domain entropy-random forest (MDERF) method by fusing multi-domain entropy and random forest. Firstly, the simulation test of inter-shaft bearing faults is conducted to simulate the typical fault modes of inter-shaft bearing and collect the data of AE signals. Secondly, multi-domain entropy is proposed as a feature extraction approach to extract the four entropies of AE signal. Finally, the samples in the built set are divided into two subsets to train and establish the random forest model of bearing fault diagnosis, respectively. The effectiveness and generalization ability of the developed model are verified based on the other experimental data. The proposed fault diagnosis method is validated to hold good generalization ability and high diagnostic accuracy (~0.9375) without over-fitting phenomenon in the fault diagnosis of bearing shaft.
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Handwerger, Alexander L., Alan W. Rempel, Rob M. Skarbek, Joshua J. Roering, and George E. Hilley. "Rate-weakening friction characterizes both slow sliding and catastrophic failure of landslides." Proceedings of the National Academy of Sciences 113, no. 37 (August 29, 2016): 10281–86. http://dx.doi.org/10.1073/pnas.1607009113.
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Catastrophic landslides cause billions of dollars in damages and claim thousands of lives annually, whereas slow-moving landslides with negligible inertia dominate sediment transport on many weathered hillslopes. Surprisingly, both failure modes are displayed by nearby landslides (and individual landslides in different years) subjected to almost identical environmental conditions. Such observations have motivated the search for mechanisms that can cause slow-moving landslides to transition via runaway acceleration to catastrophic failure. A similarly diverse range of sliding behavior, including earthquakes and slow-slip events, occurs along tectonic faults. Our understanding of these phenomena has benefitted from mechanical treatments that rely upon key ingredients that are notably absent from previous landslide descriptions. Here, we describe landslide motion using a rate- and state-dependent frictional model that incorporates a nonlocal stress balance to account for the elastic response to gradients in slip. Our idealized, one-dimensional model reproduces both the displacement patterns observed in slow-moving landslides and the acceleration toward failure exhibited by catastrophic events. Catastrophic failure occurs only when the slip surface is characterized by rate-weakening friction and its lateral dimensions exceed a critical nucleation length h* that is shorter for higher effective stresses. However, landslides that are extensive enough to fall within this regime can nevertheless slide slowly for months or years before catastrophic failure. Our results suggest that the diversity of slip behavior observed during landslides can be described with a single model adapted from standard fault mechanics treatments.
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Kim, Heonkook, Hyeyun Jeong, Hojin Lee, and Sang Woo Kim. "Online and Offline Diagnosis of Motor Power Cables Based on 1D CNN and Periodic Burst Signal Injection." Sensors 21, no. 17 (September 3, 2021): 5936. http://dx.doi.org/10.3390/s21175936.
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We introduce a new approach for online and offline soft fault diagnosis in motor power cables, utilizing periodic burst injection and nonintrusive capacitive coupling. We focus on diagnosing soft faults because local cable modifications or soft faults that occur without any indication while the cable is still operational can eventually develop into hard faults; furthermore, advance diagnosis of soft faults is more beneficial than the later diagnosis of hard faults, with respect to preventing catastrophic production stoppages. Both online and offline diagnoses with on-site diagnostic ability are needed because the equipment in the automated lines operates for 24 h per day, except during scheduled maintenance. A 1D CNN model was utilized to learn high-level features. The advantages of the proposed method are that (1) it is suitable for wiring harness cables in automated factories, where the installed cables are extremely short; (2) it can be simply and identically applied for both online and offline diagnoses and to a variety of cable types; and (3) the diagnosis model can be directly established from the raw signal, without manual feature extraction and prior domain knowledge. Experiments conducted with various fault scenarios demonstrate that this method can be applied to practical cable faults.
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Liu, Jinfu, Mingliang Bai, Zhenhua Long, Jiao Liu, Yujia Ma, and Daren Yu. "Early Fault Detection of Gas Turbine Hot Components Based on Exhaust Gas Temperature Profile Continuous Distribution Estimation." Energies 13, no. 22 (November 14, 2020): 5950. http://dx.doi.org/10.3390/en13225950.
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Failures of the gas turbine hot components often cause catastrophic consequences. Early fault detection can detect the sign of fault occurrence at an early stage, improve availability and prevent serious incidents of the plant. Monitoring the variation of exhaust gas temperature (EGT) is an effective early fault detection method. Thus, a new gas turbine hot components early fault detection method is developed in this paper. By introducing a priori knowledge and quantum particle swarm optimization (QPSO), the exhaust gas temperature profile continuous distribution model is established with finite EGT measuring data. The method eliminates influences of operating and ambient condition changes and especially the gas swirl effect. The experiment reveals the presented method has higher fault detection sensitivity.
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Milic, Miljana, and Vanco Litovski. "Oscillation-based testing method for detecting switch faults in High-Q SC biquad filters." Facta universitatis - series: Electronics and Energetics 28, no. 2 (2015): 223–36. http://dx.doi.org/10.2298/fuee1502223m.
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Testing switched capacitor circuits is a challenge due to the diversity of the possible faults. A special problem encountered is the synthesis of the test signal that will control and make the fault-effect observable at the test point. The oscillation based method which was adopted for testing in these proceedings resolves that important issue in its nature. Here we discuss the properties of the method and the conditions to be fulfilled in order to implement it in the right way. To achieve that, we have resolved the problem of synthesis of the positive feed-back circuit and the choice of a proper model of the operational amplifier. In that way, a realistic foundation to the testing process was generated. A second order notch cell was chosen as a case-study. Fault dictionaries were developed related to the catastrophic faults of the switches used within the cell. The results reported here are a continuation of our previous work and are complimentary to some other already published.
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Siddiqui, Khadim Moin, Kuldeep Sahay, and V. K. Giri. "Stator Inter-turn Fault Detection in Inverter Fed Induction Motor Drives." International Journal of Applied Power Engineering (IJAPE) 6, no. 2 (August 1, 2017): 89. http://dx.doi.org/10.11591/ijape.v6.i2.pp89-102.
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The Squirrel Cage Induction Motor (SCIM) with advanced power electronic inverters presents the greater advantages on cost and energy efficiency as compared with other industrial solutions for varying speed applications. In recent, the inverter fed induction motors are being popular in the industries. These inverter fed-motors are recently gathering great recognition for multimegawatt industrial drive applications. In this present paper, a dynamic simulation model of PWM inverter fed SCIM with direct torque control jointly has been presented and analyzed in the recent MATLAB/Simulink environment. From the proposed simulation model, the transient behavior of SCIM has been analysed for healthy as well as for stator inter-turn fault condition. The dynamic simulation of induction motor is one of the key steps in the validation of design process of the electric motor and drive system. It is extremely needed for eliminating probable faults beforehand due to inadvertent design mistakes and changes during operation. The simulated model gives encouraging results with reduced harmonics [1]. By using the model, the successful detection of stator inter-turn fault of the SCIM is carried out in the transient condition. Therefore, early stator fault detection is possible and may avoid the motor to reach in the catastrophic conditions. Therefore, may save millions of dollars for industries.
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Siddiqui, Khadim Moin, Kuldeep Sahay, and V. K. Giri. "Stator Inter-turn Fault Detection in Inverter Fed Induction Motor Drives." International Journal of Applied Power Engineering (IJAPE) 6, no. 2 (August 1, 2017): 90. http://dx.doi.org/10.11591/ijape.v6.i2.pp90-103.
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The Squirrel Cage Induction motor(SCIM) with advanced power electronic inverters presents the greater advantages on cost and energy efficiency as compared with other industrial solutions for varying speed applications. In recent, the inverter fed induction motors are being popular in the industries. These inverter fed-motors are recently gathering great recognition for multi-megawatt industrial drive applications. In this present paper, a dynamic simulation model of PWM inverter fed SCIM with direct torque control jointly has been presented and analyzed in the recent MATLAB/Simulink environment. From the proposed simulation model, the transient behavior of SCIM has been analysed for healthy as well as for stator inter-turn fault condition. The dynamic simulation of induction motor is one of the key steps in the validation of design process of the electric motor and drive system. It is extremely needed for eliminating probable faults beforehand due to inadvertent design mistakes and changes during operation. The simulated model gives encouraging results with reduced harmonics [1]. By using the model, the successful detection of stator inter-turn fault of the SCIM is carried out in the transient condition. Therefore, early stator fault detection is possible and may avoid the motor to reach in the catastrophic conditions. Therefore, may save millions of dollars for industries.
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Jadim, Ramsey, Mirka Kans, Mohammed Alhattab, and May Alhendi. "A Novel Condition Monitoring Procedure for Early Detection of Copper Corrosion Problems in Oil-Filled Electrical Transformers." Energies 14, no. 14 (July 14, 2021): 4266. http://dx.doi.org/10.3390/en14144266.
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The negative impacts of catastrophic fire and explosion accidents due to copper corrosion problems of oil-filled electrical transformers are still in the spotlight due to a lack of effective methods for early fault detection. To address this gap, a condition monitoring (CM) procedure that can detect such problems in the initial stage is proposed in this paper. The suggested CM procedure is based on identified measurable variables, which are the relevant by-products of the corrosion reaction, and utilizes an Early Fault Diagnosis (EFD) model to detect and solve the copper corrosion problems. The EFD model includes a fault trend chart that can track a fault progression during the useful life of transformers. The purpose of this paper is to verify and validate the effectiveness of the suggested CM procedure by an empirical study in a power plant. The result of applying this procedure was early detection of copper corrosion problems in two transformers with suspected copper corrosion propagation from a total of 84. The corrective action was adding an optimized amount of a passivator, an anticorrosion additive, to suppress the corrosion reaction at the correct time. The main conclusion of this study is the importance of early detection of transformer faults to avoid the negative impacts on societal, company, and individual levels.
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Arellano-Espitia, Francisco, Miguel Delgado-Prieto, Artvin-Darien Gonzalez-Abreu, Juan Jose Saucedo-Dorantes, and Roque Alfredo Osornio-Rios. "Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems." Sensors 21, no. 17 (August 30, 2021): 5830. http://dx.doi.org/10.3390/s21175830.
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The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.
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Alamelu Manghai, T. M., and R. Jegadeeshwaran. "Vibration based brake health monitoring using wavelet features: A machine learning approach." Journal of Vibration and Control 25, no. 18 (July 3, 2019): 2534–50. http://dx.doi.org/10.1177/1077546319859704.
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In this study, the application of wavelets has been investigated for diagnosing the faults on a hydraulic brake system of a light motor vehicle using the vibration signals acquired from a brake test setup through a piezoelectric type accelerometer. An efficient brake system should provide reliable and effective performance in order to ensure safety . If it is not properly monitored, it may lead to a serious catastrophic effect such as accidents, frequent breakdown, etc. Hence, the brake system needs to be monitored continuously. The condition of the brake components and the vibration signals are interrelated. If the failure starts progressing, the vibration magnitude will also progress. Analyzing the vibration signals under the various fault conditions is the key process in fault diagnosis. In recent decades wavelets have been focused on in many fault diagnosis studies as the wavelets decompose the complex information into simple form with high precision for further analysis. The wavelet features were extracted in order to retrieve the information from the vibration signals using discrete wavelet transform. From that discretized signal under each fault condition, the relevant features were extracted and feature selection was carried out. The selected features were then classified using a set of machine learning classifiers such as best first tree (pre-pruning, post-pruning, and unpruned), Hoeffding tree (HT), support vector machine, and neural network. The classification accuracies of all the algorithms were compared and discussed. Among the considered classifier model, the HT model produced a better classification accuracy as 99.45% for the hydraulic brake fault diagnosis.
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Goyal, Nupur, and Mangey Ram. "Stochastic modelling of a wind electric generating power plant." International Journal of Quality & Reliability Management 34, no. 1 (January 3, 2017): 103–27. http://dx.doi.org/10.1108/ijqrm-09-2015-0143.
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Purpose The purpose of this paper is to analyse the performance of a wind electric generating power plant through the study of reliability measures. The enhancement of the performance of the wind power plant using various approaches is also an objective of this paper. Design/methodology/approach This paper describes two models of a wind electric generating power plant using the Markov process and supplementary variable technique and solved with the help of Laplace transformation. The first model has been analyzed without fault coverage and Gumbel-Hougaard family of copula, while the second model of the wind power plant employs fault coverage and Gumbel-Hougaard family of copula which are used to enhance the performance. The proposed methodology is then illustrated in detail considering numerical examples. Findings Numerous reliability characteristics such as availability, reliability and mean time to failure to examine the performance of the wind power plant have been investigated. Through the comparative study of both the models, the authors concluded that the plant can generate electricity over long periods of time by covering more and more detected faults, which is made possible with two types of repair facility. Originality/value In this work, the authors have developed a mathematical model based on a wind electric generating power plant. This work incorporates not only the component failures that stop or degrade the working of the plant but also deals with the catastrophic and repair strategy of the plant.
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Shankar, Karthik V., Kailasnath K, and S. Babu Devasenapati. "Design of Fault Detection System for Automobile Power Train Using Digital Signal Processing and Soft Computing Techniques." International Journal of Manufacturing, Materials, and Mechanical Engineering 4, no. 3 (July 2014): 50–63. http://dx.doi.org/10.4018/ijmmme.2014070103.
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The increasing dependence of internal combustion engine in multitudes of application has mandated a detailed study on most of its subsystems. This paper focuses on predictive maintenance using machine learning based models. The transmission system of any power pace is often challenged due to sudden variation in applied load. Any fault in the transmission system could lead to the catastrophic failures hence need for this work. This paper deals with the identification of various fault conditions that happen in a transmission system using vibration signals acquired by an accelerometer. The acquired signals are processed to extract the statistical and spectral features. These features are used to build a machine learning model using decision tree or Random forest algorithm. The best combination of features and algorithm is evaluated and the results are presented.
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Zheng, Junhui, Hui Xiong, Yuchang Zhang, Kaige Su, and Zheyuan Hu. "Bearing Fault Diagnosis via Incremental Learning Based on the Repeated Replay Using Memory Indexing (R-REMIND) Method." Machines 10, no. 5 (May 6, 2022): 338. http://dx.doi.org/10.3390/machines10050338.
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In recent years, deep-learning schemes have been widely and successfully used to diagnose bearing faults. However, as operating conditions change, the distribution of new data may differ from that of previously learned data. Training using only old data cannot guarantee good performance when handling new data, and vice versa. Here, we present an incremental learning scheme based on the Repeated Replay using Memory Indexing (R-REMIND) method for bearing fault diagnosis. R-REMIND can learn new information under various working conditions while retaining older information. First, we use a feature extraction network similar to the Inception-v4 neural network to collect bearing vibration data. Second, we encode the features by product quantization and store the features in indices. Finally, the parameters of the feature extraction and classification networks are updated using real and reconstructed features, and the model did not forget old information. The experiment results show that the R-REMIND model exhibits continuous learning ability with no catastrophic forgetting during sequential tasks.
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Li, Rui, Chao Ran, Bin Zhang, Leng Han, and Song Feng. "Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM." Applied Sciences 10, no. 16 (August 11, 2020): 5542. http://dx.doi.org/10.3390/app10165542.
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Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.
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Liu, Jinfu, Zhenhua Long, Mingliang Bai, Linhai Zhu, and Daren Yu. "A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems." Energies 14, no. 2 (January 12, 2021): 389. http://dx.doi.org/10.3390/en14020389.
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As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at the turbine outlet are used to monitor the exhaust gas temperature (EGT) to indirectly monitor the performance of the combustion system, but, the EGT is not only affected by faults but also influenced by many interference factors, such as ambient conditions, operating conditions, rotation and mixing of uneven hot gas, performance degradation of compressor, etc., which will reduce the sensitivity and reliability of fault detection. For this reason, many scholars have devoted themselves to the research of combustion system fault detection and proposed many excellent methods. However, few studies have compared these methods. This paper will introduce the main methods of combustion system fault detection and select current mainstream methods for analysis. And a circumferential temperature distribution model of gas turbine is established to simulate the EGT profile when a fault is coupled with interference factors, then use the simulation data to compare the detection results of selected methods. Besides, the comparison results are verified by the actual operation data of a gas turbine. Finally, through comparative research and mechanism analysis, the study points out a more suitable method for gas turbine combustion system fault detection and proposes possible development directions.
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Balzano, Fabio, Mario L. Fravolini, Marcello R. Napolitano, Stéphane d’Urso, Michele Crispoltoni, and Giuseppe del Core. "Air Data Sensor Fault Detection with an Augmented Floating Limiter." International Journal of Aerospace Engineering 2018 (November 25, 2018): 1–16. http://dx.doi.org/10.1155/2018/1072056.
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Although very uncommon, the sequential failures of all aircraft Pitot tubes, with the consequent loss of signals for all the dynamic parameters from the Air Data System, have been found to be the cause of a number of catastrophic accidents in aviation history. This paper proposes a robust data-driven method to detect faulty measurements of aircraft airspeed, angle of attack, and angle of sideslip. This approach first consists in the appropriate selection of suitable sets of model regressors to be used as inputs of neural network-based estimators to be used online for failure detection. The setup of the proposed fault detection method is based on the statistical analysis of the residual signals in fault-free conditions, which, in turn, allows the tuning of a pair of floating limiter detectors that act as time-varying fault detection thresholds with the objective of reducing both the false alarm rate and the detection delay. The proposed approach has been validated using real flight data by injecting artificial ramp and hard failures on the above sensors. The results confirm the capabilities of the proposed scheme showing accurate detection with a desirable low level of false alarm when compared with an equivalent scheme with conventional “a priori set” fixed detection thresholds. The achieved performance improvement consists mainly in a substantial reduction of the detection time while keeping desirable low false alarm rates.
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Li, Xueying, Peng Ren, Zhe Zhang, Xiaohan Jia, and Xueyuan Peng. "A p−V Diagram Based Fault Identification for Compressor Valve by Means of Linear Discrimination Analysis." Machines 10, no. 1 (January 10, 2022): 53. http://dx.doi.org/10.3390/machines10010053.
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The pressure-volume diagram (p−V diagram) is an established method for analyzing the thermodynamic process in the cylinder of a reciprocating compressor as well as the fault of its core components including valves. The failure of suction/discharge valves is the most common cause of unscheduled shutdowns, and undetected failure may lead to catastrophic accidents. Although researchers have investigated fault classification by various estimation techniques and case studies, few have looked deeper into the barriers and pathways to realize the level determination of faults. The initial stage of valve failure is characterized in the form of mild leakage; if this is identified at this period, more serious accidents can be prevented. This study proposes a fault diagnosis and severity estimation method of the reciprocating compressor valve by virtue of features extracted from the p−V diagram. Four-dimensional characteristic variables consisting of the pressure ratio, process angle coefficient, area coefficient, and process index coefficient are extracted from the p−V diagram. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were applied to establish the diagnostic model, where PCA realizes feature amplification and projection, then LDA implements feature dimensionality reduction and failure prediction. The method was validated by the diagnosis of various levels of severity of valve leakage in a reciprocating compressor, and further, applied in the diagnosis of two actual faults: Mild leakage caused by the cracked valve plate in a reciprocating compressor, and serious leakage caused by the deformed valve in a hydraulically driven piston compressor for a hydrogen refueling station (HRS).
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Alizadeh, Mohsen, Mazlan Hashim, Esmaeil Alizadeh, Himan Shahabi, Mohammad Karami, Amin Beiranvand Pour, Biswajeet Pradhan, and Hassan Zabihi. "Multi-Criteria Decision Making (MCDM) Model for Seismic Vulnerability Assessment (SVA) of Urban Residential Buildings." ISPRS International Journal of Geo-Information 7, no. 11 (November 14, 2018): 444. http://dx.doi.org/10.3390/ijgi7110444.
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Earthquakes are among the most catastrophic natural geo-hazards worldwide and endanger numerous lives annually. Therefore, it is vital to evaluate seismic vulnerability beforehand to decrease future fatalities. The aim of this research is to assess the seismic vulnerability of residential houses in an urban region on the basis of the Multi-Criteria Decision Making (MCDM) model, including the analytic hierarchy process (AHP) and geographical information system (GIS). Tabriz city located adjacent to the North Tabriz Fault (NTF) in North-West Iran was selected as a case study. The NTF is one of the major seismogenic faults in the north-western part of Iran. First, several parameters such as distance to fault, percent of slope, and geology layers were used to develop a geotechnical map. In addition, the structural construction materials, building materials, size of building blocks, quality of buildings and buildings-floors were used as key factors impacting on the building’s structural vulnerability in residential areas. Subsequently, the AHP technique was adopted to measure the priority ranking, criteria weight (layers), and alternatives (classes) of every criterion through pair-wise comparison at all levels. Lastly, the layers of geotechnical and spatial structures were superimposed to design the seismic vulnerability map of buildings in the residential area of Tabriz city. The results showed that South and Southeast areas of Tabriz city exhibit low to moderate vulnerability, while some regions of the north-eastern area are under severe vulnerability conditions. In conclusion, the suggested approach offers a practical and effective evaluation of Seismic Vulnerability Assessment (SVA) and provides valuable information that could assist urban planners during mitigation and preparatory phases of less examined areas in many other regions around the world.
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Ding, Yu, Fei Wang, Zhen-ya Wang, and Wen-jin Zhang. "Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF." Complexity 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/8740989.
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Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR) method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data.
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G. O., Nwoke. "Mitigating Faults and Revenue Losses Using Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 883–90. http://dx.doi.org/10.22214/ijraset.2021.39072.
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Abstract: Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults. Keywords: Faults and Revenue Losses
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Zhou, Qiting, Gang Mao, and Yongbo Li. "A fusion CNN driven by images and vibration signals for fault diagnosis of gearbox." Journal of Physics: Conference Series 2252, no. 1 (April 1, 2022): 012076. http://dx.doi.org/10.1088/1742-6596/2252/1/012076.
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Abstract Gearbox diagnosis is critical for avoiding catastrophic failure and minimizing financial damages. Aiming at the problem that the vibration-based fault diagnosis methods cannot effectively identify the non-structural failure mode and the diagnosis model based on the infrared thermal image is not robust enough, a fusion fault diagnosis method for gearboxes using vibration signals and infrared images is proposed. By fusing these two kinds of heterogeneous data, the proposed method can identify both structural and unstructured health states while maintaining high robustness. In addition, CNN has powerful image processing capabilities, which can directly process two-dimensional infrared images and achieve high accuracy. Finally, a gearbox experiment is carried out to test the performance of our method. The results suggest that the proposed fusion CNN can obtain the highest accuracy compared with some methods based on single signals, shallow learning methods SVM and deep unsupervised learning methods SAE.
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Zhou, Qiting, Gang Mao, and Yongbo Li. "A fusion CNN driven by images and vibration signals for fault diagnosis of gearbox." Journal of Physics: Conference Series 2252, no. 1 (April 1, 2022): 012076. http://dx.doi.org/10.1088/1742-6596/2252/1/012076.
Full textAbstract:
Abstract Gearbox diagnosis is critical for avoiding catastrophic failure and minimizing financial damages. Aiming at the problem that the vibration-based fault diagnosis methods cannot effectively identify the non-structural failure mode and the diagnosis model based on the infrared thermal image is not robust enough, a fusion fault diagnosis method for gearboxes using vibration signals and infrared images is proposed. By fusing these two kinds of heterogeneous data, the proposed method can identify both structural and unstructured health states while maintaining high robustness. In addition, CNN has powerful image processing capabilities, which can directly process two-dimensional infrared images and achieve high accuracy. Finally, a gearbox experiment is carried out to test the performance of our method. The results suggest that the proposed fusion CNN can obtain the highest accuracy compared with some methods based on single signals, shallow learning methods SVM and deep unsupervised learning methods SAE.
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Refaee, Eshrag A. "Using Machine Learning for Performance Classification and Early Fault Detection in Solar Systems." Mathematical Problems in Engineering 2022 (April 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/6447434.
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The steady increase in the world’s population has directly influenced global climate change, resulting in catastrophic environmental consequences. This has created an immediate need for scientists from interdisciplinary domains like clean technology innovation in solar energy and computer science to join in the effort to save the world for future generations. As such, the United Nations has set a goal to ensure global access to affordable, sustainable, and clean energy. As a leading influential G20 economy, Saudi Arabia has recently established the Green Saudi initiative to align with the UN goal for enhancing the use of green energy. However, research in this area is sparse and greater effort is still required. This work is among the first to address the issue of enhancing and expanding the use of clean energy by means of studying the data collected from solar plants around Saudi. We used machine learning-based methods to assess the energy output performance of solar plants and employed the collected data to train the models to make early detection of faults. Our models achieved the highest performance at an accuracy score of 98.85% and 0.98 weighted F-score using the J48 model trained on a publicly available dataset of 874 instances collected from 26 different sites across Saudi. We anticipate that the findings of this work to serve as testbed to facilitate further research in this area and enhance the early fault detection in solar energy stations.
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Feng, Zhenmin, Dongmei Huang, Zhian Li, Rui Li, and Yupeng Sun. "Probabilistic Analysis of Wheel Loader Failure under Rockfall Conditions Based on Bayesian Network." Mathematical Problems in Engineering 2021 (October 31, 2021): 1–16. http://dx.doi.org/10.1155/2021/2744264.
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Rockfall is one of the most serious geological hazards in mountain regions. During the rescue situations after rockfall, the wheel loader, a vital type of modern engineering mechanism, plays an important role in relieving the obstruction of the catastrophic site. Increasing the reliability of the wheel loader during the rescue situation is quite important. This study aims to build a fault diagnosis model based on Bayesian network (BN) to diagnose the probability and path of the fault occurrence in the wheel loader during a rockfall disaster. Meanwhile, to reduce the influence of subjective factors, the fuzzy set theory is introduced into BN. The result showed that the probability of failure of the wheel loader under rockfall disaster is 13.11%. In addition, the key cause of the failure of the wheel loader under the rockfall disaster is the malfunction of mechanical parts. The probability of mechanical component failures in this case is as high as 88%, while the probability of human error is 6%. The research results not only show the ability of the BN to incorporate subjective judgment but also can provide a reference for fault diagnosis and risk assessment of wheel loaders under rockfall disaster conditions.
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Sanchaa, A. M., N. N. Nevedrova, and N. V. Shtabel. "DEEP STRUCTURE OF THE FAULT ZONE IN THE MUKHOR-TARKATA SITE OF THE CHUYA DEPRESSION ACCORDING TO NON-STATIONARY ELECTROMAGNETIC SOUNDING DATA USING THREE-DIMENSIONAL MODELING." Geology and mineral resources of Siberia, no. 2 (2021): 67–73. http://dx.doi.org/10.20403/2078-0575-2021-2-67-73.
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The paper presents the results of three-dimensional modeling of the fault structure in the central part of the Chuya depression in Gornyi Altai within the Mukhor-Tarkhata plot. On this site, from 2004 to the present, researchers of the IPGG SB RAS have been conducting regular annual observations by the method of nearfield time-domain electromagnetic sounding (TSB) to observe the process of restoration of the geological environment after the catastrophic Chuya earthquake with a magnitude of 7.3 in 2003. One of the aftereffects of the destructive earthquake is fracture zones expressed on the surface. Numerous deformations of the surface and industrial objects are observed in the area of the Mukhor-Tarkhata village. The fault zone with sub-vertical fracturing, identified at the site by geological and electromagnetic data, was activated after the earthquake. Detailed data on the geoelectric structure are required for the interpretation and analysis of monitoring data. Based on the interpretation of the near-field time-domain electromagnetic sounding data, the deep structure of the fault zone was obtained. To verify and clarify structural features of the geoelectric model, three-dimensional modeling was performed.
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Hebbouche, Abdelhamid, Mahmoud Bensaibi, and Hussein Mroueh. "Seismic Risk Analysis of Concrete Gravity Dams under Near-Fault Ground Motions." Applied Mechanics and Materials 256-259 (December 2012): 2240–43. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.2240.
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There are a large number of concrete dams worldwide. Some of the dams are in areas prone to seismicity and were built many years ago with minimal consideration to seismic loads. Dam safety during and after an earthquake, is the aim of the present study. The failure of a dam during an earthquake will be catastrophic in terms of human life and financial losses. In the present work, an analytical fragility analysis was performed in order to characterize the seismic vulnerability of concrete gravity dams by using a probabilistic method to model sources of uncertainty that could impact dam performance. The assessment of the seismic vulnerability of concrete gravity dams under near-fault ground motions was performed to assess their performance against seismic hazards. A case study was considered, it is about the dam of Oued el Fodda on the Oued Chelif River, West Algeria. This dam was designed in the early 1930s.
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Sun, Xiaozhe, Xingjian Wang, Zhiyuan Zhou, and Zhihan Zhou. "Active Fault-Tolerant Control Strategy for More Electric Aircraft under Actuation System Failure." Actuators 9, no. 4 (November 27, 2020): 122. http://dx.doi.org/10.3390/act9040122.
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The aircraft hydraulic system is very important for the actuation system and its failure has led to a number of catastrophic accidents in the past few years. The reasons for hydraulic loss can be leakage, blockage, and structural damage. Fortunately, the development of more electric aircraft (MEA) provides a new means of solving this difficult problem. This paper designs an active fault tolerant control (AFTC) method for MEA suffering from total hydraulic loss and actuation system failure. Two different kinds of scenarios are considered: leakage/blockage and vertical tail damage. With the application of the dissimilar redundant actuation system (DRAS) in MEA, a switching mechanism can be used to change the hydraulic actuation (HA) system into an electro-hydrostatic actuation (EHA) system when the whole hydraulic system fails. Taking account of the gap between HA and EHA, a degraded model is built. As for vertical tail damage, engine differential thrust control is adopted to help regain lateral-directional stability. The engine thrust dynamics are modeled and the mapping relationship between engine differential thrust and rudder deflection is formulated. Moreover, model reference control (MRC) and linear quadratic regulator (LQR) are used to design the AFTC method. Comparative simulation with the NASA generic transportation model (GTM) is carried out to prove the proposed strategy.
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Tyagi, Vaishali, Ritu Arora, Mangey Ram, and Ioannis S. Triantafyllou. "Copula based Measures of Repairable Parallel System with Fault Coverage." International Journal of Mathematical, Engineering and Management Sciences 6, no. 1 (October 29, 2020): 322–44. http://dx.doi.org/10.33889/ijmems.2021.6.1.021.
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The main objective of this study is to analyse the reliability behaviour of parallel systems with three types of failure, namely unit failure, human failure and major failure. For this purpose, we apply three different statistical techniques, namely copula, coverage and copula-coverage. More precisely, this study presents a stochastic model for analysing the behaviour of a multi-state system consisting of two non-identical units by incorporating the concept of coverage factor and two types of repair facilities between failed state to a normal state. The system could be characterized as being in a failed state due to unit failures, human failure and major failures, such as catastrophic and environmental failure. All failure rates are constant and it is assumed that these are exponentially distributed whereas, repair rates follow the Gumbel-Hougaard copula distribution. The entire system is modelled as a finite-state Markov process. Time-dependent reliability measures like availability, reliability and mean time to failure (MTTF) are obtained by supplementary variable techniques and Laplace transformations. The present study provides a comparative analysis for reliability measures among the aforementioned techniques, while a discussion referring to which technique makes the system more reliable is also developed. Furthermore, numerical simulations are presented to validate the analytical results.
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Ozirkovskyy, Leonid, Bohdan Volochiy, Oleksandr Shkiliuk, Mykhailo Zmysnyi, and Pavlo Kazan. "Functional safety analysis of safety-critical system using state transition diagram." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 2 (May 18, 2022): 145–58. http://dx.doi.org/10.32620/reks.2022.2.12.
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The subject of research is to determine the functional safety indicators of a fault-tolerant safety-critical system, namely, the minimal cut sets’ probability for a given duration of the system’s operation, using the state transition diagram (STD). The aim is to create a new method for analyzing the functional safety of a fault-tolerant safety-critical system. This method is based on the methodology of developing models of operational reliability behavior in the form of STD. This methodology provides a detailed representation of inoperable states and their relation with pre-failure (inoperable critical) states. The task is to propose a new classification for inoperable states of the STD to obtain all possible emergencies in the same space of inoperable states. This approach allows consideration the correlations between the failures, that it is impossible to use the fault trees. Since the space of inoperable states can reach hundreds and thousands of states, a method is proposed for their automated determination according to the classification. The state space method was used to conduct the validation of the method of functional safety analysis. The following results were obtained: the system of Chapman-Kolmogorov differential equations is formed in accordance with the STD and it provides the dependence of the functional safety indicator – the minimal cut sets’ probability as a function of the operational duration of the fault-tolerant safety-critical system. This dependence is called the emergency function. The method for determining the emergency function is based on the usage of the emergency mask. Note that the proposed model of operational reliability behavior in the form of STD provides the possibility to conduct both the functional safety and the reliability indicators. The value of the minimal cut sets’ probability for a given duration of operation is determined using the fault tree for the validation of the proposed method of functional safety analysis. The fault tree was built by Reliasoft BlockSim software. The obtained value coincides with the value of the minimal cut sets’ probability, which was defined by the emergency function for the same operational duration. Thus, the designer can comprehensively analyze the feasibility of introducing redundancy (structural, temporal, functional). Conclusions: the scientific novelty of the obtained results is the following: the new method for determining safe, critical and catastrophic states in the set of inoperable states is used in the methodology of the STD developing to obtain the stochastic model of operational reliability behavior of fault-tolerant safety-critical system. This technique ensures an automated defining of emergency function by using an improved structural-automatic model.
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LIU, HUA-SHU, LIN MA, YUAN-TONG GU, and SHAWN NIELSEN. "NUMERICAL INVESTIGATION OF MECHANICAL AND THERMAL DYNAMIC PROPERTIES OF THE INDUSTRIAL TRANSFORMER." International Journal of Computational Methods 11, supp01 (November 2014): 1344012. http://dx.doi.org/10.1142/s021987621344012x.
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Industrial transformer is one of the most critical assets in the power and heavy industry. Failures of transformers can cause enormous losses. The poor joints of the electrical circuit on transformers can cause overheating and results in stress concentration on the structure which is the major cause of catastrophic failure. Few researches have been focused on the mechanical properties of industrial transformers under overheating thermal conditions. In this paper, both mechanical and thermal properties of industrial transformers are jointly investigated using finite element analysis (FEA). Dynamic response analysis is conducted on a modified transformer FEA model, and the computational results are compared with experimental results from literature to validate this simulation model. Based on the FEA model, thermal stress is calculated under different temperature conditions. These analysis results can provide insights to the understanding of the failure of transformers due to overheating, therefore are significant to assess winding fault, especially to the manufacturing and maintenance of large transformers.
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Yu, Tao, Ying Yang, Qing Kai Han, Hong Liang Yao, and Bang Chun Wen. "ANN-Based Crack Identification in Rotor System with Multi-Crack in Shaft." Key Engineering Materials 353-358 (September 2007): 2463–66. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.2463.
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Rotating machinery, such as steam turbo, compressor, and aeroengine etc., are widely used in many industrial fields. Among the important rotor faults, the fatigue crack fault, which can lead to catastrophic failure and cause injuries and severe damage to machinery if undetected in its early stages, is most difficult to detect efficiently with traditional methods. In the paper, based on the truth of the change of the mode shapes of the cracked structure, a new method by combining accurate finite element model of rotor with multi-crack in shaft and artificial neural network (ANN) is proposed to identify the location and depth of cracks in rotating machinery. First, based on fracture mechanics and the energy principle of Paris, the accurate FE model of the rotor system considering several localized on-edge non-propagating open cracks with different depth, is built to produce the specific mode shapes. Then a set of different mode shapes of a rotor system with localized cracks in several different positions and depths, which will be treated as the input of the designed ANN model, can be obtained by repeating the above step. At last, with several selected crack cases, the errors between the results obtained by using the trained ANN model and FEM ones are compared and illustrated. Meanwhile, the influences of crack in the different position on the identification success are analyzed. The method is validated on the test-rig and proved to have good effectiveness in identification process.
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Evans, S. G., R. H. Guthrie, N. J. Roberts, and N. F. Bishop. "The disastrous 17 February 2006 rockslide-debris avalanche on Leyte Island, Philippines: a catastrophic landslide in tropical mountain terrain." Natural Hazards and Earth System Sciences 7, no. 1 (January 24, 2007): 89–101. http://dx.doi.org/10.5194/nhess-7-89-2007.
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Abstract. In February 2006, a disastrous rockslide-debris avalanche occurred in tropical mountain terrain, on Leyte Island, Central Philippines. Over 1100 people perished when the village of Guinsaugon was overwhelmed directly in the path of the landslide. The landslide was initiated by the failure of a 450 m high rock slope within the damage zone of the Philippine Fault where the rock mass consisted of sheared and brecciated volcanic, sedimentary and volcaniclastic rocks. Tectonic weakening of the failed rock mass had resulted from active strike-slip movements along the Philippine Fault which have been estimated by other workers at 2.5 cm/year. The landslide involved a total volume of 15 Mm3, including significant entrainment from its path, and ran out a horizontal distance of 3800 m over a vertical distance of 810 m, equivalent to a fahrböschung of 12°. Run-out distance was enhanced by friction reduction due to undrained loading when the debris encountered flooded paddy fields in the valley bottom at a path distance of 2600 m. A simulation of the event using the dynamic analysis model DAN indicated a mean velocity of 35 m/s and demonstrated the contribution of the paddy field effect to total run-out distance. There was no direct trigger for the landslide but the landslide did follow a period of very heavy rainfall with a lag time of four days. The rockslide-debris avalanche is one of several disastrous landslides to have occurred in the Philippines in the last twenty years. In terms of loss of life, the Guinsaugon event is the most devastating single-event landslide to have occurred worldwide since the Casita Volcano rock avalanche-debris flow which was triggered by Hurricane Mitch in Nicaragua in 1998.
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Tang, Xinye, Dezhong Jiang, and Botao Guo. "Spacecraft hitch detection and health evaluation based on Multivariable Time Series." Journal of Physics: Conference Series 2366, no. 1 (November 1, 2022): 012031. http://dx.doi.org/10.1088/1742-6596/2366/1/012031.
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Abstract In this paper, unsupervised method is studied to solve the problem that it is difficult for spacecraft to obtain tagged hitch data. Firstly, the incremental cross-correlation filtered attribute selection algorithm (ICF) is used to complete the selection of feature subsets of spacecraft multivariate time series (MTS);Then the unsupervised learning model of LSTM-SAE is trained on a large number of normal data; Finally, a complete hitch monitoring and health evaluation system is established and verified on the data of three fault degrees. The feature space of normal data is constructed, and the health state of spacecraft is measured by the reconstruction error of faulted data and the distance of feature space. It solves the problem of predicting and avoiding catastrophic failures of spacecraft under the conditions of lack of prior knowledge, unbalanced data distribution and incomplete failure modes.
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Jesus, Thiago, Paulo Portugal, Francisco Vasques, and Daniel Costa. "Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks." Sensors 18, no. 8 (August 10, 2018): 2629. http://dx.doi.org/10.3390/s18082629.
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Wireless sensor networks have been considered as an effective solution to a wide range of applications due to their prominent characteristics concerning information retrieving and distributed processing. When visual information can be also retrieved by sensor nodes, applications acquire a more comprehensive perception of monitored environments, fostering the creation of wireless visual sensor networks. As such networks are being more often considered for critical monitoring and control applications, usually related to catastrophic situation prevention, security enhancement and crises management, fault tolerance becomes a major expected service for visual sensor networks. A way to address this issue is to evaluate the system dependability through quantitative attributes (e.g., reliability and availability), which require a proper modeling strategy to describe the system behavior. That way, in this paper, we propose a methodology to model and evaluate the dependability of wireless visual sensor networks using Fault Tree Analysis and Markov Chains. The proposed modeling strategy considers hardware, battery, link and coverage failures, besides considering routing protocols on the network communication behavior. The methodology is automated by a framework developed and integrated with the SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool. The achieved results show that this methodology is useful to compare different network implementations and the corresponding dependability, enabling the uncovering of potentially weak points in the network behavior.
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Sun, Xiuquan, Tie Wang, Ruiliang Zhang, Fengshou Gu, and Andrew D. Ball. "Numerical Modelling of Vibration Responses of Helical Gears under Progressive Tooth Wear for Condition Monitoring." Mathematics 9, no. 3 (January 21, 2021): 213. http://dx.doi.org/10.3390/math9030213.
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Gear wear is a common fault that occurs in a gear transmission system that degrades the operating efficiency and may cause other catastrophic failures such as tooth breakage and fatigue. The progressive wear of a helical gear and its influences on vibration responses are rarely investigated due to the combined effects of the complicated lubrication state and the time-varying characteristic. To fill this gap, a numerical study was put forward to investigate the interactions between gear wear and dynamic response. In this study, an Archard’s wear model with elastohydradynamic lubrication (EHL) effect is adopted to simulate the helical gear wear, which is incorporated with an eight-degree of freedom dynamic model for understanding the gear dynamic at different wear degrees. The wear model shows that the gear wear mainly happens at the gear root due to the relative high slide-to-roll ratio. The dynamic modelling results demonstrate that the wear causes a reduction in time-varying gear mesh stiffness further leads to more vibration. Besides, the simulated vibration responses and experimental validation show that the wear cause increases in the amplitudes of the gear mesh frequency and its harmonics, which can reflect the evolution of progressive gear wear and can be used as monitoring features of gear wear.
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Victor, Irwan Mangatur, Zulkifli Djunaidi, and M. Fauzan Guciano. "IMPLEMENTASI TECHNOLOGICAL RISK ANALYSIS DALAM ANALISIS RISIKO KEGIATAN RIG MOVE-OUT WITHOUT SHUTDOWN DI PT X." PREPOTIF : Jurnal Kesehatan Masyarakat 6, no. 2 (June 29, 2022): 1379–91. http://dx.doi.org/10.31004/prepotif.v6i2.4055.
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PT X merupakan perusahaan MIGAS yang beroperasi di area Mahakam Kalimantan Timur, lapangan yang dioperasikan masuk pada kategori mature dimana sumur gas di anjungan lepas pantai sangat sensitif terhadap kenaikan tekanan dan shutdown, untuk itu diperlukan upaya untuk bisa mengurangi kebutuhan shutdown termasuk saat kegiatan rig move yang dari analisa risiko sebelumnya membutuhkan full platform shutdown. Belum ada metode yang teruji dan sistematis untuk analisis risiko pada kegiatain Rig move out without shutdown di lepas pantai Mahakam, untuk itu penilaian risiko dengan metode Technological Risk Assesment (TRA) berbasis skenario dipilih dikarenakan aktifitas rig move berfrekuensi rendah namun memiliki tingkat konsekuensi pada kategori catastrophic/disastrous. TRA dilakukan dengan menerapkan 5 langkah analisa risiko yaitu; Hazard Indentification (HAZID), Preliminary Risk Assesment (RA), Detailed Risk Analysis (DRA), Evaluasi risiko dengan pendekatan ALARP (As Low As Reasonably Practicable) dan kemudian menetapkan action plan (langkah pencegahan) dan implementasi dari Risk Reduction Measures (Risk Treatment). Dua major hazard yang teridentifkasi pada operasi rig move adalah rig instability dan rig uncontrolled movement. Penilaian risiko awal dengan pendekatan fault tree dan even tree model memberikan kombinasi outcome frequency dan consequence yang dari plot matriks risiko 6x6 menunjukan masih berada pada level 1 (unacceptable) pada skenario flash fire pada human dan delayed pool fire pada human dan asset. Analisis risiko detail kemudian dilakukan pada skenario risk level 1 dengan metode analisis Fault Tree, Event Tree, dan Consequence, dengan data dan asumsi berbasis review lebih lanjut dengan mengidentifikasi mitigasi yang bisa diterapkan melibatkan berbagai entitas yang terkait: Produksi Operasi, Marine, Drilling, dan Safety yang kemudian bisa menurunkan frekuensi atau likelihood of occurance sehingga semua skenario risiko masuk ke level 2 (tolerable/ALARP). Penerapan penilaian risiko dengan TRA berbasis skenario (Scenario based) pada kegiatan \ out without shutdown di PT X berhasil melakukan identifikasi seluruh risiko yang memiliki konsekuensi yang tinggi (catastrophic atau disastrous). Dengan melakukan pemetaan risiko dan perhitungan risiko maka PT X dapat menyiapkan langkah-langkah mitigasi untuk mencegah atau mengurangi kemungkinan terjadinya insiden (likelihood of occurance) sehingga seluruh scenario yang di identifikasi dalam TRA turun pada risk level 2 (kuning/diterima) dengan langkah-langkah pengurangan risiko (ALARP). Hasil dari TRA ini telah dipakai oleh Manajemen PT X untuk memutuskan melakukan kegiatan Rig Move out pada kegiatan pengeboran di lepas pantai Mahakam tanpa mematikan platform sama sekali (withouth shutdown) yang juga dilaksanakan pada kegiatan rig move out di tahun 2021.
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Luzon, Paul Kenneth, Kristina Montalbo, Jam Galang, Jasmine May Sabado, Carmille Marie Escape, Raquel Felix, and Alfredo Mahar Francisco Lagmay. "Hazard mapping related to structurally controlled landslides in Southern Leyte, Philippines." Natural Hazards and Earth System Sciences 16, no. 3 (April 1, 2016): 875–83. http://dx.doi.org/10.5194/nhess-16-875-2016.
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Abstract. The 2006 Guinsaugon landslide in Saint Bernard, Southern Leyte, is one of the largest known landslides in the Philippines in recent history. It consists of a 15–20 million m3 rockslide-debris avalanche from an approximately 675 m high mountain weakened by continuous movement of the Philippine Fault. The catastrophic Guinsaugon landslide killed 1221 people and displaced 19 000 residents over its 4.5 km path. To investigate the present-day morphology of the scar and potential failure that may occur, analysis of a 5 m resolution InSAR-derived digital elevation model was conducted using Coltop3D and Matterocking software, leading to the generation of a landslide hazard map for the province of Southern Leyte in central Philippines. The dip and dip direction of discontinuity sets that contribute to gravitational failure in mountainous areas of the province were identified and measured using a lower Schmidt–Lambert color scheme. After measurement of the morpho-structural orientations, potential sites of failure were analyzed. Conefall was then utilized to compute the extent of rock mass runout. Results of the analysis show instability in the scarp area of the 2006 Guinsaugon landslide and in adjacent slopes because of the presence of steep discontinuities that range from 45 to 60°. Apart from the 2006 Guinsaugon landslide site, runout models simulated farther rock mass extent in its adjacent slopes, revealing a high potential for fatal landslides to happen in the municipality of Saint Bernard. Concerned agencies may use maps produced in the same manner as this study to identify possible sites where structurally controlled landslides can occur. In a country like the Philippines, where fractures and faults are common, this type of simulated hazard maps would be useful for disaster prevention and facilitate disaster risk reduction efforts for landslide-susceptible areas.
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Luzon, P. K., K. P. Montalbo, J. A. M. Galang, J. M. Sabado, C. M. Escape, R. P. Felix, and A. M. F. Lagmay. "Structurally controlled hazard mapping of Southern Leyte, Philippines." Natural Hazards and Earth System Sciences Discussions 3, no. 10 (October 1, 2015): 5891–921. http://dx.doi.org/10.5194/nhessd-3-5891-2015.
Full textAbstract:
Abstract. The 2006 Guinsaugon landslide in St. Bernard, Southern Leyte is one of the largest known landslides in the Philippines in recent history. It consists of a 15–20 million m3 rockslide-debris avalanche from an approximately 675 m high mountain weakened by continuous movement of the Philippine fault. The catastrophic Guinsaugon landslide killed 1221 people and displaced 19 000 residents over its 4.5 km path. To investigate the present day morphology of the scar and potential failure that may occur, analysis of a 5 m resolution IfSAR-derived Digital Elevation Model was conducted using Coltop3D and Matterocking software, leading to the generation of a landslide hazard map for the province of Southern Leyte in Central Philippines. The dip and dip-direction of discontinuity sets that contribute to gravitational failure in mountainous areas of the province were identified and measured using a lower Schmidt-Lambert color scheme. After measurement of the morpho-structural orientations, potential sites of failure were analyzed. Conefall was then utilized to compute the extent of rock mass runout. Results of the analysis show instability in the scarp area of the 2006 Guinsaugon landslide and in adjacent slopes because of the presence of steep discontinuities that range from 45–60°. Apart from the 2006 Guinsaugon landslide site, runout models simulated farther rock mass extent in its adjacent slopes, revealing a high potential for fatal landslides to happen in the municipality of St. Bernard. Concerned agencies may use maps produced in the same manner as this study to identify possible sites where structurally-controlled landslides can occur. In a country like the Philippines, where fractures and faults are common, this type of simulated hazard maps would be useful for disaster prevention and facilitate disaster risk reduction efforts for landslide-susceptible areas.
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Solada, Katharine E., Brendan T. Reilly, Joseph S. Stoner, Shanaka L. de Silva, Adonara E. Mucek, Robert G. Hatfield, Indyo Pratomo, Rendi Jamil, and Baskoro Setianto. "Paleomagnetic observations from lake sediments on Samosir Island, Toba caldera, Indonesia, and its late Pleistocene resurgence." Quaternary Research 95 (April 7, 2020): 97–112. http://dx.doi.org/10.1017/qua.2020.13.
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AbstractApproximately 74 ka, Toba caldera in Sumatra, Indonesia, erupted in one of the most catastrophic supereruptions in Earth's history. Resurgent uplift of the caldera floor raised Samosir Island 700 m above Lake Toba, exposing valuable lake sediments. To constrain sediment chronology, we collected 173 discrete paleomagnetic 8 cm3 cubes and 15 radiocarbon samples from six sections across the island. Bulk organic 14C ages provide an initial chronostratigraphic framework ranging from ~12 to 46 ka. Natural and laboratory magnetizations were studied using alternating field demagnetization. A generally well-defined primary magnetization is isolated using principal component analysis. Comparison of inclination, and to a lesser degree declination, across independently dated sections suggests paleomagnetic secular variation (PSV) is recorded. Average inclination of −6° is more negative than a geocentric axial dipole would predict, but consistent with an eastward extension of the negative inclination anomaly observed in the western equatorial Pacific. The 14C- and PSV-derived age model constrains resurgent uplift, confirming faster uplift rates to the east and slower rates to the west, while suggesting that fault blocks moved differentially from each other within a generally trapdoor-type configuration.
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Christie, Matthew D., Shuaishuai Sun, Lei Deng, Donghong Ning, Haiping Du, Shiwu Zhang, and Weihua Li. "The variable resonance magnetorheological pendulum tuned mass damper: Mathematical modelling and seismic experimental studies." Journal of Intelligent Material Systems and Structures 31, no. 2 (November 28, 2019): 263–76. http://dx.doi.org/10.1177/1045389x19888799.
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Tuned mass damper technologies are progressively advancing through innovative application of smart materials, facilitating more versatile infrastructure protection. During seismic events, primarily encountered surrounding fault lines, high-rise buildings and other civil structures can suffer catastrophic failures if not adequately protected. Where traditional passive structural protection may mitigate such damage, adaptive systems which provide controllable vibration attenuation across a wide range of excitation frequencies have seen growth in use, overcoming the challenges resulting from unpredictable seismic spectrums. As a robust solution to this problem, this article presents and analyses a variable resonance magnetorheological-fluid-based pendulum tuned mass damper which employs a rotary magnetorheological damper in a controllable differential transmission to add stiffness to a swinging pendulum mass. The device is mathematically modelled based on magnetic field analysis, the Bingham plastic shear-stress model for magnetorheological fluids, and planetary gearbox kinematic and torque relationships, with the model then being validated against experimental data. The passive and semi-active-controlled performance of the device in seismic vibration suppression is then experimentally investigated using a scale five-storey building. In tests conducted with the 1985 Mexico City record, the semi-active device outperformed the (optimal) passive-on tuning, at best reducing peak displacement by 15.47% and acceleration by 28.28%, with similar improvement seen against the passive-off case for the 1940 El Centro record.
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Khoirunnisa, H., S. Karima, G. Gumbira, and R. A. Rachman. "A numerical study of submarine–landslide–generated tsunami and its propagation in Majene, West Sulawesi." IOP Conference Series: Earth and Environmental Science 925, no. 1 (November 1, 2021): 012035. http://dx.doi.org/10.1088/1755-1315/925/1/012035.
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Abstract On 14th January 2021, there was a devastating earthquake (Mw 6.2) hit Mamuju and Majene, West Sulawesi, Indonesia at 18.28 UTC. According to National Disaster Management Authority, this event causes 84 casualties and 279 houses were damaged. The Sulawesi Island is situated in a very complex tectonic region, there are several thrusts and faults along the area such as Majene Thrust, Palu-Karo Thrust, Matano Fault, and Tolo Thrust that can lead to tectonic activities. One of the largest earthquakes was a 7.9 Mw in 1997 generated from North Sulawesi Megathrust that caused a catastrophic tsunami. Moreover, there were 9 tsunami events in the Makassar Strait from the year 1800 to 1999. In this research, three different scenarios of the tsunami in Majene were applied to obtain the tsunami elevation. Makassar Strait could be potentially generated tsunami wave from submarine landslides due to its steep bathymetry that will impact the coastline at Sulawesi and Kalimantan, so it is necessary to model the tsunami propagation using submarine landslide as the tsunami generation. The volume of submarine landslide had been used in tsunami submarine landslide modelling as an input. Those are included the height, width and length of the submarine landslide volume. Furthermore, the domain bathymetry was obtained from National Bathymetry (BatNas) with spacing grid of 300 m × 300 m. The submarine landslide coordinate is also needed as a source of tsunami at 2.98°S and 118.94°E. The slide angle and slope angle are also inputted in this modelling with three experimental volumes, namely 1 km3, 0.8 km3, and 0.5 km3. This submarine landslide tsunami modelling used the Non-Hydrostatic WAVE Model (NHWAVE) method to obtain tsunami wave generation. The result from NHWAVE model will be used for initial elevation of tsunami wave propagation using the Fully Nonlinear Boussinesq wave model - Total Variation Diminishing (FUNWAVE - TVD) method. The highest initial tsunami elevation value at each observation point obtained from the NHWAVE model occurred at point 18 (the closest location to the earthquake source), which is around 0.4 –1.2 m. The FUNWAVE simulation result is the tsunami wave propagation for 180 minutes later. In the 180th minute, the tsunami wave was still propagating towards the north of Sulawesi Island to the east of Kalimantan Island.
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Ashraf, Suleman, Mohammad H. Shawon, Haris M. Khalid, and S. M. Muyeen. "Denial-of-Service Attack on IEC 61850-Based Substation Automation System: A Crucial Cyber Threat towards Smart Substation Pathways." Sensors 21, no. 19 (September 26, 2021): 6415. http://dx.doi.org/10.3390/s21196415.
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The generation of the mix-based expansion of modern power grids has urged the utilization of digital infrastructures. The introduction of Substation Automation Systems (SAS), advanced networks and communication technologies have drastically increased the complexity of the power system, which could prone the entire power network to hackers. The exploitation of the cyber security vulnerabilities by an attacker may result in devastating consequences and can leave millions of people in severe power outage. To resolve this issue, this paper presents a network model developed in OPNET that has been subjected to various Denial of Service (DoS) attacks to demonstrate cyber security aspect of an international electrotechnical commission (IEC) 61850 based digital substations. The attack scenarios have exhibited significant increases in the system delay and the prevention of messages, i.e., Generic Object-Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV), from being transmitted within an acceptable time frame. In addition to that, it may cause malfunction of the devices such as unresponsiveness of Intelligent Electronic Devices (IEDs), which could eventually lead to catastrophic scenarios, especially under different fault conditions. The simulation results of this work focus on the DoS attack made on SAS. A detailed set of rigorous case studies have been conducted to demonstrate the effects of these attacks.
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PRIOLO, ENRICO. "EARTHQUAKE GROUND MOTION SIMULATION THROUGH THE 2-D SPECTRAL ELEMENT METHOD." Journal of Computational Acoustics 09, no. 04 (December 2001): 1561–81. http://dx.doi.org/10.1142/s0218396x01001522.
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The application of the 2-D Chebyshev spectral element method (SPEM) to engineering seismology problems is reviewed in this paper. The SPEM is a high-order finite element technique which solves the variational formulation of the seismic wave propagation equations. The computational domain is discretised into an unstructured grid composed by irregular quadrilateral elements. This property makes the SPEM particularly suitable to compute numerically accurate solutions of the full wave equations in complex media. The earthquake is simulated following an approach that can be considered "global", that is all the factors influencing the wave propagation — source, crustal heterogeneity, fine details of the near-surface structure, and topography — are taken into account and solved simultaneously. The basic earthquake source is represented by a 2-D point double couple model. Ruptures propagating along fault segments placed on the model plane are simulated as a finite summation of elementary point sources. After a general introduction, the paper first gives an overview of the method; then it concentrates on some methodological topics of interest for practical applications, such as quadrangular mesh generation, source definition and scaling, numerical accuracy and computational efficiency. Limitations and advantages of using a 2-D approach, although sophisticated such as the SPEM, are addressed, as well. The effectiveness of the method is illustrated through two case histories, i.e. the ground shaking prediction in Catania (Sicily, Italy) for a catastrophic earthquake, and the analysis of the ground motion in the presence of a massive structure.
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Korma, V. D. "The Use of Catastrophe Theory in Forensics." Actual Problems of Russian Law 17, no. 12 (October 6, 2022): 204–12. http://dx.doi.org/10.17803/1994-1471.2022.145.12.204-212.
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The modern theory of catastrophes contributes to the understanding of dynamic situations that govern the evolutionary development of natural phenomena, society, and man, as well as the prediction of the instability of various dynamic systems that can have catastrophic consequences. The results of the development of this theory are widely used in various fields of knowledge: in physics, biology, economics, medicine, psychology, linguistics, ecology and other sciences. The paper attempts to use the theory of catastrophes in forensic science, in particular, in the investigation of criminally relevant incidents of a man-made nature related to professional activities, which are inherently a destructive product of the functioning of various dynamic systems. First, the theoretical aspects of the theory of catastrophes are briefly outlined, the main concepts and their classifications are considered (catastrophe, man-made disaster, man-made emergency, emergencies, man-made sources of increased danger, etc.), which are important for developing a methodology for investigating man-made crimes related to professional activities. In order to improve the efficiency of the investigator’s recognition activity in establishing the cause of the crime under investigation (man-made disaster), the author proposes to use schemes (probabilistic models) obtained using the techniques for constructing the so-called fault and event trees, addressed primarily to specialists associated with the operation of technical systems and supervision behind them. According to the author, this will contribute to the effectiveness of putting forward investigative leads in a criminal case and their verification.
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Zhu, Jianhua, Chaoan Lai, and Yanming Sun. "Fault Mechanism Analysis for Manufacturing System Based on Catastrophe Model." Mathematical Problems in Engineering 2019 (June 24, 2019): 1–11. http://dx.doi.org/10.1155/2019/2313581.
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Fault analysis is important in both research and industry. Current fault analysis tasks are mainly concerned with fault prediction and classification and do not focus enough on fault evolution mechanisms. In this paper, we propose a fault analysis method based on catastrophe theory for manufacturing system to improve the effectiveness and efficiency of real time monitoring of potential fault and causes analysis. The key advantages of our proposed method are (i) utilizing catastrophe theory and big data analysis to establish the fault cusp catastrophe model of manufacturing system and create the internal fault evolution mechanism of manufacturing system by the cusp catastrophe model and, (ii) with the established catastrophe model, fulfilling fault monitoring and accurate preventive control of the manufacturing system and ensuring the healthy operation of the manufacturing system.
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Chen, X. L., H. L. Ran, and W. T. Yang. "Evaluation of factors controlling large earthquake-induced landslides by the Wenchuan earthquake." Natural Hazards and Earth System Sciences 12, no. 12 (December 12, 2012): 3645–57. http://dx.doi.org/10.5194/nhess-12-3645-2012.
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Abstract. During the 12 May 2008, Wenchuan earthquake in China, more than 15 000 landslides were triggered by the earthquake. Among these landslides, there were 112 large landslides generated with a plane area greater than 50 000 m2. These large landslides were markedly distributed closely along the surface rupture zone in a narrow belt and were mainly located on the hanging wall side. More than 85% of the large landslides are presented within the range of 10 km from the rupture. Statistical analysis shows that more than 50% of large landslides occurred in the hard rock and second-hard rock, like migmatized metamorphic rock and carbonate rock, which crop out in the south part of the damaged area with higher elevation and steeper landform in comparison with the northeast part of the damaged area. All large landslides occurred in the region with seismic intensity ≥ X except a few of landslides in the Qingchuan region with seismic intensity IX. Spatially, the large landslides can be centred into four segments, namely the Yingxiu, the Gaochuan, the Beichuan and the Qingchuan segments, from southwest to northeast along the surface rupture. This is in good accordance with coseismic displacements. With the change of fault type from reverse-dominated slip to dextral slip from southwest to northeast, the largest distance between the triggered large landslides and the rupture decreases from 15 km to 5 km. The critical acceleration ac for four typical large landslides in these four different segments were estimated by the Newmark model in this paper. Our results demonstrate that, given the same strength values and slope angles, the characteristics of slope mass are important for slope stability and deeper landslides are less stable than shallower landslides. Comprehensive analysis reveals that the large catastrophic landslides could be specifically tied to a particular geological setting where fault type and geometry change abruptly. This feature may dominate the occurrence of large landslides. The results will be useful for improving reliable assessments of earthquake-induced landslide susceptibility, especially for large landslides which may result in serious damages.