Based on machine learning techniques, this paper presents a novel intelligent fault diagnosis method, which is an integrated framework concerning reconstruction independent component analysis (RICA) and multiclass relevance vector machine (MRVM). In this method, the RICA is first used to automatically extract features from raw vibration signals. Then, the learned features are used as the input data of MRVM for the classification of different health conditions of machines. The proposed method is applied to the fault diagnosis of locomotive rolling bearings. According to the diagnosis results, it is verified that the proposed method is able to reliably classify different health conditions. By comparing with diagnosis method based on time-domain statistical analysis and wavelet transformation, the proposed method shows its superiority in automatic features extraction from raw signals.
['Yaguo Lei', 'Shan H', 'Feng Jia', 'Jing Lin']
Reconstruction independent component analysis-based methods for intelligent fault diagnosis