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Failure diagnosis using deep belief learning based health state classification

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Cited by:

  1. Huang, Wei & Shao, Changzheng & Hu, Bo & Li, Weizhan & Sun, Yue & Xie, Kaigui & Zio, Enrico & Li, Wenyuan, 2023. "A restoration-clustering-decomposition learning framework for aging-related failure rate estimation of distribution transformers," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
  2. Yongzhi Qu & Yue Zhang & Miao He & David He & Chen Jiao & Zude Zhou, 2019. "Gear pitting fault diagnosis using disentangled features from unsupervised deep learning," Journal of Risk and Reliability, , vol. 233(5), pages 719-730, October.
  3. Yanxi Zhang & Deyong You & Xiangdong Gao & Congyi Wang & Yangjin Li & Perry P. Gao, 2020. "Real-time monitoring of high-power disk laser welding statuses based on deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 799-814, April.
  4. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
  5. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
  6. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
  7. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
  8. Yuanyuan Yang & Md Muhie Menul Haque & Dongling Bai & Wei Tang, 2021. "Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review," Energies, MDPI, vol. 14(21), pages 1-26, October.
  9. Ashis Kumar Das & Harihar Bhattarai & Saji Saraswathy Gopalan, 2019. "Determinants of Generic Drug Use Among Medicare Beneficiaries- Predictive Modelling Analysis Using Artificial Intelligence," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 22(1), pages 16405-16413, October.
  10. Ossama Abou Ali Modad & Jason Ryska & Abdallah Chehade & Georges Ayoub, 2025. "Revolutionizing sheet metal stamping through industry 5.0 digital twins: a comprehensive review," Journal of Intelligent Manufacturing, Springer, vol. 36(6), pages 3717-3739, August.
  11. Ariannik, Mohamadreza & Razi-Kazemi, Ali A. & Lehtonen, Matti, 2020. "An approach on lifetime estimation of distribution transformers based on degree of polymerization," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
  12. Zhao, Junjie & Li, Yi-Guang & Sampath, Suresh, 2023. "A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics," Applied Energy, Elsevier, vol. 332(C).
  13. Ajagekar, Akshay & You, Fengqi, 2021. "Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems," Applied Energy, Elsevier, vol. 303(C).
  14. Andriotis, C.P. & Papakonstantinou, K.G., 2019. "Managing engineering systems with large state and action spaces through deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
  15. Xiaobo Bi & Jiansheng Lin & Daijie Tang & Fengrong Bi & Xin Li & Xiao Yang & Teng Ma & Pengfei Shen, 2020. "VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals," Energies, MDPI, vol. 13(1), pages 1-20, January.
  16. Mengyao Gu & Jiangqin Ge, 2023. "Research on health state assessment and prediction for complex equipment based on the improved FMECA and GM (1,1)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 523-538, March.
  17. Zhang, Liangwei & Lin, Jing & Karim, Ramin, 2015. "An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 482-497.
  18. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  19. Chuang Wang & Pingyu Jiang, 2019. "Deep neural networks based order completion time prediction by using real-time job shop RFID data," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1303-1318, March.
  20. Wang, Sen & Qin, Chaoxu & Feng, Qihong & Javadpour, Farzam & Rui, Zhenhua, 2021. "A framework for predicting the production performance of unconventional resources using deep learning," Applied Energy, Elsevier, vol. 295(C).
  21. Shungang Ning & Jianzhong Sun & Cui Liu & Yang Yi, 2021. "Applications of deep learning in big data analytics for aircraft complex system anomaly detection," Journal of Risk and Reliability, , vol. 235(5), pages 923-940, October.
  22. Hui Zhang & Cunhua Pan & Yuanxin Wang & Min Xu & Fu Zhou & Xin Yang & Lou Zhu & Chao Zhao & Yangfan Song & Hongwei Chen, 2022. "Fault Diagnosis of Coal Mill Based on Kernel Extreme Learning Machine with Variational Model Feature Extraction," Energies, MDPI, vol. 15(15), pages 1-14, July.
  23. Yong Hu & Boyu Ping & Deliang Zeng & Yuguang Niu & Yaokui Gao, 2020. "Modeling of Coal Mill System Used for Fault Simulation," Energies, MDPI, vol. 13(7), pages 1-14, April.
  24. Lee, Jinwook & Kim, Myungyon & Ko, Jin Uk & Jung, Joon Ha & Sun, Kyung Ho & Youn, Byeng D., 2022. "Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
  25. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
  26. Mercedes Grijalvo Martín & Antonia Pacios Álvarez & Joaquín Ordieres-Meré & Javier Villalba-Díez & Gustavo Morales-Alonso, 2020. "New Business Models from Prescriptive Maintenance Strategies Aligned with Sustainable Development Goals," Sustainability, MDPI, vol. 13(1), pages 1-26, December.
  27. Xingyu Xiao & Jingang Liang & Jiejuan Tong & Haitao Wang, 2024. "Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends," Energies, MDPI, vol. 17(10), pages 1-35, May.
  28. Fink, Olga & Zio, Enrico & Weidmann, Ulrich, 2014. "Predicting component reliability and level of degradation with complex-valued neural networks," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 198-206.
  29. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
  30. Ma, Zhonghai & Liao, Haitao & Gao, Jianhang & Nie, Songlin & Geng, Yugang, 2023. "Physics-Informed Machine Learning for Degradation Modeling of an Electro-Hydrostatic Actuator System," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
  31. Malinowski, Simon & Chebel-Morello, Brigitte & Zerhouni, Noureddine, 2015. "Remaining useful life estimation based on discriminating shapelet extraction," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 279-288.
  32. Peters, Benjamin & Yildirim, Murat & Gebraeel, Nagi & Paynabar, Kamran, 2020. "Severity-based diagnosis for vehicular electric systems with multiple, interacting fault modes," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  33. Peng Jieyang & Andreas Kimmig & Wang Dongkun & Zhibin Niu & Fan Zhi & Wang Jiahai & Xiufeng Liu & Jivka Ovtcharova, 2023. "A systematic review of data-driven approaches to fault diagnosis and early warning," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3277-3304, December.
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