Extracting invariable fault features of rotating machines with multi-ICA networks |
| |
| Authors: | Jiao Wei-dong Yang Shi-xi Wu Zhao-tong |
| |
| Institution: | (1) Department of Mechanical Engineering, Zhejiang University, 310027 Hangzhou, China |
| |
| Abstract: | This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault
modes. By the use of ICA, invariable features embedded in multi-channel vibration measurements under different operating conditions
(rotating speed and/or load) can be captured together. Thus, stable MLP classifiers insensitive to the variation of operation
conditions are constructed. The successful results achieved by selected experiments indicate great potential of ICA in health
condition monitoring of rotating machines.
Project supported by the National Natural Science Foundation (No. 50205025) and the Natural Science Foundation of Zhejiang
Province (No. 5001004). |
| |
| Keywords: | Independent Component Analysis (ICA) Mutual Information (MI) Principal Component Analysis (PCA) Multi-Layer Perceptron (MLP) Residual Total Correlation (RTC) |
| 本文献已被 万方数据 SpringerLink 等数据库收录！ |
