Data-driven performance degradation trend predicting method for the rotating equipment

被引：0
|
作者：
Wang Q. [1 ]
Liu J. [1 ]
Liu X. [1 ]
Xu S. [2 ]
机构：
[1] Beijing Municipal Key Laboratory of Health Monitoring and Self-recovery of High-End Machinery Equipment, Beijing University of Chemical Technology, Beijing
[2] SINOPEC Qingdao Research Institute of Safety Engineering, Qingdao
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 03期
关键词：
Data-driven; L[!sub]1[!/sub] trend filtering; Performance degradation; Predictive maintenance; Trend prediction;
D O I：
10.13196/j.cims.2022.03.007
中图分类号：
学科分类号：
摘要：
It is difficult to track the occurrence and development of the performance degradation of rotating machinery by the conventional fixed threshold alarm method in the in-service condition monitoring system. To solve this problem, a data-driven rotating equipment performance degradation trend prediction model was constructed by using the raw normal vibration monitoring data and real-time monitoring data, and a method for predicting performance degradation trend based on the spectral distance index operating reliability curve l1 trend filtering was proposed. By dynamically tracking the point-by-point slope change of the trend filtering curve, the occurrence and development of performance degradation of rotating equipment could be detected. Using the Cincinnati Intelligent Maintenance Information System (IMS) center bearing experimental data and the rotor unbalance fault case data of a Chinese petrochemical company's centrifugal compressor to verify the model, the results showed that the data-driven performance degradation prediction model of rotating equipment only needed raw vibration data in normal operating conditions without relying on the prior knowledge of external experts, and could accurately predict and track the occurrence and development of the performance degradation trend of rotating equipment. © 2022, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：724 / 734
页数：10
相关论文
共 26 条
  • [1] RUIZ-SARMIENTO J R, MONROY J, MORENO F-A, Et al., A predictive model for the maintenance of Industrial machinery in the context of Industry 4.0, Engineering Applications of Artificial Intelligence, 87, (2020)
  • [2] YANG Rui, LI Hongkun, HE Changbo, Et al., Rolling element bearing incipient fault feature extraction based on optimal wavelet scales cyclic spectrum, Journal of Mechanical Engineering, 54, 17, pp. 208-217, (2018)
  • [3] WANG Hongchao, CHEN Jin, DONG Guangming, Fault diagnosis method for rolling bearing's weak fault based on minimum entropy deconvolution and sparse decomposition, Journal of Mechanical Engineering, 49, 1, pp. 88-94, (2013)
  • [4] JIA Feng, WU Bing, XIONG Xiaoyan, Et al., Early fault diagnosis of bearing based on multi-dimension permutation entropy and SVM, Computer Integrated Manufacturing Systems, 20, 9, pp. 2275-2282, (2014)
  • [5] TANG Guiji, WANG Xiaolong, Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing, Journal of Xi'an Jiaotong University, 49, 5, pp. 73-81, (2015)
  • [6] LI Hongkun, LIU Hongyi, XU Fujian, Et al., Method for the optimal continuous wavelet reconstruction scale determination and early fault classification, Journal of Mechanical Engineering, 50, 17, pp. 69-76, (2014)
  • [7] GU Yutong, JIA Ziwen, YIN Chuantao, Et al., Non-dimensional parameters trend analysis method in the early warning and diagnosis of wind turbine gearbox failures, Journal of Vibration Shock, 36, 19, pp. 213-220, (2017)
  • [8] GU Yutong, SONG Lei, SU Luwei, Et al., Early warning method for wind turbine gearbox based on multivariate outlier detection of angle domain parameters, Journal of Vibration Shock, 34, 1, pp. 80-87, (2015)
  • [9] WEI Xiang, LI Benwei, ZHANG Yun, A warning method for early weak failures of bearings by symbolic time series analysis, Journal of Xi'an Jiaotong University, 52, 6, pp. 84-90, (2018)
  • [10] MENG Lingxia, XU Xiaoli, XU Yangmei, Et al., Time-frequency ridge manifold early fault warning on variable conditions, Transactions of Beijing Institute of Technology, 37, 9, pp. 942-947, (2017)
123