Q residual non-parametric Distribution on Fault Detection Approach Using Unsupervised LSTM-KDE
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Published
Aug 19, 2024
Nur Maisarah Mohd Sobran
Zool Hilmi Ismail
Abstract
It is well known among practitioner, majority collected data from industrial process plant are unlabeled. The collected historical data if utilize, able to provide vital information of process plant condition. Learning from unlabeled dataset, this study proposed Unsupervised LSTM-KDE approach as a measure to predict fault in industrial process plant. The residual based fault detection approach framework is utilized with long short-term memory (LSTM) as the main pattern learner for nonlinear and multimode condition that usually appear in process plant. Furthermore, kernel density approach (KDE) is used to determine the threshold value in non-parametric condition of unlabeled data. The LSTM-KDE approach later is evaluated with numerical data as well as Tennessee Eastman process plant dataset. The performance also was compared to Principal Component Analysis (PCA), Local outlier factor (LOF) and Auto-associative Kernel Regression (AAKR) to further examine the LSTM-KDE performance. The experimental results indicate that the LSTM-KDE fault detection approach has better learning performance and accuracy compared to other approaches
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Keywords
Fault detection, Non-parametric, Long short term memory (LSTM), Time series, Kernel density estimation(KDE)
References
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Ammann, Oliver, Gabriel Michau, and Olga Fink. 2020. “Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks.”
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Belagoune, Soufiane, Noureddine Bali, Azzeddine Bakdi, Bousaadia Baadji, and Karim Atif. 2021. “Deep Learning through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems.” Measurement 177:109330. doi: 10.1016/j.measurement.2021.109330.
Benkő, Zsigmond, Tamás Bábel, and Zoltán Somogyvári. 2022. “Model-Free Detection of Unique Events in Time Series.” Scientific Reports 12(1):227. doi: 10.1038/s41598-021-03526-y.
Cacciarelli, Davide, and Murat Kulahci. 2022. “A Novel Fault Detection and Diagnosis Approach Based on Orthogonal Autoencoders.” Computers & Chemical Engineering 163:107853. doi: 10.1016/j.compchemeng.2022.107853.
Calabrese, Francesca, Alberto Regattieri, Lucia Botti, Cristina Mora, and Francesco Gabriele Galizia. 2020. “Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems.” Applied Sciences 10(12):4120. doi: 10.3390/app10124120.
Chen, Tingting, Xueping Liu, Bizhong Xia, Wei Wang, and Yongzhi Lai. 2020. “Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder.” IEEE Access 8:47072–81. doi: 10.1109/ACCESS.2020.2977892.
Dong, Jingyi, Yuntong Ma, and Datong Liu. 2019. “Deep Learning Based Multiple Sensors Monitoring and Abnormal Discovery for Satellite Power System.” Pp. 638–43 in 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC).
Downs, J. J., and E. F. Vogel. 1993. “A Plant-Wide Industrial Process Control Problem.” Computers & Chemical Engineering 17(3):245–55. doi: 10.1016/0098-1354(93)80018-I.
Ellefsen, A. L., E. Bjørlykhaug, V. Æsøy, and H. Zhang. 2019. “An Unsupervised Reconstruction-Based Fault Detection Algorithm for Maritime Components.” IEEE Access 7:16101–9. doi: 10.1109/ACCESS.2019.2895394.
Gajjar, Shriram, and Ahmet Palazoglu. 2016. “A Data-Driven Multidimensional Visualization Technique for Process Fault Detection and Diagnosis.” Chemometrics and Intelligent Laboratory Systems 154:122–36. doi: 10.1016/j.chemolab.2016.03.027.
Guo, Yifan, Tianxi Ji, Qianlong Wang, Lixing Yu, Geyong Min, and Pan Li. 2020. “Unsupervised Anomaly Detection in IoT Systems for Smart Cities.” IEEE Transactions on Network Science and Engineering 7(4):2231–42. doi: 10.1109/TNSE.2020.3027543.
Hwang, Jenq-Neng, Shyh-Rong Lay, and A. Lippman. 1994. “Nonparametric Multivariate Density Estimation: A Comparative Study.” IEEE Transactions on Signal Processing 42(10):2795–2810. doi: 10.1109/78.324744.
Jaya, A. I., T. R. Aulia, F. D. Putri, and T. Rakhmawati. 2021. “T 2 Control Chart Based on PCA with KDE Control Limit for Monitoring Intrusion.” Journal of Physics: Conference Series 2123(1):012017. doi: 10.1088/1742-6596/2123/1/012017.
Jiang, Guoqian, Ping Xie, Haibo He, and Jun Yan. 2018. “Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information.” IEEE/ASME Transactions on Mechatronics 23(1):89–100. doi: 10.1109/TMECH.2017.2759301.
Jin, S., Z. Zhang, K. Chakrabarty, and X. Gu. 2020. “Self-Learning and Efficient Health-Status Analysis for a Core Router System.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39(9):1935–48. doi: 10.1109/TCAD.2019.2926506.
Kourti, Theodora, and John F. MacGregor. 1995. “Process Analysis, Monitoring and Diagnosis, Using Multivariate Projection Methods.” Chemometrics and Intelligent Laboratory Systems 28(1):3–21. doi: 10.1016/0169-7439(95)80036-9.
Kukkala, V. K., S. V. Thiruloga, and S. Pasricha. 2020. “INDRA: Intrusion Detection Using Recurrent Autoencoders in Automotive Embedded Systems.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39(11):3698–3710. doi: 10.1109/TCAD.2020.3012749.
Li, W., Y. Liu, Y. Li, and F. Guo. 2020. “Series Arc Fault Diagnosis and Line Selection Method Based on Recurrent Neural Network.” IEEE Access 8:177815–22. doi: 10.1109/ACCESS.2020.3027002.
Liu, Lei, Dongli Song, Zilin Geng, and Zejun Zheng. 2020. “A Real-Time Fault Early Warning Method for a High-Speed EMU Axle Box Bearing.” Sensors 20(3):823. doi: 10.3390/s20030823.
Machado, André Paulo Ferreira, Ricardo Emanuel Vaz Vargas, Patrick Marques Ciarelli, and Celso Jose Munaro. 2022. “Improving Performance of One-Class Classifiers Applied to Anomaly Detection in Oil Wells.” Journal of Petroleum Science and Engineering 218:110983. doi: 10.1016/j.petrol.2022.110983.
Monostori, L., B. Kádár, T. Bauernhansl, S. Kondoh, S. Kumara, G. Reinhart, O. Sauer, G. Schuh, W. Sihn, and K. Ueda. 2016. “Cyber-Physical Systems in Manufacturing.” CIRP Annals 65(2):621–41. doi: 10.1016/j.cirp.2016.06.005.
Nagarajan, S., S. Kayalvizhi, and B. Karthikeyan. 2016. “Neural Network Based Intelligent Sensor Fault Detection in a Three Tanks Interacting Level Process.” Pp. 2429–34 in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).
Principi, Emanuele, Damiano Rossetti, Stefano Squartini, and Francesco Piazza. 2019. “Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders.” IEEE/CAA Journal of Automatica Sinica 6(2):441–51. doi: 10.1109/JAS.2019.1911393.
Reinartz, Christopher, Murat Kulahci, and Ole Ravn. 2021. “An Extended Tennessee Eastman Simulation Dataset for Fault-Detection and Decision Support Systems.” Computers & Chemical Engineering 149:107281. doi: 10.1016/j.compchemeng.2021.107281.
Ren, Hao, Yi Chai, Jianfeng Qu, Xin Ye, and Qiu Tang. 2018. “A Novel Adaptive Fault Detection Methodology for Complex System Using Deep Belief Networks and Multiple Models: A Case Study on Cryogenic Propellant Loading System.” Neurocomputing 275:2111–25. doi: 10.1016/j.neucom.2017.10.063.
Shadi, Mohammad Reza, Mohammad-Taghi Ameli, and Sasan Azad. 2022. “A Real-Time Hierarchical Framework for Fault Detection, Classification, and Location in Power Systems Using PMUs Data and Deep Learning.” International Journal of Electrical Power & Energy Systems 134:107399. doi: 10.1016/j.ijepes.2021.107399.
Shao, Kaixuan, Yigang He, Zhikai Xing, and Bolun Du. 2023. “Detecting Wind Turbine Anomalies Using Nonlinear Dynamic Parameters-Assisted Machine Learning with Normal Samples.” Reliability Engineering & System Safety 233:109092. doi: 10.1016/j.ress.2023.109092.
Silva, Moisés Felipe, Alessandro Pacini, Andrea Sgambelluri, and Luca Valcarenghi. 2022. “Learning Long-and Short-Term Temporal Patterns for ML-Driven Fault Management in Optical Communication Networks.” IEEE Transactions on Network and Service Management 1–1. doi: 10.1109/TNSM.2022.3146869.
Stępień, Bartłomiej. 2016. “Confidence Intervals for the Long-Term Noise Indicators Using the Kernel Density Estimator.” Archives of Acoustics 41(3):517–25. doi: 10.1515/aoa-2016-0050.
Tang, Peng, Kaixiang Peng, Kai Zhang, Zhiwen Chen, Xu Yang, and Linlin Li. 2018. “A Deep Belief Network-Based Fault Detection Method for Nonlinear Processes.” IFAC-PapersOnLine 51(24):9–14. doi: 10.1016/j.ifacol.2018.09.522.
Wang, B., D. Liu, Y. Peng, and X. Peng. 2020. “Multivariate Regression-Based Fault Detection and Recovery of UAV Flight Data.” IEEE Transactions on Instrumentation and Measurement 69(6):3527–37. doi: 10.1109/TIM.2019.2935576.
Wang, B., X. Peng, M. Jiang, and D. Liu. 2020. “Real-Time Fault Detection for UAV Based on Model Acceleration Engine.” IEEE Transactions on Instrumentation and Measurement 69(12):9505–16. doi: 10.1109/TIM.2020.3001659.
Wang, Kai, Le Zhou, and Yalin Wang. 2020. “Enhanced Fault Detection Using Deviation Degree Penalty with Stacked Autoencoder in Industry Process.” Pp. 1084–89 in 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS).
Wang, Zhanwei, Lin Wang, Kunfeng Liang, and Yingying Tan. 2018. “Enhanced Chiller Fault Detection Using Bayesian Network and Principal Component Analysis.” Applied Thermal Engineering 141:898–905. doi: 10.1016/j.applthermaleng.2018.06.037.
Węglarczyk, Stanisław. 2018. “Kernel Density Estimation and Its Application.” ITM Web of Conferences 23:00037. doi: 10.1051/itmconf/20182300037.
Xiang, Gang, Ran Tao, Yu Peng, Kun Tian, and Chen Qu. 2020. “Unsupervised Deep Learning for Fault Detection on Spacecraft Using Improved Variational Autoencoder.” Pp. 5527–31 in 2020 Chinese Automation Congress (CAC).
Xiao, Yutang, Hongbo Shi, Boyu Wang, Yang Tao, Shuai Tan, and Bing Song. 2022. “Weighted Conditional Discriminant Analysis for Unseen Operating Modes Fault Diagnosis in Chemical Processes.” IEEE Transactions on Instrumentation and Measurement 71:1–14. doi: 10.1109/TIM.2022.3152235.
Yu, Jianbo, Xing Liu, and Lyujiangnan Ye. 2021. “Convolutional Long Short-Term Memory Autoencoder-Based Feature Learning for Fault Detection in Industrial Processes.” IEEE Transactions on Instrumentation and Measurement 70:1–15. doi: 10.1109/TIM.2020.3039614.
Yu, Jianbo, and Xuefeng Yan. 2021. “Deep unLSTM Network: Features with Memory Information Extracted from Unlabeled Data and Their Application on Industrial Unsupervised Industrial Fault Detection.” Applied Soft Computing 108:107382. doi: 10.1016/j.asoc.2021.107382.
Yu, Jungwon, Jaeyeong Yoo, Jaeyel Jang, June Ho Park, and Sungshin Kim. 2018. “A Novel Hybrid of Auto-Associative Kernel Regression and Dynamic Independent Component Analysis for Fault Detection in Nonlinear Multimode Processes.” Journal of Process Control 68:129–44. doi: 10.1016/j.jprocont.2018.05.004.
Zhao, B., C. Cheng, Z. Peng, X. Dong, and G. Meng. 2020. “Detecting the Early Damages in Structures With Nonlinear Output Frequency Response Functions and the CNN-LSTM Model.” IEEE Transactions on Instrumentation and Measurement 69(12):9557–67. doi: 10.1109/TIM.2020.3005113.
Zhao, Jiachen, Yongling Li, Haibo He, and Fang Deng. 2020. “One-Step Predictive Encoder - Gaussian Segment Model for Time Series Anomaly Detection.” Pp. 1–7 in 2020 International Joint Conference on Neural Networks (IJCNN).
Amarbayasgalan, Tsatsral, Van Huy Pham, Nipon Theera-Umpon, and Keun Ho Ryu. 2020. “Unsupervised Anomaly Detection Approach for Time-Series in Multi-Domains Using Deep Reconstruction Error.” Symmetry 12(8):1251. doi: 10.3390/sym12081251.
Ammann, Oliver, Gabriel Michau, and Olga Fink. 2020. “Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks.”
Anaissi, Ali, and Seid Miad Zandavi. 2019. “Multi-Objective Autoencoder for Fault Detection and Diagnosis in Higher-Order Data.” Pp. 1–8 in 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary: IEEE.
Belagoune, Soufiane, Noureddine Bali, Azzeddine Bakdi, Bousaadia Baadji, and Karim Atif. 2021. “Deep Learning through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems.” Measurement 177:109330. doi: 10.1016/j.measurement.2021.109330.
Benkő, Zsigmond, Tamás Bábel, and Zoltán Somogyvári. 2022. “Model-Free Detection of Unique Events in Time Series.” Scientific Reports 12(1):227. doi: 10.1038/s41598-021-03526-y.
Cacciarelli, Davide, and Murat Kulahci. 2022. “A Novel Fault Detection and Diagnosis Approach Based on Orthogonal Autoencoders.” Computers & Chemical Engineering 163:107853. doi: 10.1016/j.compchemeng.2022.107853.
Calabrese, Francesca, Alberto Regattieri, Lucia Botti, Cristina Mora, and Francesco Gabriele Galizia. 2020. “Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems.” Applied Sciences 10(12):4120. doi: 10.3390/app10124120.
Chen, Tingting, Xueping Liu, Bizhong Xia, Wei Wang, and Yongzhi Lai. 2020. “Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder.” IEEE Access 8:47072–81. doi: 10.1109/ACCESS.2020.2977892.
Dong, Jingyi, Yuntong Ma, and Datong Liu. 2019. “Deep Learning Based Multiple Sensors Monitoring and Abnormal Discovery for Satellite Power System.” Pp. 638–43 in 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC).
Downs, J. J., and E. F. Vogel. 1993. “A Plant-Wide Industrial Process Control Problem.” Computers & Chemical Engineering 17(3):245–55. doi: 10.1016/0098-1354(93)80018-I.
Ellefsen, A. L., E. Bjørlykhaug, V. Æsøy, and H. Zhang. 2019. “An Unsupervised Reconstruction-Based Fault Detection Algorithm for Maritime Components.” IEEE Access 7:16101–9. doi: 10.1109/ACCESS.2019.2895394.
Gajjar, Shriram, and Ahmet Palazoglu. 2016. “A Data-Driven Multidimensional Visualization Technique for Process Fault Detection and Diagnosis.” Chemometrics and Intelligent Laboratory Systems 154:122–36. doi: 10.1016/j.chemolab.2016.03.027.
Guo, Yifan, Tianxi Ji, Qianlong Wang, Lixing Yu, Geyong Min, and Pan Li. 2020. “Unsupervised Anomaly Detection in IoT Systems for Smart Cities.” IEEE Transactions on Network Science and Engineering 7(4):2231–42. doi: 10.1109/TNSE.2020.3027543.
Hwang, Jenq-Neng, Shyh-Rong Lay, and A. Lippman. 1994. “Nonparametric Multivariate Density Estimation: A Comparative Study.” IEEE Transactions on Signal Processing 42(10):2795–2810. doi: 10.1109/78.324744.
Jaya, A. I., T. R. Aulia, F. D. Putri, and T. Rakhmawati. 2021. “T 2 Control Chart Based on PCA with KDE Control Limit for Monitoring Intrusion.” Journal of Physics: Conference Series 2123(1):012017. doi: 10.1088/1742-6596/2123/1/012017.
Jiang, Guoqian, Ping Xie, Haibo He, and Jun Yan. 2018. “Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information.” IEEE/ASME Transactions on Mechatronics 23(1):89–100. doi: 10.1109/TMECH.2017.2759301.
Jin, S., Z. Zhang, K. Chakrabarty, and X. Gu. 2020. “Self-Learning and Efficient Health-Status Analysis for a Core Router System.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39(9):1935–48. doi: 10.1109/TCAD.2019.2926506.
Kourti, Theodora, and John F. MacGregor. 1995. “Process Analysis, Monitoring and Diagnosis, Using Multivariate Projection Methods.” Chemometrics and Intelligent Laboratory Systems 28(1):3–21. doi: 10.1016/0169-7439(95)80036-9.
Kukkala, V. K., S. V. Thiruloga, and S. Pasricha. 2020. “INDRA: Intrusion Detection Using Recurrent Autoencoders in Automotive Embedded Systems.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39(11):3698–3710. doi: 10.1109/TCAD.2020.3012749.
Li, W., Y. Liu, Y. Li, and F. Guo. 2020. “Series Arc Fault Diagnosis and Line Selection Method Based on Recurrent Neural Network.” IEEE Access 8:177815–22. doi: 10.1109/ACCESS.2020.3027002.
Liu, Lei, Dongli Song, Zilin Geng, and Zejun Zheng. 2020. “A Real-Time Fault Early Warning Method for a High-Speed EMU Axle Box Bearing.” Sensors 20(3):823. doi: 10.3390/s20030823.
Machado, André Paulo Ferreira, Ricardo Emanuel Vaz Vargas, Patrick Marques Ciarelli, and Celso Jose Munaro. 2022. “Improving Performance of One-Class Classifiers Applied to Anomaly Detection in Oil Wells.” Journal of Petroleum Science and Engineering 218:110983. doi: 10.1016/j.petrol.2022.110983.
Monostori, L., B. Kádár, T. Bauernhansl, S. Kondoh, S. Kumara, G. Reinhart, O. Sauer, G. Schuh, W. Sihn, and K. Ueda. 2016. “Cyber-Physical Systems in Manufacturing.” CIRP Annals 65(2):621–41. doi: 10.1016/j.cirp.2016.06.005.
Nagarajan, S., S. Kayalvizhi, and B. Karthikeyan. 2016. “Neural Network Based Intelligent Sensor Fault Detection in a Three Tanks Interacting Level Process.” Pp. 2429–34 in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).
Principi, Emanuele, Damiano Rossetti, Stefano Squartini, and Francesco Piazza. 2019. “Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders.” IEEE/CAA Journal of Automatica Sinica 6(2):441–51. doi: 10.1109/JAS.2019.1911393.
Reinartz, Christopher, Murat Kulahci, and Ole Ravn. 2021. “An Extended Tennessee Eastman Simulation Dataset for Fault-Detection and Decision Support Systems.” Computers & Chemical Engineering 149:107281. doi: 10.1016/j.compchemeng.2021.107281.
Ren, Hao, Yi Chai, Jianfeng Qu, Xin Ye, and Qiu Tang. 2018. “A Novel Adaptive Fault Detection Methodology for Complex System Using Deep Belief Networks and Multiple Models: A Case Study on Cryogenic Propellant Loading System.” Neurocomputing 275:2111–25. doi: 10.1016/j.neucom.2017.10.063.
Shadi, Mohammad Reza, Mohammad-Taghi Ameli, and Sasan Azad. 2022. “A Real-Time Hierarchical Framework for Fault Detection, Classification, and Location in Power Systems Using PMUs Data and Deep Learning.” International Journal of Electrical Power & Energy Systems 134:107399. doi: 10.1016/j.ijepes.2021.107399.
Shao, Kaixuan, Yigang He, Zhikai Xing, and Bolun Du. 2023. “Detecting Wind Turbine Anomalies Using Nonlinear Dynamic Parameters-Assisted Machine Learning with Normal Samples.” Reliability Engineering & System Safety 233:109092. doi: 10.1016/j.ress.2023.109092.
Silva, Moisés Felipe, Alessandro Pacini, Andrea Sgambelluri, and Luca Valcarenghi. 2022. “Learning Long-and Short-Term Temporal Patterns for ML-Driven Fault Management in Optical Communication Networks.” IEEE Transactions on Network and Service Management 1–1. doi: 10.1109/TNSM.2022.3146869.
Stępień, Bartłomiej. 2016. “Confidence Intervals for the Long-Term Noise Indicators Using the Kernel Density Estimator.” Archives of Acoustics 41(3):517–25. doi: 10.1515/aoa-2016-0050.
Tang, Peng, Kaixiang Peng, Kai Zhang, Zhiwen Chen, Xu Yang, and Linlin Li. 2018. “A Deep Belief Network-Based Fault Detection Method for Nonlinear Processes.” IFAC-PapersOnLine 51(24):9–14. doi: 10.1016/j.ifacol.2018.09.522.
Wang, B., D. Liu, Y. Peng, and X. Peng. 2020. “Multivariate Regression-Based Fault Detection and Recovery of UAV Flight Data.” IEEE Transactions on Instrumentation and Measurement 69(6):3527–37. doi: 10.1109/TIM.2019.2935576.
Wang, B., X. Peng, M. Jiang, and D. Liu. 2020. “Real-Time Fault Detection for UAV Based on Model Acceleration Engine.” IEEE Transactions on Instrumentation and Measurement 69(12):9505–16. doi: 10.1109/TIM.2020.3001659.
Wang, Kai, Le Zhou, and Yalin Wang. 2020. “Enhanced Fault Detection Using Deviation Degree Penalty with Stacked Autoencoder in Industry Process.” Pp. 1084–89 in 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS).
Wang, Zhanwei, Lin Wang, Kunfeng Liang, and Yingying Tan. 2018. “Enhanced Chiller Fault Detection Using Bayesian Network and Principal Component Analysis.” Applied Thermal Engineering 141:898–905. doi: 10.1016/j.applthermaleng.2018.06.037.
Węglarczyk, Stanisław. 2018. “Kernel Density Estimation and Its Application.” ITM Web of Conferences 23:00037. doi: 10.1051/itmconf/20182300037.
Xiang, Gang, Ran Tao, Yu Peng, Kun Tian, and Chen Qu. 2020. “Unsupervised Deep Learning for Fault Detection on Spacecraft Using Improved Variational Autoencoder.” Pp. 5527–31 in 2020 Chinese Automation Congress (CAC).
Xiao, Yutang, Hongbo Shi, Boyu Wang, Yang Tao, Shuai Tan, and Bing Song. 2022. “Weighted Conditional Discriminant Analysis for Unseen Operating Modes Fault Diagnosis in Chemical Processes.” IEEE Transactions on Instrumentation and Measurement 71:1–14. doi: 10.1109/TIM.2022.3152235.
Yu, Jianbo, Xing Liu, and Lyujiangnan Ye. 2021. “Convolutional Long Short-Term Memory Autoencoder-Based Feature Learning for Fault Detection in Industrial Processes.” IEEE Transactions on Instrumentation and Measurement 70:1–15. doi: 10.1109/TIM.2020.3039614.
Yu, Jianbo, and Xuefeng Yan. 2021. “Deep unLSTM Network: Features with Memory Information Extracted from Unlabeled Data and Their Application on Industrial Unsupervised Industrial Fault Detection.” Applied Soft Computing 108:107382. doi: 10.1016/j.asoc.2021.107382.
Yu, Jungwon, Jaeyeong Yoo, Jaeyel Jang, June Ho Park, and Sungshin Kim. 2018. “A Novel Hybrid of Auto-Associative Kernel Regression and Dynamic Independent Component Analysis for Fault Detection in Nonlinear Multimode Processes.” Journal of Process Control 68:129–44. doi: 10.1016/j.jprocont.2018.05.004.
Zhao, B., C. Cheng, Z. Peng, X. Dong, and G. Meng. 2020. “Detecting the Early Damages in Structures With Nonlinear Output Frequency Response Functions and the CNN-LSTM Model.” IEEE Transactions on Instrumentation and Measurement 69(12):9557–67. doi: 10.1109/TIM.2020.3005113.
Zhao, Jiachen, Yongling Li, Haibo He, and Fang Deng. 2020. “One-Step Predictive Encoder - Gaussian Segment Model for Time Series Anomaly Detection.” Pp. 1–7 in 2020 International Joint Conference on Neural Networks (IJCNN).
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