HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction
| Autor: | Liubin Li, Fang Liu, Zheng Cao, Siliang Lu, Yongbin Liu, Hui Yang |
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| Rok vydání: | 2019 |
| Předmět: |
SVR
Computer science 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Blind signal separation Signal Article Analytical Chemistry law.invention performance degradation 010309 optics Kernel (linear algebra) law 0103 physical sciences 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Extreme learning machine Bearing (mechanical) hybrid kernel function Atomic and Molecular Physics and Optics Vibration Nonlinear system rolling bearing Kernel method Kernel (statistics) krill herd algorithm 020201 artificial intelligence & image processing Hybrid kernel Algorithm |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 3, p 660 (2020) Sensors Volume 20 Issue 3 |
| ISSN: | 1424-8220 |
| Popis: | In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF&ndash SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF&ndash SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF&ndash SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings. |
| Databáze: | OpenAIRE |
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