A New Signal Processing and Feature Extraction Approach for Bearing Fault Diagnosis using AE Sensors

In this paper, a new signal processing and feature extraction approach for bearing fault diagnosis using acoustic emission (AE) sensors is presented. The presented approach uses time-frequency manifold analysis to extract time-frequency manifold features from AE signals. It reconstructs a manifold by embedding AE signals into a high-dimensional phase space. The tangent direction of the neighborhood for each point is then used to approximate its local geometry. The variation of the manifolds representing different condition states of the bearing can be revealed by performing multiway principal component analysis. AE signals acquired from a bearing test rig are used to validate the presented approach. The test results have shown that the presented approach can interpret different bearing conditions and is effective for bearing fault diagnosis.