Reliability Prediction Method Based on State Space Model for Rolling Element Bearing

doi:10.1007/s12204-015-1629-4

Abstract

Abstract: Reliability analysis based on equipment’s performance degradation characteristics is one of the significant research areas in reliability research. Nowadays, many researches are carried on multi-sample analysis. But it is limited for a single equipment reliability prediction. Therefore, the method of reliability prediction based on state space model (SSM) is proposed in this research. Feature energy of the monitored signals is extracted with the wavelet packet analysis and the associated frequency band energy with online monitored data. Then, degradation feature is improved by moving average filtering processing taken as input pair model parameter of SSM to be estimated. In the end, state space predicting model of degradation index is established. The probability density distribution of the degradation index is predicted, and the degree of reliability is calculated. A real testing example of bearing is used to demonstrate the rationality and effectiveness of this method. It is a useful method for single sample reliability prediction.

Key words: reliability prediction| state space model| feature extraction| wavelet analysis| moving average

摘要： Reliability analysis based on equipment’s performance degradation characteristics is one of the significant research areas in reliability research. Nowadays, many researches are carried on multi-sample analysis. But it is limited for a single equipment reliability prediction. Therefore, the method of reliability prediction based on state space model (SSM) is proposed in this research. Feature energy of the monitored signals is extracted with the wavelet packet analysis and the associated frequency band energy with online monitored data. Then, degradation feature is improved by moving average filtering processing taken as input pair model parameter of SSM to be estimated. In the end, state space predicting model of degradation index is established. The probability density distribution of the degradation index is predicted, and the degree of reliability is calculated. A real testing example of bearing is used to demonstrate the rationality and effectiveness of this method. It is a useful method for single sample reliability prediction.

关键词: reliability prediction| state space model| feature extraction| wavelet analysis| moving average

CLC Number:

TB 114.3

LI Hong-kun1 (李宏坤), ZHANG Zhi-xin2* (张志新), LI Xiu-gang3 (李秀刚), REN Yuan-jie1 (任远杰). Reliability Prediction Method Based on State Space Model for Rolling Element Bearing[J]. Journal of shanghai Jiaotong University (Science), 2015, 20(3): 317-321.

References 10

[1]	Ding Feng, He Zheng-jia, Zi Yan-yang, et al. Reliability assessment based on equipment condition vibration feature using proportional hazard model [J]. Journal of Mechanical Engineering, 2009, 45(12): 89-94 (in Chinese).
[2]	He Zheng-jia, Cao Hong-rui, Zi Yan-yang, et al.Developments and thoughts on operational reliability assessment of mechanical equipment [J]. Journal of Mechanical Engineering, 2014, 50(2): 171-186 (in Chinese).
[3]	Orchard M E, Vachtsevanos G J. A particlefiltering approach for on-line fault diagnosis and failure prognosis [J]. Transactions of the Institute of Measurement and Control, 2009, 31(3-4): 221-246.
[4]	Gaˇsperin M, Juriˇci′c D, Boˇskoski P, et al. Modelbased prognostics of gear health using stochastic dynamical models [J]. Mechanical Systems and Signal Processing, 2011, 25(2): 537-548.
[5]	LU H T, Kolarik W J, Lu S S. Real-time performance reliability prediction [J]. IEEE Transactions on Reliability, 2001, 50(4): 353-357.
[6]	Xu Z G, Ji Y D, Zhou D H, et al. Real-time reliability prediction for a dynamic system based on the hidden degradation process identification [J]. IEEE Transactions on Reliability, 2008, 57(2), 230-242.
[7]	Hua C, Zhang Q, Xu G H, et al. Performance reliability estimation method based on adaptive failure threshold [J]. Mechanical Systems and Signal Processing,2013, 36(2): 505-519.
[8]	Pei Yi-xuan, Guo Ming. The fundamental principle and application of sliding average method [J]. Gun Launch & Control Journal, 2001, 1(1): 21-23 (in Chinese).
[9]	Digalakis V, Rohlicek J R, Ostendorf M. ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition [J].IEEE Transactions on Speech and Audio Processing,1993, 1(4): 431-442.
[10]	FEMTO-ST. IEEE PHM 2012 data challenge[EB/OL]. (2014-04-10). http://www.femto-st.fr/en/Research-departments/AS2M/Research-groups/PHM/IEEE-PHM-2012-Data-challenge.

[1]	ZHONG Yiming1, YU Caoyang1, 2, XIANG Xianbo2, 3, LIAN Lian1, 4. Motion Prediction of Marine Vehicles Based on Cascaded Filter and Error-Triggered Support Vector Regression [J]. Ocean Engineering Equipment and Technology, 2025, 12(1): 133-140.
[2]	BAI Zixuan ¹(白子轩), MA Yixin^1，2∗(马艺馨), KONG Zhibin³(孔志斌),XUE Shan⁴ (薛珊). Experimental Investigation of Possibility of Simultaneously Monitoring Lung Perfusion/Cardiomotility and Ventilation via Thoracic Impedance Measurement [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 81-90.
[3]	LIU Zengmin (刘增敏), WANG Shentao(王申涛), YAO Lixiu(姚莉秀), CAI Yunze(蔡云泽). Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 388-399.
[4]	NIU Guochen, SUN Xiangyu, YUAN Zhengyan. Vehicle-Road Collaborative Perception Method Based on Dual-Stream Feature Extraction [J]. Journal of Shanghai Jiao Tong University, 2024, 58(11): 1826-1834.
[5]	ZENG Zhirian(曾志贤),CAO Jianjun*(曹建军),WENG Nianfeng(翁年凤)，YUAN Zhen(袁震)，YU Xu(余旭). Cross-Modal Entity Resolution for Image and Text Integrating Global and Fine-Grained Joint Attention Mechanism [J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 728-737.
[6]	ZHAN Yan, CHEN Zhihui, ZHU Baochang, ZHU Tingting, SHAO Yiping, LU Jiansha. A Pallet Recognition Method Based on Adaptive Color Fast Point Feature Histogram [J]. Journal of Shanghai Jiao Tong University, 2023, 57(3): 297-308.
[7]	ZHANG Yanhe, ZANG Yuejin, CHEN Bo, XU Mingsheng. Radar HRRP Target Recognition Algorithm Based on Variational Auto-encoder with Disentangled Representation [J]. Air & Space Defense, 2022, 5(2): 87-93.
[8]	HUANG Jiayang (黄嘉阳), YANG Pengfei* (杨鹏飞), WAN Bo (万波), ZHANG Zhiqiang (张志强). KDLPCCA-Based Projection for Feature Extraction in SSVEP-Based Brain-Computer Interfaces [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(2): 168-175.
[9]	WANG Wei, WANG Bing, HU Xiong, SUN Dejian. Online Degradation Assessment of Shore Bridge Hoisting Gearbox Based on Improved Symbolic Sequence Entropy and Logistic Regression Model [J]. Journal of Shanghai Jiao Tong University, 2021, 55(10): 1272-1280.
[10]	WANG Jun, WANG Sai, REN Yuming, CHEN Dehong, CUI Shan, WEI Shaoming. Combining Denoising with Super Resolution for Target Detection and Recognition of SAR Image Based on Deep Learning [J]. Air & Space Defense, 2020, 3(3): 24-30.
[11]	ZHANG Tian,PAN Ershun. Degradation Modeling of Capacitors Based on Time Series Analysis [J]. Journal of Shanghai Jiaotong University, 2019, 53(11): 1316-1325.
[12]	SHANG Nuonuo, LIANG Yan, ZHAI Hengfeng, YUAN Haojuan. An Efficient Method of Full-pulse Feature Extraction for Radar Signals [J]. Air & Space Defense, 2018, 1(4): 37-43.
[13]	WANG Rui,LIU Bin,ZHOU Tianrun,YANG Yu. Vehicle Recognition in Acoustic and Seismic Networks via Collaboration Representation [J]. Journal of Shanghai Jiaotong University, 2018, 52(1): 103-110.
[14]	CUI Yan* (崔妍), CHEN Shijun (陈世均), QU Meng (瞿勐), HE Shanhong (何善红). Research and Application of New Threshold De-noising Algorithm for Monitoring Data Analysis in Nuclear Power Plant [J]. Journal of shanghai Jiaotong University (Science), 2017, 22(3): 355-360.
[15]	Khan M.; Cui L.. Memory based scheme to monitor non-random small shift patterns in manufacturing process [J]. J Shanghai Jiaotong Univ Sci, 2016, 21(4): 509-512.