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

• Published in: Shanghai jiao tong da xue xue bao Vol. 20; no. 3; pp. 317 - 321
• Main Author: 李宏坤 张志新 李秀刚 任远杰
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 01.06.2015
• Subjects: Architecture; Bearing; Computer Science; Degradation; Electrical Engineering; Engineering; Feature extraction; Filtering; Filtration; Frequency bands; Life Sciences; Materials Science; Mathematical models; State space models; reliability prediction; state space model; moving average; feature extraction; wavelet analysis; TB 114.3
• ISSN: 1007-1172; 1995-8188
• DOI: 10.1007/s12204-015-1629-4

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.