| Citation: | LIU Xiyang, CHEN Guo, WEI Xunkai, et al. Early fault alarm method of rolling bearing based on wavelet analysis and convolution neural network[J]. Journal of Aerospace Power, 2024, 39（9）：20220622 doi: 10.13224/j.cnki.jasp.20220622
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In view of the problems in condition monitoring of aero-engine main bearing, such as the difficulty in obtaining the real fault samples, the limitation in defining the general alarm threshold under variable conditions and the difficulty to identify the early weak faults, a general alarm method for early faults of rolling bearings was proposed. This method only trained convolutional neural networks based on normal samples, constructed evolution state indicator by the characteristic distance between degraded data and normal data, and unified the alarm thresholds of different working conditions based on training labels; at the same time, the sensitivity of wavelet band envelope signal to early high-frequency fault was used to realize early warning; then, the evolution stages were divided based on the Pauta criterion, according to which the degradation and failure thresholds were determined; finally, the remaining useful life was predicted step by step based on particle filter. Three groups of test results showed that the degradation threshold and failure threshold of wavelet analysis and convolution neural network (Wavelet-CNN) based on different fault test data can be normalized around 0.6 and 1.0, and the predictions of degradation start time were 13.01%, 12.33% and 13.70% earlier than those of non wavelet methods respectively.
| [1] |
尉询楷,朱纪洪,陈良峰,等. 航空发动机PHM中的数据挖掘机遇与挑战[J]. 计算机工程与科学,2012,34(4): 88-93. WEI Xunkai,ZHU Jihong,CHEN Liangfeng,et al. The opportunities and challenges of data mining in gas turbine engine prognostics and health management[J]. Computer Engineering & Science,2012,34(4): 88-93. (in Chinese doi: 10.3969/j.issn.1007-130X.2012.04.018
WEI Xunkai, ZHU Jihong, CHEN Liangfeng, et al. The opportunities and challenges of data mining in gas turbine engine prognostics and health management[J]. Computer Engineering & Science, 2012, 34(4): 88-93. (in Chinese) doi: 10.3969/j.issn.1007-130X.2012.04.018
|
| [2] |
YANG Chuangyan,MA Jun,WANG Xiaodong,et al. A novel based-performance degradation indicator RUL prediction model and its application in rolling bearing[J]. ISA Transactions,2022,121: 349-364. doi: 10.1016/j.isatra.2021.03.045
|
| [3] |
LIN Tong,CHEN Guo,OUYANG Wenli,et al. Hyper-spherical distance discrimination: a novel data description method for aero-engine rolling bearing fault detection[J]. Mechanical Systems and Signal Processing,2018,109: 330-351. doi: 10.1016/j.ymssp.2018.01.009
|
| [4] |
MANJURUL I M M,PROSVIRIN A E,KIM J M. Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines[J]. Mechanical Systems and Signal Processing,2021,160: 107853. doi: 10.1016/j.ymssp.2021.107853
|
| [5] |
刘小峰,冯伟,柏林. 考虑退化轨迹差异性与相似性的轴承RUL预测[J]. 控制与决策,2021,36(11): 2833-2840. LIU Xiaofeng,FENG Wei,BO Lin. Prediction of bearing remaining useful life involving difference and similarity of degradation trajectories[J]. Control and Decision,2021,36(11): 2833-2840. (in Chinese
LIU Xiaofeng, FENG Wei, BO Lin. Prediction of bearing remaining useful life involving difference and similarity of degradation trajectories[J]. Control and Decision, 2021, 36(11): 2833-2840. (in Chinese)
|
| [6] |
LIU Xiyang,CHEN Guo,HAO Tengfei,et al. A combined deep learning model for damage size estimation of rolling bearing[J]. International Journal of Engine Research,2023,24(4): 1362-1373. doi: 10.1177/14680874221086601
|
| [7] |
LU Chen,WANG Zhenya,ZHOU Bo. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification[J]. Advanced Engineering Informatics,2017,32: 139-151. doi: 10.1016/j.aei.2017.02.005
|
| [8] |
SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research,2014,15: 1929-1958.
|
| [9] |
刘建伟,赵会丹,罗雄麟,等. 深度学习批归一化及其相关算法研究进展[J]. 自动化学报,2020,46(6): 1090-1120. LIU Jianwei,ZHAO Huidan,LUO Xionglin,et al. Research progress on batch normalization of deep learning and its related algorithms[J]. Acta Automatica Sinica,2020,46(6): 1090-1120. (in Chinese
LIU Jianwei, ZHAO Huidan, LUO Xionglin, et al. Research progress on batch normalization of deep learning and its related algorithms[J]. Acta Automatica Sinica, 2020, 46(6): 1090-1120. (in Chinese)
|
| [10] |
QIU Hai,LEE J,LIN Jing,et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration,2006,289(4/5): 1066-1090.
|
| [11] |
WANG Biao,LEI Yaguo,LI Naipeng,et al. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings[J]. IEEE Transactions on Reliability,2020,69(1): 401-412. doi: 10.1109/TR.2018.2882682
|
| [12] |
LIU Xiyang,CHEN Guo,CHENG Zhenjie,et al. Convolution neural network based particle filtering for remaining useful life prediction of rolling bearing[J]. Advances in Mechanical Engineering,2022,14(6): 168781322211006.
|
| [13] |
AIT SGHIR K,COUSINARD O,BOLAERS F,et al. Bearing monitoring in variable speed regime using normalized indicators[J]. Mechanics & Industry,2016,17(3): 305.
|
| [14] |
张向阳,陈果,郝腾飞,等. 基于机匣信号的滚动轴承故障卷积神经网络诊断方法[J]. 航空动力学报,2019,34(12): 2729-2737. ZHANG Xiangyang,CHEN Guo,HAO Tengfei,et al. Convolutional neural network diagnosis method of rolling bearing fault based on casing signal[J]. Journal of Aerospace Power,2019,34(12): 2729-2737. (in Chinese
ZHANG Xiangyang, CHEN Guo, HAO Tengfei, et al. Convolutional neural network diagnosis method of rolling bearing fault based on casing signal[J]. Journal of Aerospace Power, 2019, 34(12): 2729-2737. (in Chinese)
|
| [15] |
胡爱军,孙俊豪,邢磊,等. 基于冲击特征提取的旋转机械智能故障诊断[J]. 航空动力学报,2023,38(12): 2973-2981. HU Aijun,SUN Junhao,XING Lei,et al. Intelligent fault diagnosis of rotating machinery based on impact feature extraction[J]. Journal of Aerospace Power,2023,38(12): 2973-2981. (in Chinese
HU Aijun, SUN Junhao, XING Lei, et al. Intelligent fault diagnosis of rotating machinery based on impact feature extraction[J]. Journal of Aerospace Power, 2023, 38(12): 2973-2981. (in Chinese)
|
| [16] |
王洪伟. 航空发动机滚动轴承故障诊断与预测关键技术研究[D]. 南京: 南京航空航天大学,2015. WANG Hongwei. Research on key techniques of fault diagnosis and prognosis for aero engine rolling bearing[D]. Nanjing: Nanjing University of Aeronautics and Astronautics,2015. (in Chinese
WANG Hongwei. Research on key techniques of fault diagnosis and prognosis for aero engine rolling bearing[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2015. (in Chinese)
|
| [17] |
ASWAL N,SEN S,MEVEL L. Estimation of local failure in tensegrity using Interacting Particle-Ensemble Kalman Filter[J]. Mechanical Systems and Signal Processing,2021,160: 107824. doi: 10.1016/j.ymssp.2021.107824
|
| [18] |
LI Lin,SALDIVAR A A F,BAI Yun,et al. Battery remaining useful life prediction with inheritance particle filtering[J]. Energies,2019,12(14): 2784. doi: 10.3390/en12142784
|
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