Ball Bearing Fault Diagnosis Using Wavelet Transform and Principal Component Analysis

This study proposes a new method for fault diagnosis in ball bearings based on wavelet transform and principal component analysis (PCA) of the acquired vibration signals. The signals collected are pre-processed using a wavelet transform to decompose the signals into low (approximated) and high (detailed) frequency part where the high-frequency part are needed for fault diagnosis purposes. Eleven potential statistical features are then extracted from the high-frequency part coming from different bearing fault signals and those from healthy bearings as well. Four types of signals are proposed, they are outer race fault, inner race fault, ball fault and no-fault signals. The PCA is used to linearly transform and reduce multidimensional data resulted from statistical extraction down to a few dimensions for more straightforward analysis. Six principal components retaining more than 95% significance level are used for bearing fault detection and classification. By combining the wavelet transform, statistical features extraction and PCA, the proposed method successfully detected and classified fault types without knowledge of a bearing fault frequencies and analysis from experienced users.