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Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM

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  • Gao, Shuzhi
  • Zhang, Sixuan
  • Zhang, Yimin
  • Gao, Yue

Abstract

In order to solve the problem of evaluation and prediction of rolling bearing operation reliability, a prediction method of rolling bearing operational reliability is proposed based on isometric mapping and nonhomogeneous cuckoo search-least squares support vector machine(NoCuSa-LSSVM). Firstly, isometric mapping (ISOMAP)algorithm is used to reduce the dimensionality of the high-dimensional collection composed of the characteristics of time domain, frequency domain and time-frequency domain of bearing vibration signal. Secondly, as the characteristics of the performance degradation state of the bearing, the integrated characteristic index establishes the reliability model of the rolling bearing through the logistic regression model. Finally, the NoCuSa-LSVM model is used to predict the characteristics of bearing performance degradation state, and the results are embedded into the established reliability model, so as to obtain the prediction results of bearing operational reliability. The method proposed in this paper was verified by the whole life test data of rolling bearing from the university of Cincinnati.

Suggested Citation

  • Gao, Shuzhi & Zhang, Sixuan & Zhang, Yimin & Gao, Yue, 2020. "Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:reensy:v:201:y:2020:i:c:s0951832019312992
    DOI: 10.1016/j.ress.2020.106968
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    References listed on IDEAS

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    1. Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
    2. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    3. Tao, Tao & Zio, Enrico & Zhao, Wei, 2018. "A novel support vector regression method for online reliability prediction under multi-state varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 35-49.
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    Cited by:

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    2. Jia-Qi, Liu & Yun-Wen, Feng & Da, Teng & Jun-Yu, Chen & Cheng, Lu, 2023. "Operational reliability evaluation and analysis framework of civil aircraft complex system based on intelligent extremum machine learning model," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Liu, Junqiang & Pan, Chunlu & Lei, Fan & Hu, Dongbin & Zuo, Hongfu, 2021. "Fault prediction of bearings based on LSTM and statistical process analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    4. He, Xinxin & Wang, Zhijian & Li, Yanfeng & Khazhina, Svetlana & Du, Wenhua & Wang, Junyuan & Wang, Wenzhao, 2022. "Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Xie, Bin & Wang, Yanzhong & Zhu, Yunyi & Liu, Peng & Wu, Yu & Lu, Fengxia, 2024. "Time-variant reliability analysis of angular contact ball bearing considering the coupled effect of rolling contact fatigue damage and wear," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. 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).
    7. Guo, Yongjin & Wang, Hongdong & Guo, Yu & Zhong, Mingjun & Li, Qing & Gao, Chao, 2022. "System operational reliability evaluation based on dynamic Bayesian network and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Zuo, Lin & Xu, Fengjie & Zhang, Changhua & Xiahou, Tangfan & Liu, Yu, 2022. "A multi-layer spiking neural network-based approach to bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    9. Baklouti, Ahmad & Dammak, Khalil & El Hami, Abdelkhalak, 2022. "Optimum reliable design of rolling element bearings using multi-objective optimization based on C-NSGA-II," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    10. Li, Xin & Yang, Yu & Wu, Zhantao & Yan, Ke & Shao, Haidong & Cheng, Junsheng, 2022. "High-accuracy gearbox health state recognition based on graph sparse random vector functional link network," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).

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