Abstract
Rolling element bearings are widely used in high-speed rotating machinery; thus proper monitoring and fault diagnosis procedure to avoid major machine failures is necessary. As feature extraction and classification based on vibration signals are important in condition monitoring technique, and superfluous features may degrade the classification performance, it is needed to extract independent features, so LSSVM (least square support vector machine) based on hybrid KICA-GDA (kernel independent component analysis-generalized discriminate analysis) is presented in this study. A new method named sensitive subband feature set design (SSFD) based on wavelet packet is also presented; using proposed variance differential spectrum method, the sensitive subbands are selected. Firstly, independent features are obtained by KICA; the feature redundancy is reduced. Secondly, feature dimension is reduced by GDA. Finally, the projected feature is classified by LSSVM. The whole paper aims to classify the feature vectors extracted from the time series and magnitude of spectral analysis and to discriminate the state of the rolling element bearings by virtue of multiclass LSSVM. Experimental results from two different fault-seeded bearing tests show good performance of the proposed method.
Highlights
Rotor machinery condition monitoring is gaining importance as the need to increase reliability and to decrease possible loss of production due to machine breakdown
Bearings serve as the best location for measuring machinery vibration since this is where the basic loads and forces of machine are applied [2]; bearing faults are common problems in high speed rotating machinery such as aero-engine, and bearing faults recognition has been an important research topic in pattern recognition over the last decade
Statistical data [3] shows that 90% of faults which occur in rolling bearings are due to cracks in inner and outer race; the rest are cracks in balls or cage
Summary
Rotor machinery condition monitoring is gaining importance as the need to increase reliability and to decrease possible loss of production due to machine breakdown. Hou et al extract feature based on genetic programming and linear discriminant analysis with aims to eliminate correlations between features and reduce dimensions of the high-dimensional data [8]. Almost all the traditional methods usually remove the redundant and irrelevant features and extract independent features from the original feature set to obtain better performance. It is worth conducting dimension reduction before further works. The sensitive feature design method is rarely developed The purpose of this investigation is to present a useful method for fault feature extraction of rolling bearing based on the KICA-GDA, and sensitive feature selection method is proposed here. We compare the effectiveness of LSSVM classifiers on the original and preprocessed data to examine the effectiveness of the proposed method
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