Rolling bearing fault diagnosis and performance degradation assessment under variable operation conditions based on nuisance attribute projection

Abstract

Abstract The operation conditions are always non-stationary in the operation of rotating machinery. It is the key to realize the fault diagnosis of rotating machinery under different operation conditions by extracting the features which are irrelevant to the operation conditions and contain fault information. Nuisance attribute projection (NAP) is compensation technique to eliminate the influence of interference information in the feature space which has been used in speaker recognition and face recognition. In this paper, the rolling bearing vibration signals under different operation conditions are used to verify the nuisance removing ability of (NAP) in the feature space. Above all, after NAP are applied to the simulation and measured signals under different rotating speeds and loads, the comparisons between the original features and NAP features have shown that NAP can effectively eliminate the effect of nuisance attributes through projection. Moreover, it is verified that the information of fault pattern and fault degree are retained in the features after projection by analyzing the measured signals of different fault pattern and simulation signals of different fault degree under various rotating speeds, respectively. Furthermore, it is confirmed that the NAP can get rid of the nuisance attributes by analyzing twelve bearing tests of whole life under different operation conditions and channels. On the other hand, a feature selection method based on ranking mutual information (RMI) is used in this paper to select the features of more monotonous in bearing performance degradation assessment. The application sequences of RMI and NAP are studied for their influence on the bearing performance degradation index in the last part since the different sequence may affect the assessment results. The comparison results have shown that NAP should be used before feature selection in rotating machinery fault diagnosis.