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Статті в журналах з теми "Catastrophic Fault Model"
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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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Частини книг з теми "Catastrophic Fault Model"
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De Florio, Vincenzo. "Fault-Tolerant Software." In Application-Layer Fault-Tolerance Protocols, 21–52. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-182-7.ch002.
Повний текст джерелаАнотація:
After having described the main characteristics of dependability and fault-tolerance, it is analyzed here in more detail what it means that a program is fault-tolerant and what are the properties expected from a fault-tolerant program. The main objective of this chapter is introducing two sets of design assumptions that shape the way our fault-tolerant software is structured—the system and the fault models. Often misunderstood or underestimated, those models describe • what is expected from the execution environment in order to let our software system function correctly, and • what are the faults that our system is going to consider. Note that a fault-tolerant program shall (try to) tolerate only those faults stated in the fault model, and will be as defenseless against all other faults as any non fault-tolerant program. Together with the system specification, the fault and system models represent the foundation on top of which our computer services are built. It is not surprising that weak foundations often result in failing constructions. What is really surprising is that in so many cases, little or no attention had been given to those important factors in fault-tolerant software engineering. To give an idea of this, three wellknown accidents are described—the Ariane 5 flight 501, Mariner-1 disasters, and the Therac-25 accidents. In each case it is stressed what went wrong, what were the biggest mistakes, and how a careful understanding of fault models and system models would have helped highlighting the path to avoid catastrophic failures that cost considerable amounts of money and even the lives of innocent people. The other important objective of this chapter is introducing the core subject of this book: Software fault-tolerance situated at the level of the application layer. First of all, it is explained why targeting (also) the application layer is not an open option but a mandatory design choice for effective fault-tolerant software engineering. Secondly, given the peculiarities of the application layer, three properties to measure the quality of the methods to achieve fault-tolerant application software are introduced: 1. Separation of design concerns, that is, how good the method is in keeping the functional aspects and the fault-tolerance aspects separated from each other. 2. Syntactical adequacy, namely how versatile the employed method is in including the wider spectrum of fault-tolerance strategies. 3. Adaptability: How good the employed fault-tolerance method is in dealing with the inevitable changes characterizing the system and its run-time environment, including the dynamics of faults that manifest themselves at service time. Finally, this chapter also defines a few fundamental fault-tolerance services, namely watchdog timers, exception handling, transactions, and checkpointingand- rollback.
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Mohan Khilar, Pabitra. "Genetic Algorithms." In Advances in Secure Computing, Internet Services, and Applications, 239–55. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4940-8.ch012.
Повний текст джерелаАнотація:
Genetic Algorithms are important techniques to solve many NP-Complete problems related to distributed computing and its application domains. Genetic algorithm-based fault diagnoses in distributed computing systems have been a feasible methodology to solve diagnosis problems recently. Distributed embedded systems consisting of sensors, actuators, processors/microcontrollers, and interconnection networks are one class of distributed computing systems that have long been used, staring from small-scale home appliances to large-scale satellite systems. Some of their applications are in safety-critical systems where occurrence of faults can result in catastrophic situations for which fault diagnosis in such systems are very important. In this chapter, different types of faults, which are likely to occur in distributed embedded systems and a GA-based methodology to solve these problems along with the performance analysis of fault diagnosis algorithm have been presented. Nevertheless, the diagnosis algorithm presented here is well suitable for general purpose distributed computing systems with appropriate modification over system and fault model. In fact, this book chapter will enable the reader not only to study various aspects of fault diagnosis techniques but will also provide insight to build robust systems to allow for continued normal service despite the occurrence of failures.
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Sheikh, Muhammad Aman, Nordin B. Saad, Nursyarizal Mohd Nor, Sheikh Tahir Bakhsh, and Muhammad Irfan. "Invasive Methods to Diagnose Stator Winding and Bearing Defects of an Induction Motors." In Advances in Computer and Electrical Engineering, 122–30. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6989-3.ch006.
Повний текст джерелаАнотація:
Condition monitoring in an induction motor must concentrate on the root causes of the failure modes that exhibit a slow failure sequence. According to published surveys, two-fifths of the faults are due to bearing failures. Inter-turn short circuit faults in stator windings are approximately responsible for one-third of the motor faults. In the last few decades, various methods and alternative techniques have been proposed and implemented to diagnose induction motor faults. In an induction motor, stator winding and bearing faults account the largest percentage of motor failure. Due to the fact these faults can lead the motor to catastrophic failure that are expensive in term of maintenance cost, wastage raw material, and unplanned shutdown. Thus, to diagnose the state of motor and overcome existing problem, the chapter provides detailed invasive methods which are proposed and are currently in practice. Moreover, the chapter also highlights the limitation, scope, and the challenges of existing invasive condition monitoring techniques.
Тези доповідей конференцій з теми "Catastrophic Fault Model"
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Alozie, Ogechukwu, Yi-Guang Li, Pericles Pilidis, Yang Liu, Xin Wu, Xingchao Shong, Wencheng Ren, and Theodosios Korakianitis. "An Integrated Principal Component Analysis, Artificial Neural Network and Gas Path Analysis Approach for Multi-Component Fault Diagnostics of Gas Turbine Engines." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15740.
Повний текст джерелаАнотація:
Abstract Gas path diagnostics is a key aspect of the engine health monitoring (EHM) process that aims to detect, identify and predict engine component faults, using information from installed sensors, in order to guide maintenance action, maintain engine efficiency and prevent catastrophic failures. To achieve high prediction accuracies, current data-derived diagnostic models tend to be engine specific while the model-based methods are known to be time-consuming, especially for complex engine configurations. This paper proposes an integrated approach for accurate and accelerated isolation and prediction of multiple-degraded gas turbine component faults that comprises 3 steps — feature extraction using the Principal Component Analysis (PCA), machine learning classification with a multi-layer perceptron, artificial neural network (MLP-ANN) and model-based fault prediction via the non-linear Gas Path Analysis (GPA) technique. In this hybrid approach, the PCA first transforms the measurement fault signature into a fault-feature domain, which becomes an input to the multi-label ANN classifier used to isolate the potential faulty components. The non-linear GPA finally quantifies the magnitude of degradation that produced the recorded fault signature. Once trained and validated, the PCA-ANN model is deployed as part of the data processing mechanism prior to the actual GPA calculation. This method was assessed and validated using the thermodynamic performance model of a 2-shaft, high-bypass ratio, turbofan engine. For training and testing the PCA-ANN classifier, a total of 28,000 final samples for 14 measurement parameters, each averaged from 10 data points with Gaussian noise of zero mean and unit standard deviation, and implanted with single-, double- and triple-component fault cases of various magnitude, were generated by steady-state performance simulation of the engine model at its reference operating condition. Correlation analysis of this data set revealed the optimum sensor subset to be used for multi-component diagnostics. A quantitative analysis of the PCA-ANN fault isolation on the test set produced a classification accuracy of 96.6% and performed better on all metrics, compared to other multi-label classification algorithms. Finally, the proposed integrated approach achieved an average of 94.35% reduction in processing time, when compared to the conventional non-linear GPA by component-fault-cases (CFCs), while predicting implanted faults to the same accuracy.
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Yin, Hao, He Xu, Yuhan Zhao, and Feng Sun. "Fault Diagnosis of Control Valve Based on Fusion of Deep Learning and Elastic Weight Consolidation." In BATH/ASME 2022 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/fpmc2022-89359.
Повний текст джерелаАнотація:
Abstract Deep neural network learning is a commonly used method for fault diagnosis of the control valve. However, the catastrophic forgetting problem of deep learning in multi-task affects the fault diagnosis accuracy. Moreover, the traditional training model can be improved by using parameter constraint control or adding a few parameters, but it has many limitations. Therefore, this paper proposed a fusion of elastic weight consolidation algorithm and residual shrinkage network method, sharing common feature layers. According to the weight of the same or similar parameters of the previous task, the correct solution of the current task could be obtained, and the forgetting degree of the previous task could be reduced. It improved the generalization ability of the training model. The control valve data were collected and compared with the stochastic gradient descent algorithm in different valve openings. The results indicate that this method has a high accuracy for the condition identification of the control valve. This method can effectively alleviate the problem of the catastrophic forgetting of deep learning in multi-task identification of control valve.
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Dey, Satadru, and Beshah Ayalew. "A Diagnostic Scheme for Detection, Isolation and Estimation of Electrochemical Faults in Lithium-Ion Cells." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9699.
Повний текст джерелаАнотація:
Improvement of the safety and reliability of the Lithium-ion (Li-ion) battery operation is one of the key tasks for advanced Battery Management Systems (BMSs). It is critical for BMSs to be able to diagnose battery electrochemical faults that can potentially lead to catastrophic failures. In this paper, an observer-based fault diagnosis scheme is presented that can detect, isolate and estimate some internal electrochemical faults. The scheme uses a reduced-order electrochemical-thermal model for a Li-ion battery cell. The paper first presents a modeling framework where the electrochemical faults are modeled as parametric faults. Then, multiple sliding mode observers are incorporated in the diagnostic scheme. The design and selection of the observer gains as well as the convergence of the observers are verified theoretically via Lyapunov’s direct method. Finally, the performance of the observer-based diagnostic scheme is illustrated via simulation studies.
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Zhao, Hao, Weifei Hu, Zhenyu Liu, and Jianrong Tan. "A CapsNet-Based Fault Diagnosis Method for a Digital Twin of a Wind Turbine Gearbox." In ASME 2021 Power Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/power2021-66029.
Повний текст джерелаАнотація:
Abstract Accurate fault diagnosis of complex energy systems, such as wind turbines, is essential to avoid catastrophic accidents and ensure a stable power source. However, accurate fault diagnoses under dynamic operating conditions and various failure mechanisms are major challenges for wind turbines nowadays. Here we present a CapsNet-based deep learning scheme for data-driven fault diagnosis used in a digital twin of a wind turbine gearbox. The CapsNet model can extract the multi-dimensional features and rich spatial information from the gearbox monitoring data by an artificial neural network named the CapsNet. Through the dynamic routing algorithm between capsules, the network structure and parameters of the CapsNet model can be adjusted effectively to realize an accurate and robust classification of the operational conditions of a wind turbine gearbox, including front box stuck (single fault) and high-speed shaft bearing damage & planetary gear damage (coupling faults). Two gearbox datasets are used to verify the performance of the CapsNet model. The experimental results show that the accuracy of this proposed method is up to 98%, which proves the accuracy of CapsNet model in the case study when this model performed three-state classification (health, stuck, and coupled damage). Compared with state-of-the-art fault diagnosis methods reported in the literature, the CapsNet model has a competitive advantage, especially in the ability to diagnose coupling faults, high-speed shaft bearing damage & planetary gear damage in our case study. CapsNet has at least 2.4 percentage points higher than any other measure in our experiment. In addition, the proposed method can automatically extract features from the original monitoring data, and do not rely on expert experience or signal processing related knowledge, which provides a new avenue for constructing an accurate and efficient digital twin of wind turbine gearboxes.
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Mjit, Mustapha, Pierre-Philippe J. Beaujean, and David J. Vendittis. "Fault Detection and Diagnostics in an Ocean Turbine Using Vibration Analysis." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-40963.
Повний текст джерелаАнотація:
This paper describes the approach, procedure and techniques developed to evaluate the health of ocean turbines, based on vibration measurements and analyses. A LabVIEW model for on-line vibration condition monitoring, implemented with advanced diagnostic techniques features, was developed. In order to distinguish between a vibration amplitude change due to a developing fault and that due to a change in operating condition, this program includes the use of an ordering technique in the frequency domain, which relates the vibration to the machine speed. Some experiments were first performed on a commercial fan to illustrate and demonstrate the fault detection capability of the monitoring and diagnostics system. To increase the reliability of the monitoring system, and to demonstrate that it can be used for monitoring a wide range of machines, a second series of vibration data collection and monitoring events was performed on a small boat with different combination (on/off status) of the engine, hydraulic pump, generator and air conditioning. This allowed for the detection of the frequency components associated with each subsystem, alone and together, and enabled the detection of mechanical faults, such as imbalance and misalignment, if they existed. For long term monitoring, the model allow for the automatic storing of raw data either periodically and/or after any deviations from normal conditions, i.e., when alerts are on. This makes it possible to follow the progress (towards an alarm condition) of any faults without saving data continuously. In this way, measurements of unexpected events may be made without the vibration engineer’s physical presence, hopefully, early fault detection and diagnosis will avoid catastrophic failure from occurring. This enables the economic and efficient health monitoring of ocean turbines as they become operational.
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Leão, Leandro de S., Aldemir Ap Cavalini, Tobias S. Morais, Gilberto Pechoto de Melo, and Valder Steffen. "Fault Detection in Rotating Machinery by Using the Modal State Observer Approach." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67044.
Повний текст джерелаАнотація:
The presence of transversal cracks in rotating shafts can lead to catastrophic failures, which imply economic losses and security issues. In the last years, an important attention has been devoted to this subject, leading to the development of several vibration-based structural health monitoring approaches. In this paper, a fault detection technique based on the so-called modal state observer is applied to detect transversal cracks in rotating machines. The Luenberger state observer is described in modal domain to determine the most affected vibration modes due to the crack presence. A cracked rotor composed by a flexible horizontal shaft, two rigid discs, and two self-aligning ball bearings is used for illustration purposes. The additional flexibility introduced by the crack is calculated by using the linear fracture mechanics theory. The breathing behavior of the crack is simulated according to the Mayes’ model, in which the crack transition from fully open to fully closed is described by a cosine function. The obtained results lead to the conclusion that the modal state observer is a potential technique to detect cracks in rotating machines.
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Karpat, Fatih, Ahmet Emir Dirik, Onur Can Kalay, Oğuz Doğan, and Burak Korcuklu. "Vibration-Based Early Crack Diagnosis With Machine Learning for Spur Gears." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24006.
Повний текст джерелаАнотація:
Abstract Gear mechanisms are one of the most significant components of the power transmission systems. Due to increasing emphasis on the high-speed, longer working life, high torques, etc. cracks may be observed on the gear surface. Recently, Machine Learning (ML) algorithms have started to be used frequently in fault diagnosis with developing technology. The aim of this study is to determine the gear root crack and its degree with vibration-based diagnostics approach using ML algorithms. To perform early crack detection, the single tooth stiffness and the mesh stiffness calculated via ANSYS for both healthy and faulty (25-50-75-100%) teeth. The calculated data transferred to the 6-DOF dynamic model of a one-stage gearbox, and vibration responses was collected. The data gathered for healthy and faulty cases were evaluated for the feature extraction with five statistical indicators. Besides, white Gaussian noise was added to the data obtained from the 6-DOF model, and it was aimed at early fault diagnosis and condition monitoring with ML algorithms. In this study, the gear root crack and its degree analyzed for both healthy and four different crack sizes (25%-50%-75%-100%) for the gear crack detection. Thereby, a method was presented for early fault diagnosis without the need for a big experimental dataset. The proposed vibration-based approach can eliminate the high test rig construction costs and can potentially be used for the evaluation of different working conditions and gear design parameters. Therefore, catastrophic failures can be prevented, and maintenance costs can be optimized by early crack detection.
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Xue, X., and J. Tang. "Condition Monitoring of PEM Fuel Cell Using Hotelling T2 Control Limit." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81160.
Повний текст джерелаАнотація:
Although a variety of design and control strategies have been proposed to improve the performance of polymer electrolyte membrane (PEM) fuel cell systems, temporary faults in such systems still might occur under practical operating conditions due to the complexity of the physical process and the functional limitations of some components. If these faults cannot be detected in a timely manner, long-time malfunction of fuel cell components may lead to catastrophic failures. Clearly, it is necessary to study the appropriate state condition monitoring scheme for fuel cell systems. In this research, we first develop a fuel cell stack model which can simulate the complicated transient behavior and dynamic interactions of the temperature, gas flow, phase change in the anode and cathode channels, and membrane humidification under operating conditions. Using this model as basis, we then employ the Hotelling T2 control limit approach to monitor stack conditions by using real-time measurements of fuel cell state variables such as output voltage. An important feature of the Hotelling method, a multivariate statistical analysis approach, is that one may decide fault occurrence under measurement noise. Simulation indicates that the new method has very high detection sensitivity and can detect the fault conditions at the early stage. This proposed monitoring strategy could provide valuable information for low-level real time control as well as high-level decision making.
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Harihara, Parasuram P., and Alexander G. Parlos. "Sensorless Detection of Impeller Cracks in Motor Driven Centrifugal Pumps." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66273.
Повний текст джерелаАнотація:
Electrical signal analysis has been in use for quite some time to detect and diagnose induction motor faults. In most industrial applications, induction motors are used to drive dynamic loads such as pumps, compressors, fans, etc. Failure of either the motors or the driven loads results in an unscheduled downtime which in turn leads to loss of production. These operational disruptions could be avoided if the equipment degradation is detected in its early stages prior to reaching catastrophic failure conditions. Hence the need arises for cost-effective detection schemes not only for assessing the condition of electric motors but also the driven loads. This paper presents an experimentally demonstrated sensor-less approach to detect impeller cracks in centrifugal pumps. The proposed method is sensorless in the sense that it does not use any mechanical and/or process pump sensors to detect impeller faults. Rather motor electrical measurements are used for the intended purpose. Mechanical sensors have high costs and low reliability, and frequently fail more often than the system being monitored. Hence add-on mechanical sensors reduce the overall system reliability. In this study, fault detection is accomplished using only the line voltages and phase currents of the electric motor driving the pump. The developed detection algorithm is insensitive to electrical power supply and load variations. Furthermore, it does not require prior knowledge of either a motor or the pump model or design parameters and hence the detection algorithm can be easily ported to motor-pump systems of varying manufacturers and sizes. The proposed fault detection scheme has been tested on data collected from a centrifugal pump driven by a 3-φ, 3 hp induction motor. Several cracks on the pump impeller are staged to validate the detection effectiveness of the proposed scheme and compare its effectiveness with respect to continuous vibration monitoring. In addition to these staged faults, experiments are also conducted to demonstrate the prevention of false alarms by the algorithm. Results from all of these experiments are presented to substantiate the performance of the sensorless pump fault detection scheme.
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Bercha, Frank G. "Arctic and Northern Offshore Oil Spill Probabilities." In SNAME 9th International Conference and Exhibition on Performance of Ships and Structures in Ice. SNAME, 2010. http://dx.doi.org/10.5957/icetech-2010-187.
Повний текст джерелаАнотація:
Current catastrophic consequences of the Gulf of Mexico blowout have refocused interest on the probabilities of such events in both temperate and northern regions. This paper reviews some of the early studies on oil spill probabilities with emphasis on oil blowouts, and details more recent studies carried out specifically for the Alaskan OCS. Due to the embryonic state of offshore oil development in arctic regions, which has been the case since 1976 to the present, it is not possible to base oil spill probability estimates on empirical data. The early studies relied on a detailed fault tree analysis dealing with the operations as systems without history. More recent studies in northern but not arctic operations use world wide data as a starting point. In the recent and current Alaskan OCS studies, statistically significant non-Arctic empirical data from the US Gulf of Mexico and world-wide sources, together with their variance, were used as a starting point. Next, both the historical non-Arctic frequency distributions and spill causal distributions were modified to reflect specific effects of the Arctic setting, and the resultant fault tree model was evaluated using Monte Carlo simulation to adequately characterize uncertainties treated as probability distribution inputs to the fault tree.