PCA and FDA Based Dimensionality Reduction Techniques for Effective Fault diagnosis of Rolling Element Bearing
نویسنده
چکیده
This paper uses Multi-Layer Perceptron Neural Network (MLPNN) for comparing the linear dimensionality reduction techniques (DRTs) for fault diagnosis in rolling element bearing (REB).The vibration signals from normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired under different radial loads and shaft speeds. These signals were subjected to wavelet based denoising technique, from which 17 statistical features have been extracted. Linear DRTs namely, principal component analysis (PCA) and Fisher’s discriminant analysis (FDA) have been used to select the sensitive features. The selected features have been evaluated using MLPNN. Finally a comparison of Linear DRTs based on MLPNN performance is presented. Keywords— Rolling element bearing, condition monitoring, Wavelet transform, PCA, FDA, MLPNN.
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