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1
Wei, Lu, Zheng Qian, Yan Pei, and Jingyue Wang. "Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis." Applied Sciences 12, no. 1 (December 22, 2021): 69. http://dx.doi.org/10.3390/app12010069.
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Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.
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Kim, Kyusung, and Alexander G. Parlos. "Reducing the Impact of False Alarms in Induction Motor Fault Diagnosis." Journal of Dynamic Systems, Measurement, and Control 125, no. 1 (March 1, 2003): 80–95. http://dx.doi.org/10.1115/1.1543550.
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Early detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance, and improved operational efficiency of induction motors. At the same time, reducing the probability of false alarms increases the confidence of equipment owners in this new technology. In this paper, a model-based fault diagnosis system recently proposed by the authors for induction motors is experimentally compared for fault detection and false alarm performance with a more traditional signal-based motor fault estimator. In addition to the nameplate information required for the initial set-up, the proposed model-based fault diagnosis system uses measured motor terminal currents and voltages, and motor speed. The motor model embedded in the diagnosis system is empirically obtained using dynamic recurrent neural networks, and the resulting residuals are processed using wavelet packet decomposition. The effectiveness of the model-based diagnosis system in detecting the most widely encountered motor electrical and mechanical faults, while minimizing the impact of false alarms resulting from power supply and load variations, is demonstrated through extensive testing with staged motor faults. The model-based fault diagnosis system is scalable to motors of different power ratings and it has been successfully tested with fault data from 2.2kW,373kW, and 597kW induction motors.
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Ding, Wei, Qing Chen, Yuzhan Dong, and Ning Shao. "Fault Diagnosis Method of Intelligent Substation Protection System Based on Gradient Boosting Decision Tree." Applied Sciences 12, no. 18 (September 7, 2022): 8989. http://dx.doi.org/10.3390/app12188989.
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In order to improve the efficiency of the devices’ fault diagnosis of the protection systems of intelligent substation, a fault diagnosis method based on a gradient boosting decision tree (GBDT) was proposed. Using the integrated alarm information, the device self-checking information, the link information of generic object-oriented substation event (GOOSE) and sampled value (SV) and the sampling value information generated during the fault of the protection system, the fault feature information set is constructed. According to different fault characteristics, the protection system faults are classified into simple faults and complex faults to improve the diagnosis efficiency. Using GBDT training rules, a fault diagnosis model of protection system based on GBDT is established and fault diagnosis steps are given. This study takes a 110 kV intelligent substation in southern China as an example, to verify the effectiveness and accuracy of the proposed fault diagnosis method, and compared it with the existing methods in terms of the accuracy. The diagnostic accuracy in the case of false alarms and the case of multiple faults are verified. The results show that the method can meet the practical engineering application.
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Liu, Pan, Xing Ming Li, and Jian Wu. "A New Algorithm for the Fuzziness of Alarms in Network Faults Diagnosis." Applied Mechanics and Materials 198-199 (September 2012): 1539–44. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1539.
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The alarm correlation analysis based on fuzzy association rules mining is the popular and cutting-edge field of the network fault diagnosis research. In the application environment of alarms in communication networks, a new algorithm of the fuzziness of alarms which is called FKMA (Fuzzy K-Means of Alarms algorithm) is proposed .During the process of fuzziness, there are two methods of sorting the center. Simulations are carried out to the comparison of the two methods. The fuzziness of alarms is effectively realized. And fuzzy association rules mining are achieved. The advantages and efficiency of FKMA are demonstrated by experiments.
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Zhu, Zhi Jie, Jun Li, Jian Yong Liu, and Hong Cheng Jiang. "The Study of Intelligent Processing Frame to Alarms in Monitoring Center." Advanced Materials Research 614-615 (December 2012): 1008–12. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1008.
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With the build of Monitoring Center in our country, a great of signals are uploaded from many substations which are located on every corner. When an abnormal or a grid fault in Power System happens, lots of signals come out, then many operators on duty can’t often react quickly and judge the fault accurately.Recently，more and more scholars begin studying intelligent alarm processing system. In this paper, by analyzing signals characters and SCADA network of Monitoring Center, an intelligent processing frame to alarms in monitoring center is provided, the frame includes three layers named signals foundation treatment layer, signals connecting and sharing layer, signals intelligent diagnosis layer. Now the frame has been implemented successfully in Monitoring Center of our company. At the same time, based on an virtual signals software, lots of devices alarms and grid faults are simulated, this intelligent processing system, which are built on the frame, always show the alarm tip quickly. Facts prove it attributes to grid fault judge for operators of Monitoring Center.
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Deng, Lingzhi, Yuqiang Cheng, and Yehui Shi. "Fault Detection and Diagnosis for Liquid Rocket Engines Based on Long Short-Term Memory and Generative Adversarial Networks." Aerospace 9, no. 8 (July 26, 2022): 399. http://dx.doi.org/10.3390/aerospace9080399.
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The development of health monitoring technology for liquid rocket engines (LREs) can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. Therefore, we propose a fault detection and diagnosis (FDD) method for a large LOX/kerosene rocket engine based on long short-term memory (LSTM) and generative adversarial networks (GANs). Specifically, we first modeled a large LOX/kerosene rocket engine using MATLAB/Simulink and simulated the engine’s normal and fault operation states involving various startup and steady-state stages utilizing fault injection. Second, we created an LSTM-GAN model trained with normal operating data using LSTM as the generator and a multilayer perceptron (MLP) as the discriminator. Third, the test data were input into the discriminator to obtain the discrimination results and realize fault detection. Finally, the test data were input into the generator to obtain the predicted samples and calculate the absolute error between the predicted and the real value of each parameter. Then the fault diagnosis index, standardized absolute error (SAE), was constructed. SAE was analyzed to realize fault diagnosis. The simulated results highlight that the proposed method effectively detects faults in the startup and steady-state processes, and diagnoses the faults in the steady-state process without missing an alarm or being affected by false alarms. Compared with the conventional redline cut-off system (RCS), adaptive threshold algorithm (ATA), and support vector machine (SVM), the fault detection process of LSTM-GAN is more concise and more timely.
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Zdiri, Mohamed Ali, Badii Bouzidi, and Hsan Hadj Abdallah. "Performance investigation of an advanced diagnostic method for SSTPI-fed IM drives under single and multiple open IGBT faults." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 2 (March 4, 2019): 616–41. http://dx.doi.org/10.1108/compel-04-2018-0181.
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Purpose This paper aims to analyze and investigate the performance of an improved fault detection and identification (FDI) method based on multiple criteria, applied to six-switch three-phase inverter (SSTPI)-fed induction motor (IM) drives under both single and multiple open insulated-gate bipolar transistors(IGBT) faults. Design/methodology/approach This paper proposes an advanced diagnostic method for both single and multiple open IGBT faults dedicated to SSTPI-fed IM drives considering five distinct faulty operating conditions as follows: a single IGBT open-circuit fault, a single-phase open-circuit fault, a non-crossed double fault in two different legs, a crossed double fault in two different legs and a three-IGBT open-circuit fault. This is achieved because of the introduction of a new diagnosis variable provided using the information of the slope of the current vector in (α-β) frame. The proposed FDI method is based on the synthesis and the analysis, under both healthy and faulty operations, of the behaviors of the introduced diagnosis variable, the three motor phase currents and their normalized average values. Doing so, the developed FDI method allows a best compromise of fast detection and precision localization of IGBT open-circuit fault of the inverter. Findings Simulation works, carried out considering the implementation of the direct rotor flux oriented control in an IM fed by the conventional SSTPI, have proved the high performance of the advanced FDI method in terms of fast fault detection associated with a high robustness against false alarms, against speed and load torque fast variations and against the oscillations of the DC-bus voltage in the case of both healthy and faulty operations. Research limitations/implications This work should be extended considering the validation of the obtained simulation results through experiments. Originality/value Different from other FDI methods, which suffer from a low diagnostic effectiveness for low load levels and false alarms during transient operation, this method offers the potentialities to overcome these drawbacks because of the introduction of the new diagnosis variable. This latter, combined with the information provided from the three motor phase currents and their normalized average values allow a more efficient detection and identification of IGBT open-circuit fault.
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You, Zhuan. "Fault Alarms and Power Performance in Hybrid Electric Vehicles Based on Hydraulic Technology." World Electric Vehicle Journal 14, no. 1 (January 10, 2023): 20. http://dx.doi.org/10.3390/wevj14010020.
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In order to improve the fault alarm effect on the power performance of hydraulic hybrid electric vehicles (HEV), this paper proposes a fault alarm method for hybrid electric vehicle power performance based on hydraulic technology, builds a hybrid electric vehicle power system model, uses hydraulic technology to extract the characteristic signals of key components, uses support vector mechanisms to build a hybrid electric vehicle classifier, and obtains the fault alarm results for dynamic performance based on hydraulic technology. The results show that the proposed method can improve real-time diagnosis and alarm for engine faults in HEV, and the fault can be diagnosed after 5 s of injection, thus ensuring the dynamic stability of HEV.
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Chin, Hsinyung, and Kourosh Danai. "A Method of Fault Signature Extraction for Improved Diagnosis." Journal of Dynamic Systems, Measurement, and Control 113, no. 4 (December 1, 1991): 634–38. http://dx.doi.org/10.1115/1.2896468.
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Efficient extraction of fault signatures from sensory data is a major concern in fault diagnosis. This paper introduces a self-tuning method of fault signature extraction that enhances fault detection, minimizes false alarms, improves diagnosability, and reduces fault signature variability. The proposed method uses a Flagging Unit to convert the processed measurements to binary vectors, and utilizes nonparametric pattern classification techniques to estimate the fault signatures. The performance of the Flagging Unit, which relies on its adaptation algorithms to optimize its performance based upon a sample batch of measurement-fault vectors, is demonstrated in simulation.
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Tian, Ying, Qiang Zou, and Jin Han. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification." Energies 14, no. 7 (March 30, 2021): 1918. http://dx.doi.org/10.3390/en14071918.
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Data-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose faults through the state variable data collected during the operation of the PEMFC system. However, the state variable data collected from the PEMFC system during the stack switching between different operating points can easily cause false alarms, such that the practical value of the diagnosis system is reduced. To overcome this problem, a fault diagnosis method for PEMFC systems based on steady-state identification is proposed in this paper. The support vector data description (SVDD) and relevance vector machine (RVM) optimized by the artificial bee colony (ABC) are used for the steady-state identification and fault diagnosis. The density-based spatial clustering of applications with noise (DBSCAN) and linear least squares fitting (LLSF) are used to identify the abnormal data in datasets and estimate change rates of the system state variables respectively. The proposed method can automatically identify the state variable data collected from the PEMFC system during the stack switching between different operating points, so that the diagnosis accuracy can be improved and false alarms can be reduced. The proposed method has a certain practical value and can provide a reference for further study.
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Shen, Guang, Yong Zhang, Haifeng Qiu, Chongyu Wang, Fushuan Wen, Md Salam, Liguo Weng, Bin Yu, and Jie Chen. "Fault Diagnosis with False and/or Missing Alarms in Distribution Systems with Distributed Generators." Energies 11, no. 10 (September 27, 2018): 2579. http://dx.doi.org/10.3390/en11102579.
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A comprehensive method is presented in this work to locate faults in distribution systems with distributed generators (DGs). A two-level model is developed for this purpose with both telecommunication and telemetering data employed, so as to make good use of fused information for attaining a more credible optimization solution under scenarios with alarm distortions of feeder terminal units (FTUs) or loss during communication. First, at the upper level, an analytic model is developed to search all potential faulted sections/candidates based on the telecommunication data. Then, on the lower level, a model is presented using the telemetering data to identify the most likely fault location from the candidates provided by the upper model. The essential features of the two-level diagnosis model are demonstrated through a number of case studies. Simulation results have shown that the proposed approach is capable of not only locating the faulted section(s) in a distribution system with DGs but also identifying false and/or missing alarms.
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Liu, Edwina, and Du Zhang. "Diagnosis of Component Failures in the Space Shuttlemain Engines Using Bayesian Belief Network: A Feasibility Study." International Journal on Artificial Intelligence Tools 12, no. 03 (September 2003): 355–74. http://dx.doi.org/10.1142/s0218213003001277.
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Although the Space Shuttle is a high reliability system, the health of the Space Shuttle must be accurately diagnosed in real-time. Two problems current plague the system, false alarms that may be costly, and missed alarms which may be not only expensive, but also dangerous to the crew. This paper describes the results of a feasibility study where a multivariate state estimation technique is coupled with a Bayesian Belief Network to provide both fault detection and fault diagnostic capabilities for the Space Shuttle Main Engines (SSME). Five component failure modes and several single sensor failures are simulated in our study and correctly diagnosed. The results indicate that this is a feasible fault detection and diagnosis technique and fault detection and diagnosis can be made earlier than standard redline methods allow.
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Song, Huizhong, Ming Dong, Rongjie Han, Fushuan Wen, Md Salam, Xiaogang Chen, Hua Fan, and Jian Ye. "Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information." Energies 11, no. 10 (September 26, 2018): 2565. http://dx.doi.org/10.3390/en11102565.
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When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necessary to develop a methodologically appropriate framework for this application. As a branch of stochastic programming, the well-developed chance-constrained programming approach provides an efficient way to solve programming problems fraught with uncertainties. In this work, a novel fault diagnosis analytic model is developed with the ability of accommodating the malfunctioning of PRs and CBs, as well as the false and/or missing alarms. The genetic algorithm combined with Monte Carlo simulations are then employed to solve the optimization model. The feasibility and efficiency of the developed model and method are verified by a real fault scenario in an actual power system. In addition, it is demonstrated by simulation results that the computation speed of the developed method meets the requirements for the on-line fault diagnosis of actual power systems.
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LANG, HAOXIANG, and CLARENCE W. DE SILVA. "FAULT DIAGNOSIS OF AN INDUSTRIAL MACHINE THROUGH SENSOR FUSION." International Journal of Information Acquisition 05, no. 02 (June 2008): 93–110. http://dx.doi.org/10.1142/s0219878908001521.
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In this paper, a four layer neuro-fuzzy architecture of multi-sensor fusion is developed for a fault diagnosis system which is applied to an industrial fish cutting machine. An important characteristic of the fault diagnosis approach developed in this paper is to make an accurate decision of the machine condition by fusing information acquired from three types of sensors: Accelerometer, microphone and charge-coupled device (CCD) camera. Feature vectors for vibration and sound signals from their fast Fourier transform (FFT) frequency spectra are defined and extracted from the acquired information. A feature-based vision method is applied for object tracking in the machine, to detect and track the fish moving on the conveyor. A four-layer neural network including a fuzzy hidden layer is developed in the paper to analyze and diagnose existing faults. Feature vectors of vibration, sound and vision are provided as inputs to the neuro-fuzzy network for fault detection and diagnosis. By proper training of the neural network using data samples for typical faults, six crucial faults in the fish cutting machine are detected with high reliability and robustness. On this basis, not only the condition of the machine can be determined for possible retuning and maintenance, but also alarms to warn about impending faults may be generated during the machine operation.
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Quatrini, Elena, Francesco Costantino, Xiaochuan Li, and David Mba. "Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions." Applied Sciences 12, no. 9 (May 8, 2022): 4737. http://dx.doi.org/10.3390/app12094737.
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In the industrial panorama, many processes operate under time-varying conditions. Adapting high-performance diagnostic techniques under these relatively more complex situations is urgently needed to mitigate the risk of false alarms. Attention is being paid to fault anticipation, requiring an in-depth study of prediction techniques. Predicting remaining life before the occurrence of faults allows for a comprehensive maintenance management protocol and facilitates the wear management of the machine, avoiding faults that could permanently compromise the integrity of such machinery. This study focuses on canonical variate analysis for fault detection in processes operating under time-varying conditions and on its contribution to the diagnostic and prognostic analysis, the latter of which was performed with machine learning techniques. The approach was validated on actual datasets from a granulator operating in the pharmaceutical sector.
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Cartocci, Nicholas, Marcello R. Napolitano, Francesco Crocetti, Gabriele Costante, Paolo Valigi, and Mario L. Fravolini. "Data-Driven Fault Diagnosis Techniques: Non-Linear Directional Residual vs. Machine-Learning-Based Methods." Sensors 22, no. 7 (March 29, 2022): 2635. http://dx.doi.org/10.3390/s22072635.
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Linear dependence of variables is a commonly used assumption in most diagnostic systems for which many robust methodologies have been developed over the years. In case the system nonlinearities are relevant, fault diagnosis methods, relying on the assumption of linearity, might potentially provide unsatisfactory results in terms of false alarms and missed detections. In recent years, many authors have proposed machine learning (ML) techniques to improve fault diagnosis performance to mitigate this problem. Although very powerful, these techniques require faulty data samples that are representative of any fault scenario. Additionally, ML techniques suffer from issues related to overfitting and unpredictable performance in regions which are not fully explored in the training phase. This paper proposes a non-linear additive model to characterize the non-linear redundancy relationships among the system signals. Using the multivariate adaptive regression splines (MARS) algorithm, these relationships are identified directly from the data. Next, the non-linear redundancy relationships are linearized to derive a local time-dependent fault signature matrix. The faulty sensor can then be isolated by measuring the angular distance between the column vectors of the fault signature matrix and the primary residual vector. A quantitative analysis of fault isolation and fault estimation performance is performed by exploiting real data from multiple flights of a semi-autonomous aircraft, thus allowing a detailed quantitative comparison with state-of-the-art machine-learning-based fault diagnosis algorithms.
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Zhou, Zhuoran, Zhanguo Ma, Yingying Jiang, and Minjun Peng. "Fault Diagnosis Using Bond Graphs in an Expert System." Energies 15, no. 15 (August 5, 2022): 5703. http://dx.doi.org/10.3390/en15155703.
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A fault diagnosis method using bond graphs in an expert system is proposed for a reactor coolant system. Firstly, the time causality graph and the variable relationship graph are derived from the bond graph. Secondly, the fault signature matrix is obtained by combining the change relationship of fault parameters. Finally, the fault signature matrix is used as the rule of the inference engine design in the expert system for fault diagnosis. In this paper, the key equipment of the reactor coolant system is used to verify the fault diagnosis method of the bond graph expert system, and the path reasoning relationship between alarms is obtained, which can accurately obtain the deep knowledge required by the operators. A new idea for fault diagnosis in a nuclear power plant’s expert system is provided by this method.
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Abboudi, Ayman, and Fouad Belmajdoub. "Dynamic Thresholds for a Reliable Diagnosis of Switched Systems." Journal Européen des Systèmes Automatisés 54, no. 6 (December 29, 2021): 827–33. http://dx.doi.org/10.18280/jesa.540604.
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Safety, availability and reliability are the main concern of many industries. Thus, fault detection and isolation of industrial machines, which are in most cases switched systems, is a primary task in many companies. The presented paper proposes a new diagnostic approach for switched systems using two powerful tools: bond graph and observer. A diagnostic layer detects model errors using bond graph, and a smart algorithm identifies and locates faults using observer. Although observers serve as fault detectors, they also have their own errors caused by convergence delay of calculations; even in the case of no sensor defect, the residue does not converge to zero. In this paper, we propose a new method to solve this problem by integrating dynamic thresholds in the detection procedure, which helped to avoid false alarms and ensure a highly reliable diagnosis.
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Xiangyang, Li, and Chen Wanqiang. "Rolling Bearing Fault Diagnosis Based on Physical Model and One-Class Support Vector Machine." ISRN Mechanical Engineering 2014 (April 14, 2014): 1–4. http://dx.doi.org/10.1155/2014/160281.
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This paper aims at diagnosing the fault of rolling bearings and establishes the system of dynamics model with the consideration of rolling bearing with nonlinear bearing force, the radial clearance, and other nonlinear factors, using Runge-Kutla such as Hertzian elastic contactforce and internal radial clearance, which are solved by the Runge-Kutta method. Using simulated data of the normal state, a self-adaptive alarm method for bearing condition based on one-class support vector machine is proposed. Test samples were diagnosed with a recognition accuracy over 90%. The present method is further applied to the vibration monitoring of rolling bearings. The alarms under the actual abnormal condition meet the demand of bearings monitoring.
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Segovia Ramirez, Isaac, Behnam Mohammadi-Ivatloo, and Fausto Pedro García Márquez. "Alarms management by supervisory control and data acquisition system for wind turbines." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 1 (January 2, 2021): 110–16. http://dx.doi.org/10.17531/ein.2021.1.12.
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Wind energy is one of the most relevant renewable energy. A proper wind turbine maintenance management is required to ensure continuous operation and optimized maintenance costs. Larger wind turbines are being installed and they require new monitoring systems to ensure optimization, reliability and availability. Advanced analytics are employed to analyze the data and reduce false alarms, avoiding unplanned downtimes and increasing costs. Supervisory control and data acquisition system determines the condition of the wind turbine providing large dataset with different signals and alarms. This paper presents a new approach combining statistical analysis and advanced algorithm for signal processing, fault detection and diagnosis. Principal component analysis and artificial neural networks are employed to evaluate the signals and detect the alarm activation pattern. The dataset has been reduced by 93% and the performance of the neural network is incremented by 1000% in comparison with the performance of original dataset without filtering process.
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Ragsdale, Austin, Roger Lew, Brian P. Dyre, and Ronald L. Boring. "Fault Diagnosis with Multi-State Alarms in a Nuclear Power Control Simulator." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 56, no. 1 (September 2012): 2167–71. http://dx.doi.org/10.1177/1071181312561458.
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Becker, Vincent, Thilo Schwamm, Sven Urschel, and Jose Alfonso Antonino-Daviu. "Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps." Energies 14, no. 15 (July 26, 2021): 4514. http://dx.doi.org/10.3390/en14154514.
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The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation.
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Wu, Lifeng, Beibei Yao, Zhen Peng, and Yong Guan. "An adaptive threshold algorithm for sensor fault based on the grey theory." Advances in Mechanical Engineering 9, no. 2 (February 2017): 168781401769319. http://dx.doi.org/10.1177/1687814017693193.
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An appropriate threshold is the key factor in a diagnosis of fault. However, the traditional method of setting a fixed threshold does not take into consideration the influence of system status and noise interference, and it often leads to false alarms and missed detections of system fault. To improve the accuracy of fault diagnosis, we first obtained the residual signal based on the strong tracking filter method – cubature Kalman filtering. We then proposed an adaptive dynamic threshold adjustment algorithm based on the grey theory. In this method, the threshold value can be dynamically adjusted according to the real-time mean and variance of the residual. Finally, we performed a sensor fault experiment involving three sensors in different locations of a robot. The results demonstrate the feasibility of our proposed method.
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Qiu, Yingning, Yanhui Feng, and David Infield. "Fault diagnosis of wind turbine with SCADA alarms based multidimensional information processing method." Renewable Energy 145 (January 2020): 1923–31. http://dx.doi.org/10.1016/j.renene.2019.07.110.
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Cheng, Yu Jie, Chen Lu, Li Mei Wang, and Hong Mei Liu. "Fault Detection and Isolation for Hydraulic Servo System Based on Adaptive Threshold and SOM Neural Network." Applied Mechanics and Materials 764-765 (May 2015): 691–97. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.691.
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A fault detection and diagnosis method for the hydraulic servo system based on adaptive threshold and self-organizing map (SOM) neural network is proposed in this study. The nonlinear, time-varying, fluid-solid coupling properties of the hydraulic servo system are considered. Fault detection is realized based on a two-stage radial basis function (RBF) neural network model. The first-stage RBF neural network is adopted as a fault observer for the hydraulic servo system; the residual error signal is generated by comparing the estimated observer output with the actual measurements. To overcome the drawback of false alarms when the traditional fixed fault threshold is used, an adaptive threshold producer is established by the second-stage RBF neural network. Fault occurrence is detected by comparing the residual error signal with the adaptive threshold. When a system fault is detected, the SOM neural network is employed to implement fault classification and isolation by analyzing the features of the residual error signal. Three types of common faults are simulated to verify the performance and effectiveness of the proposed method. Experimental results demonstrate that the proposed method based on adaptive threshold and SOM neural network is effective in detecting and isolating the failure of the hydraulic servo system.
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Orlowska-Kowalska, T., and P. Sobanski. "Simple diagnostic technique of a single IGBT open-circuit faults for a SVM-VSI vector controlled induction motor drive." Bulletin of the Polish Academy of Sciences Technical Sciences 63, no. 1 (March 1, 2015): 281–88. http://dx.doi.org/10.1515/bpasts-2015-0032.
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Abstract In this paper a simple diagnostic system for a single IGBT open-circuit faults for a two level voltage inverter-fed field oriented controlled induction motor drive was presented. A fault diagnostic procedure is carried out by utilizing an analysis of a stator current vector trajectory in α-β coordinates. An extraction of the failure information is based on monitoring of an angle between the stator current space vector and the axis. Thanks to a diagnostic signal normalization, high robustness to false diagnosis alarms is guaranteed. To confirm the proposed method, simulation results under a wide range working condition of the induction motor drive were presented.
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Gu, Yong Bin, Zhi Nong Jiang, and Qi Luo. "The Development of Automatic Tracking Filter for Data Acquisition in Fault Diagnosis System Based on FPGA." Applied Mechanics and Materials 614 (September 2014): 335–38. http://dx.doi.org/10.4028/www.scientific.net/amm.614.335.
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The running speed of rotating machinery will have a negative influence on the quality of acquired data used in fault diagnosis. Poor-quality signal may cause misinterpretation of monitoring system, and even lead to the false alarms or failure of detection. To improve the quality of the signal and enhance the accuracy of the fault monitoring system, a novel automatic tracking filter for data acquisition based on FPGA was developed. This newly developed filter can adjust to its real-time cut-off frequency relying on the detected rotational speed. Moreover, the introduction of the Ping-Pong operation realized the non-disturbance shifting of output data. The results obtained from the simulated and pragmatic experiments revealed that this filter could achieve automatic tracking for rotational speed and ameliorate the quality of sampling signal utilized in fault diagnosis.
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Kim, K. "Fault diagnosis and prognosis for fuel supply systems in gas turbine engines." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 223, no. 3 (December 1, 2008): 757–68. http://dx.doi.org/10.1243/09544062jmes1064.
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This paper introduces a feature-extraction method to characterize gas turbine engine dynamics. The extracted features are used to develop a fault diagnosis and prognosis method for the fuel supply system in gas turbine engines. The engine start-up profiles of the core speed (N2) and the exhaust gas temperature collected with high-speed sampling rate are obtained and processed into a more compact data set by identifying critical-to-characterization instances. The fuzzy-clustering method is applied to the smaller number of parameters, and the fault is detected by differentiating the clusters matching the failures. In this work, the actual flight data collected in the field was used to develop and validate the system, and the results are shown for the test on nine engines that experienced fuel supply system failure. The developed fault diagnosis system detected the failure successfully in all nine cases. For the earliest detection cases, the alarms start to trigger 26 days before the system completely fails and 7 days in advance for the last detection.
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Hafaifa, Ahmed, Ferhat Laaouad, and Kouider Laroussi. "Fuzzy modeling and control for detection and isolation of surge in industrial centrifugal compressors." Journal of Automatic Control 19, no. 1 (2009): 19–26. http://dx.doi.org/10.2298/jac0901019h.
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This paper provides the possible application of the fuzzy approaches in fault detection and isolation area for a class of complex industrial processes with uncertain interval parameters. The main idea of fuzzy fault detection and isolation is to build a model of a diagnosis procedures, using rules-based Fuzzy Expert System, capable to minimize false alarms enhance detectability and isolability and minimize detection time by hardware implementation to improve reliability, safety and global efficiency. This paper illustrates an alternative implementation to the compression systems supervision task using the basic principles of model-based fault detection and isolation associated with the self-tuning of surge measurements with subsequent appropriate corrective actions. Using a combination of fuzzy modeling approach makes it possible to devise a fault-isolation scheme based on the given incidence matrix. Simulation results of a fault detection and isolation for a compression system are provided, which illustrate the relevance of the proposed FDI method.
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Calabrese, Francesca, Alberto Regattieri, Marco Bortolini, Francesco Gabriele Galizia, and Lorenzo Visentini. "Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery." Applied Sciences 11, no. 20 (October 14, 2021): 9580. http://dx.doi.org/10.3390/app11209580.
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Given the strategic role that maintenance assumes in achieving profitability and competitiveness, many industries are dedicating many efforts and resources to improve their maintenance approaches. The concept of the Smart Factory and the possibility of highly connected plants enable the collection of massive data that allow equipment to be monitored continuously and real-time feedback on their health status. The main issue met by industries is the lack of data corresponding to faulty conditions, due to environmental and safety issues that failed machinery might cause, besides the production loss and product quality issues. In this paper, a complete and easy-to-implement procedure for streaming fault diagnosis and novelty detection, using different Machine Learning techniques, is applied to an industrial machinery sub-system. The paper aims to offer useful guidelines to practitioners to choose the best solution for their systems, including a model hyperparameter optimization technique that supports the choice of the best model. Results indicate that the methodology is easy, fast, and accurate. Few training data guarantee a high accuracy and a high generalization ability of the classification models, while the integration of a classifier and an anomaly detector reduces the number of false alarms and the computational time.
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Fentaye, Amare D., and Konstantinos G. Kyprianidis. "An intelligent data filtering and fault detection method for gas turbine engines." MATEC Web of Conferences 314 (2020): 02007. http://dx.doi.org/10.1051/matecconf/202031402007.
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In a gas turbine fault diagnostics, the removal of measurement noise and data outliers prior to the fault analysis is very essential. The conventional filtering methods, particularly the linear ones, are not sufficiently accurate, which might possibly lead to the loss of critically important features in the fault analysis process. Conversely, the recorded accuracies obtained from the non-linear filters are promising. Recently, the focus has been shifted to the artificial neural network (ANN) based nonlinear filters due to their capability of providing a robust identity map between the input and output data, which can be efficiently exploited in the process of fault diagnosis. This paper aims to present combined auto-associative neural network (AANN) and K-nearest neighbor (KNN) based noise reduction and fault detection method for a gas turbine engine application. The performance of the developed method has been evaluated using data obtained from a model simulation. The test results revealed that the developed hybrid method is more effective and reliable than the conventional methods for the fault detection of the gas turbine engine with negligible false alarms and missed detections.
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Yoon, Joung Taek, Byeng D. Youn, Minji Yoo, Yunhan Kim, and Sooho Kim. "Life-cycle maintenance cost analysis framework considering time-dependent false and missed alarms for fault diagnosis." Reliability Engineering & System Safety 184 (April 2019): 181–92. http://dx.doi.org/10.1016/j.ress.2018.06.006.
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Jung, Sang Jin, Tanvir Alam Shifat, and Jang-Wook Hur. "A Hybrid Fault Diagnosis Approach Using FEM Optimized Sensor Positioning and Machine Learning." Processes 10, no. 10 (September 22, 2022): 1919. http://dx.doi.org/10.3390/pr10101919.
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Sensor acquired signal has been a fundamental measure in rotary machinery condition monitoring (CM) to enhance system reliability and stability. Inappropriate sensor mounting can lead to loss of fault-related information and generate false alarms in industrial systems. To ensure reliable system operation, in this paper we investigate a system’s multiple degrees-of-freedom (DOF) using the finite element method (FEM) to find the optimum sensor mounting position. An appropriate sensor position is obtained by the highest degree of deformation in FEM modal analysis. The effectiveness of the proper sensor mounting position was compared with two other sensor mounting points, which were selected arbitrarily. To validate the effectiveness of this method we considered a gear-actuator test bench, where the sensors were mounted in the same place as the FEM simulation. Vibration data were acquired through these sensors for different health states of the system and failure patterns were recognized using an artificial neural network (ANN) model. An ANN model shows that the optimum sensor mounting point found in FEM has the highest accuracy, compared to other mounting points. A hybrid CM framework, combining the physics-based and data-driven approaches, provides robust fault detection and identification analysis of the gear-actuator system.
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Shi, Wen Li, and Xue Min Zi. "Diagnosing Problems of Distribution-Free Multivariate Control Chart." Advanced Materials Research 971-973 (June 2014): 1602–6. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1602.
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In order to solve the problem of only have a few historical data that can be used in multivariate process monitoring, a new distribution-free multivariate control chart has been proposed. And in the control chart structure the control limits are determined on-line with the future observations and the historical data. Therefore, the proposed control chart has very important application in practice. However, the research doesn’t study the problem of the fault diagnosis after the control chart alarms. So we use LASSO-based diagnostic framework to identify when a detected shift has occurred and to isolate the shifted components.
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Ren, Xiaogeng, Chunwang Li, Xiaojun Ma, Fuxiang Chen, Haoyu Wang, Ashutosh Sharma, Gurjot Singh Gaba, and Mehedi Masud. "Design of Multi-Information Fusion Based Intelligent Electrical Fire Detection System for Green Buildings." Sustainability 13, no. 6 (March 19, 2021): 3405. http://dx.doi.org/10.3390/su13063405.
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Building management systems are costly for small- to medium-sized buildings. A massive volume of data is collected on different building contexts by the Internet of Things (IoT), which is then further monitored. This intelligence is integrated into building management systems (BMSs) for energy consumption management in a cost-effective manner. Electric fire safety is paramount in buildings, especially in hospitals. Facility managers focus on fire protection strategies and identify where system upgrades are needed to maintain existing technologies. Furthermore, BMSs in hospitals should minimize patient disruption and be immune to nuisance alarms. This paper proposes an intelligent detection technology for electric fires based on multi-information fusion for green buildings. The system model was established by using fuzzy logic reasoning. The extracted multi-information fusion was used to detect the arc fault, which often causes electrical fires in the low-voltage distribution system of green buildings. The reliability of the established multi-information fusion model was verified by simulation. Using fuzzy logic reasoning and the membership function in fuzzy set theory to solve the uncertain relationship between faults and symptoms is a widely applied method. In order to realize the early prediction and precise diagnosis of faults, a fuzzy reasoning system was applied to analyze the arcs causing electrical fires in the lines. In order to accurately identify the fault arcs that easily cause electrical fires in low-voltage distribution systems for building management, this paper introduces in detail a fault identification method based on multi-information fusion, which can consolidate the complementary advantages of different types of judgment. The results demonstrate that the multi-information fusion method reduces the deficiency of a single criterion in fault arc detection and prevents electrical fires in green buildings more comprehensively and accurately. For the real-time dataset, the data results are presented, showing disagreements among the testing methods.
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Gültekin, Özgür, Eyup Cinar, Kemal Özkan, and Ahmet Yazıcı. "Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence." Sensors 22, no. 9 (April 22, 2022): 3208. http://dx.doi.org/10.3390/s22093208.
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Early fault detection and real-time condition monitoring systems have become quite significant for today’s modern industrial systems. In a high volume of manufacturing facilities, fleets of equipment are expected to operate uninterrupted for days or weeks. Any unplanned interruptions to equipment uptime could jeopardize manufacturers’ cycle time, capacity, and, most significantly, credibility for their customers. With the help of smart manufacturing technologies, companies have started to develop and integrate fault detection and classification systems where end-to-end constant monitoring of equipment is facilitated, and smart algorithms are adapted for the early generation of fault alarms and classification. This paper proposes a generic real-time fault diagnosis and condition monitoring system utilizing edge artificial intelligence (edge AI) and a data distributor open source middleware platform called FIWARE. The implemented system architecture is flexible and includes interfaces that can be easily expanded for various devices. This work demonstrates it for condition monitoring of autonomous transfer vehicle (ATV) equipment targeting a smart factory use case. The system is verified in a designated industrial model environment in a lab with a single ATV operation. The anomaly conditions of the ATV are diagnosed by a deep learning-based fault diagnosis method performed in the Edge AI unit, and the results are transferred to the data storage via a data pipeline setup. The proposed system’s Edge AI solution for the ATV use case provides significant real-time performance. The network bandwidth requirement and total elapsed data transfer time have been reduced by 43 and 37 times, respectively. The proposed system successfully enables real-time monitoring of ATV fault conditions and expands to a fleet of equipment in a real manufacturing facility.
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Ho, Siu Ki, Harish Chandra Nedunuri, Wamadeva Balachandran, Jamil Kanfoud, and Tat-Hean Gan. "Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach." Applied Sciences 11, no. 13 (June 22, 2021): 5792. http://dx.doi.org/10.3390/app11135792.
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Machinery with several rotating and stationary components tends to produce non-stationary and random vibration signatures due to the fluctuations in the input loads and process defects due to long hours of operation. Traditional heuristics methods are suitable for the detection of fault signatures, however, they become more complicated when the level of uncertainty or randomness exceeds beyond control. A novel methodology to identify these fault signatures using optimal filtering of vibration data is proposed to eliminate any false alarms and is expected to provide a higher probability of correct diagnosis. In this paper, a detailed pipeline of the algorithms are presented along with the results of the investigation that was carried out. These investigations are performed using open-source vibration data published by the NASA prognostics centre. The performance of these algorithms are evaluated based on the ground truth results published by NASA researchers. Based on the performance of these algorithms several parameters are fine-tuned to ensure generalisation and reliable performance.
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Asokan, A., and D. Sivakumar. "Model based fault detection and diagnosis using structured residual approach in a multi-input multi-output system." Serbian Journal of Electrical Engineering 4, no. 2 (2007): 133–45. http://dx.doi.org/10.2298/sjee0702133a.
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Fault detection and isolation (FDI) is a task to deduce from observed variable of the system if any component is faulty, to locate the faulty components and also to estimate the fault magnitude present in the system. This paper provides a systematic method of fault diagnosis to detect leak in the three-tank process. The proposed scheme makes use of structured residual approach for detection, isolation and estimation of faults acting on the process [1]. This technique includes residual generation and residual evaluation. A literature review showed that the conventional fault diagnosis methods like the ordinary Chisquare (?2) test method, generalized likelihood ratio test have limitations such as the "false alarm" problem. From the results it is inferred that the proposed FDI scheme diagnoses better when compared to other conventional methods.
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Makkawi, Khoder, Nourdine Ait-Tmazirte, Maan El Badaoui El Najjar, and Nazih Moubayed. "Adaptive Diagnosis for Fault Tolerant Data Fusion Based on α-Rényi Divergence Strategy for Vehicle Localization." Entropy 23, no. 4 (April 14, 2021): 463. http://dx.doi.org/10.3390/e23040463.
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When applying a diagnostic technique to complex systems, whose dynamics, constraints, and environment evolve over time, being able to re-evaluate the residuals that are capable of detecting defaults and proposing the most appropriate ones can quickly prove to make sense. For this purpose, the concept of adaptive diagnosis is introduced. In this work, the contributions of information theory are investigated in order to propose a Fault-Tolerant multi-sensor data fusion framework. This work is part of studies proposing an architecture combining a stochastic filter for state estimation with a diagnostic layer with the aim of proposing a safe and accurate state estimation from potentially inconsistent or erroneous sensors measurements. From the design of the residuals, using α-Rényi Divergence (α-RD), to the optimization of the decision threshold, through the establishment of a function that is dedicated to the choice of α at each moment, we detail each step of the proposed automated decision-support framework. We also dwell on: (1) the consequences of the degree of freedom provided by this α parameter and on (2) the application-dictated policy to design the α tuning function playing on the overall performance of the system (detection rate, false alarms, and missed detection rates). Finally, we present a real application case on which this framework has been tested. The problem of multi-sensor localization, integrating sensors whose operating range is variable according to the environment crossed, is a case study to illustrate the contributions of such an approach and show the performance.
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Brito, Lucas Costa, Gian Antonio Susto, Jorge Nei Brito, and Marcus Antonio Viana Duarte. "Fault Detection of Bearing: An Unsupervised Machine Learning Approach Exploiting Feature Extraction and Dimensionality Reduction." Informatics 8, no. 4 (November 25, 2021): 85. http://dx.doi.org/10.3390/informatics8040085.
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The monitoring of rotating machinery is an essential activity for asset management today. Due to the large amount of monitored equipment, analyzing all the collected signals/features becomes an arduous task, leading the specialist to rely often on general alarms, which in turn can compromise the accuracy of the diagnosis. In order to make monitoring more intelligent, several machine learning techniques have been proposed to reduce the dimension of the input data and also to analyze it. This paper, therefore, aims to compare the use of vibration features extracted based on machine learning models, expert domain, and other signal processing approaches for identifying bearing faults (anomalies) using machine learning (ML)—in addition to verifying the possibility of reducing the number of monitored features, and consequently the behavior of the model when working with reduced dimensionality of the input data. As vibration analysis is one of the predictive techniques that present better results in the monitoring of rotating machinery, vibration signals from an experimental bearing dataset were used. The proposed features were used as input to an unsupervised anomaly detection model (Isolation Forest) to identify bearing fault. Through the study, it is possible to verify how the ML model behaves in view of the different possibilities of input features used, and their influences on the final result in addition to the possibility of reducing the number of features that are usually monitored by reducing the dimension. In addition to increasing the accuracy of the model when extracting correct features for the application under study, the reduction in dimensionality allows the specialist to monitor in a compact way the various features collected on the equipment.
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Sim, Hoi Yin, Rahizar Ramli, and Ahmad Saifizul. "Assessment of characteristics of acoustic emission parameters for valve damage detection under varying compressor speeds." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 234, no. 17 (April 14, 2020): 3521–40. http://dx.doi.org/10.1177/0954406220915232.
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Acoustic emission technique is often employed to detect valve abnormalities. With the development of technology, machine learning-based fault diagnosis methods are prevalent in the nondestructive testing industry as they can automatically detect valve problems without any human intervention. Nevertheless, feeding in all possible input parameters into the learning algorithm without any prior assessment may result in high computational cost and time, while adding to the risk of having false alarms. This study intended to obtain characteristics of acoustic emission signal for various valve conditions and compressor speeds by examining the four most commonly used parameters, namely the acoustic emission root mean square, acoustic emission crest factor, acoustic emission variance, and acoustic emission kurtosis. The study begins with time–frequency analysis of one revolution acoustic emission signal acquired from a faulty suction valve through discrete wavelet transform to obtain the signal characteristics of valve events. To associate signals with valve movements, the reconstructed discrete wavelet transform signals are further segregated into six time segments, and the four acoustic emission parameters are computed from each of the time segments. These parameters are analyzed through statistical analysis namely the two-way analysis of variance, followed by the Tukey test to obtain the best parameter which can differentiate each valve condition clearly at all speeds. The results revealed that acoustic emission root mean square is the best parameter especially in identification of heavy grease valve condition during suction valve opening event while acoustic emission crest factor is capable to detect leaky valve during the suction valve closing event at all speeds. It is believed that effective valve diagnosis strategy can be delivered by referring to the features of parameters and the characteristic valve event timing corresponding to each valve condition and speed.
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Miao, Di. "Design of power network fault diagnosis based on time series matching." Thermal Science 23, no. 5 Part A (2019): 2595–604. http://dx.doi.org/10.2298/tsci181126148m.
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Common grid fault diagnosis does not fully utilize the alarm timing information generated by the fault. To solve this problem, this paper proposes a fault diagnosis method based on time series. The method analyzes the alarm hypothesis sequence generated by the grid fault and the time sequence actually received by the dispatch center, and utilizes the discrete characteristics of the edit distance and reflects the event discreteness and time continuity of the alarm information by adding the time distance. The calculated data of the similarity between the two sequences and the confidence of the alarm hypothesis sequence determine the faulty component.
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Li, Fang, Yongjun Min, and Ying Zhang. "A Novel Method for Lithium-Ion Battery Fault Diagnosis of Electric Vehicle Based on Real-Time Voltage." Wireless Communications and Mobile Computing 2022 (May 25, 2022): 1–17. http://dx.doi.org/10.1155/2022/7277446.
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The cell faults of lithium-ion batteries will lead to the atypical deterioration of battery performance and even thermal runaway. In this paper, a novel fault diagnosis method for lithium-ion batteries of electric vehicles based on real-time voltage is proposed. Firstly, the voltage distribution of battery cells is confirmed in electric vehicles, and the reasons are analyzed. Furthermore, kurtosis is utilized to discover cell faults for the first time. After the kurtosis-based strategy alarm, the faulty cells in the battery pack are identified through multidimensional scaling and density-based spatial clustering of applications with noise. This method reduces the computational load of the data platform due to the characteristics of the sequential structure. Finally, the strategies to quantify the level of faulty cells and evaluate the safety of electric vehicles are presented. Through the real-time data collected by electric vehicles, it has been proven that this method can warn and locate faulty cells earlier than the original system method and has better robustness than other unsupervised fault diagnosis methods.
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Poddar, Surojit, and Naresh Tandon. "Classification and detection of cavitation, particle contamination and oil starvation in journal bearing through machine learning approach using acoustic emission signals." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 235, no. 10 (January 25, 2021): 2137–43. http://dx.doi.org/10.1177/1350650121991316.
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The ability to classify condition-monitoring data and make a decision can be imparted to a computer through the machine learning approach. In this article, the acoustic emission signals emerging from journal bearings under normal operating conditions and faulty states, namely cavitation, particle contamination and oil starvation, have been classified to develop fault-prediction model using the machine learning approach. Furthermore, an application has been developed that takes acoustic emission data as input and diagnoses the category of faults besides triggering an alarm under faulty states.
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Chang, Huang, and Chung. "Real-Time Evaluation of the Mechanical Performance and Residual Life of a Notching Mold using Embedded PVDF Sensors and SVM Criteria." Sensors 19, no. 23 (November 22, 2019): 5123. http://dx.doi.org/10.3390/s19235123.
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The geometric tolerance of notching machines used in the fabrication of components for induction motor stators and rotators is less than 50 µm. The blunt edges of worn molds can cause the edge of the sheet metal to form a burr, which can seriously impede assembly and reduce the efficiency of the resulting motor. The overuse of molds without sufficient maintenance leads to wasted sheet material, whereas excessive maintenance shortens the life of the punch/die plate. Diagnosing the mechanical performance of die molds requires extensive experience and fine-grained sensor data. In this study, we embedded polyvinylidene fluoride (PVDF) films within the mechanical mold of a notching machine to obtain direct measurements of the reaction forces imposed by the punch. We also developed an automated diagnosis program based on a support vector machine (SVM) to characterize the performance of the mechanical mold. The proposed cyber-physical system (CPS) facilitated the real-time monitoring of machinery for preventative maintenance as well as the implementation of early warning alarms. The cloud server used to gather mold-related data also generated data logs for managers. The hyperplane of the CPS-PVDF was calibrated using a variety of parameters pertaining to the edge characteristics of punches. Stereo-microscopy analysis of the punched workpiece verified that the accuracy of the fault classification was 97.6%.
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Yin, Hong, Shuqiang Yang, Xiaoqian Zhu, Songchang Jin, and Xiang Wang. "Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/582042.
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The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.
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Rato, Tiago J., Pedro Delgado, Cristina Martins, and Marco S. Reis. "First Principles Statistical Process Monitoring of High-Dimensional Industrial Microelectronics Assembly Processes." Processes 8, no. 11 (November 23, 2020): 1520. http://dx.doi.org/10.3390/pr8111520.
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Modern industrial units collect large amounts of process data based on which advanced process monitoring algorithms continuously assess the status of operations. As an integral part of the development of such algorithms, a reference dataset representative of normal operating conditions is required to evaluate the stability of the process and, after confirming that it is stable, to calibrate a monitoring procedure, i.e., estimate the reference model and set the control limits for the monitoring statistics. The basic assumption is that all relevant “common causes” of variation appear well represented in this reference dataset (using the terminology adopted by the founding father of process monitoring, Walter A. Shewhart). Otherwise, false alarms will inevitably occur during the implementation of the monitoring scheme. However, we argue and demonstrate in this article, that this assumption is often not met in modern industrial systems. Therefore, we introduce a new approach based on the rigorous mechanistic modeling of the dominant modes of common cause variation and the use of stochastic computational simulations to enrich the historical dataset with augmented data representing a comprehensive coverage of the actual operational space. We show how to compute the monitoring statistics and set their control limits, as well as to conduct fault diagnosis when an abnormal event is declared. The proposed method, called AGV (Artificial Generation of common cause Variability) is applied to a Surface Mount Technology (SMT) production line of Bosch Car Multimedia, where more than 17 thousand product variables are simultaneously monitored.
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Zang, Chongquan, Xinhua Wei, Lin Li, Cong Hu, and Hao Tong. "Research on Remote Fault Diagnosis System of Harvester." Journal of Physics: Conference Series 2417, no. 1 (December 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2417/1/012023.
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The structure of combined harvester is complex, and its operation process includes many processes. There is material transportation between each process, so blocking faults often occur. The blockage fault of combined harvester will seriously affect the efficiency of working and harvest quality, so this paper designs a remote diagnosis system of blockage fault of combined harvester. The system can carry out remote monitoring, fault diagnosis and fault alarm for the operation status of the combine, and also provide information management and other functions, which can effectively carry out remote maintenance services. This paper presents an IPSO-BP fault diagnosis model, which is tested by simulation test. The results show that the accuracy of fault prediction by this method is 97.78%. Compared with BP neural network model and PSO-BP model, the accuracy of fault prediction is improved by 5.28% and 13.45%, meeting the fault diagnosis requirements of combined harvester.
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Al-Shatri, Ali H., Ahmad Arshad, Oladokun Olagoke, and Bemgba B. Nyakuma. "Unknown input observer design for fault detection and diagnosis in a continuous stirred-tank reactor." E3S Web of Conferences 90 (2019): 02004. http://dx.doi.org/10.1051/e3sconf/20199002004.
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Early and accurate fault detection and diagnosis (FDD) minimises downtime, increases the safety and reliability of plant operation, and reduces manufacturing costs. This paper presents a robust FDD strategy for a nonlinear system using a bank of unknown input observers (UIO). The approach is based on structure residual generation that provides not only decoupling of faults from model uncertainties and unknown input disturbance but also decoupling the effect of a fault from the effects of other faults. The generated residual was evaluated through the statistical threshold to avoid fault missing or false alarm. The performance of the robust FDD scheme was assessed by some sensor fault scenarios created in a continuous stirred-tank reactor (CSTR). The simulation result showed the effectiveness of the proposed approach.
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Li, Xiang, Xin Min, Zeng Yang, Hongyu He, and Jinfeng Li. "Knowledge representation and intelligent fault diagnosis technology for power grid faults." E3S Web of Conferences 182 (2020): 02006. http://dx.doi.org/10.1051/e3sconf/202018202006.
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Aiming at that the automatic fault diagnosis method is difficult to locate the fault causes under uncertain circumstances which include malfunctions of the equipment and wrong alarm messages, a knowledge model was proposed to describe the relationship, status and operation of the equipment. And based on the model, the action logic between equipment after accidents is expressed in the form of rules combined with predicate logic. The corresponding interpretation and checking results of the relative alarm messages are given by reasoning the accident chain under different fault hypothesis. And the optimal judgment result is obtained through the calculation of prior probability. The validity of the method is verified by a practical fault case.