Intelligent Fault Prognosis Method Based on Stacked Autoencoder and Continuous Deep Belief Network

被引:5
|
作者
Zhang, Chao [1 ]
Zhang, Yibin [2 ]
Huang, Qixuan [1 ]
Zhou, Yong [1 ]
机构
[1] Northwestern Polytech Univ, Sch Aeronaut, Dept Integrated Technol & Control Engn, Xian 710072, Peoples R China
[2] Aeronaut Radio Elect Res Inst, Shanghai 200233, Peoples R China
关键词
continuous deep belief network; fault prognosis; rolling bearings; stacked autoencoder; MACHINE; ALGORITHM; DIAGNOSIS;
D O I
10.3390/act12030117
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Mechanical fault prediction is one of the main problems in condition-based maintenance, and its purpose is to predict the future working status of the machine based on the collected status information of the machine. However, on one hand, the model health indices based on the information collected by the sensors will directly affect the evaluation results of the system. On the other hand, because the model health index is a continuous time series, the effect of feature learning on continuous data also affects the results of fault prognosis. This paper makes full use of the autonomous information fusion capability of the stacked autoencoder and the strong feature learning capability of continuous deep belief networks for continuous data, and proposes a novel fault prognosis method. Firstly, a stacked autoencoder is used to construct the model health index through the feature learning and information fusion of the vibration signals collected by the sensors. To solve the local fluctuations in the health indices, the exponentially weighted moving average method is used to smooth the index data to reduce the impact of noise. Then, a continuous deep belief network is used to perform feature learning on the constructed health index to predict future performance changes in the model. Finally, a fault prognosis experiment based on bearing data was performed. The experimental results show that the method combines the advantages of stacked autoencoders and continuous deep belief networks, and has a lower prediction error than traditional intelligent fault prognosis methods.
引用
收藏
页数:22
相关论文
共 50 条
  • [21] Fault diagnosis method of rotating machinery based on stacked denoising autoencoder
    Chen, Zhouliang
    Li, Zhinong
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 34 (06) : 3443 - 3449
  • [22] Transformer fault diagnosis based on adversarial generative networks and deep stacked autoencoder
    Zhang, Lei
    Xu, Zhongyang
    Lu, Chen
    Qiao, Tianjiao
    Su, Hongzhi
    Luo, Yazhou
    HELIYON, 2024, 10 (09)
  • [23] Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings
    Zhu, Haiping
    Cheng, Jiaxin
    Zhang, Cong
    Wu, Jun
    Shao, Xinyu
    APPLIED SOFT COMPUTING, 2020, 88
  • [24] Transformer Fault Diagnosis Based on Adversarial Generative Networks and Deep Stacked Autoencoder
    Zhang, Lei
    Xu, Zhongyang
    Qiao, Tianjiao
    Lu, Chen
    Su, Hongzhi
    Luo, Yazhou
    2024 THE 7TH INTERNATIONAL CONFERENCE ON ENERGY, ELECTRICAL AND POWER ENGINEERING, CEEPE 2024, 2024, : 496 - 504
  • [25] A Network Intrusion Detection Method Based on Stacked Autoencoder and LSTM
    Yan, Yu
    Qi, Lin
    Wang, Jie
    Lin, Yun
    Chen, Lei
    ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2020,
  • [26] Fault diagnosis and isolation method for wind turbines based on deep belief network
    Li M.-S.
    Yu D.
    Chen Z.-M.
    Xiahou K.-S.
    Li Y.-Y.
    Ji T.-Y.
    Dianji yu Kongzhi Xuebao/Electric Machines and Control, 2019, 23 (02): : 114 - 122
  • [27] A Deep Belief Network-based Fault Detection Method for Nonlinear Processes
    Tang, Peng
    Peng, Kaixiang
    Zhang, Kai
    Chen, Zhiwen
    Yang, Xu
    Li, Linlin
    IFAC PAPERSONLINE, 2018, 51 (24): : 9 - 14
  • [28] Fault diagnosis method of petrochemical air compressor based on deep belief network
    Lu C.
    Li W.
    Huagong Xuebao/CIESC Journal, 2019, 70 (02): : 757 - 763
  • [29] A gear fault diagnosis method based on deep belief network and information fusion
    Li Y.
    Huang D.
    Ma J.
    Jiang L.
    Zhendong yu Chongji/Journal of Vibration and Shock, 2021, 40 (08): : 62 - 69
  • [30] Deep Neural Network Hardware Implementation Based on Stacked Sparse Autoencoder
    Coutinho, Maria G. F.
    Torquato, Matheus F.
    Fernandes, Marcelo A. C.
    IEEE ACCESS, 2019, 7 : 40674 - 40694