Efficient bearing fault diagnosis by extracting intrinsic fault information using envelope power spectrum

Early and efficient fault diagnosis of bearing of industrial motor is a modern demand for reducing unexpected breakdown of industrial process. Extracting the intrinsic fault signature in very early stage is important. In this point of view, this paper proposes a fault diagnosis model of industrial bearing including efficient fault signature extraction technique based on narrow band frequency domain analysis of acoustic emission (AE) signal using envelope power spectrum. To do that, AE signals are collected from defective and non-defective bearings under different rotational speeds from industrial-like experimental environment. Envelope power spectrum is calculated from the AE signal and narrow band root mean square (NBRMS) fault features are extracted from defect frequency ranges of the envelope power spectrum. Finally, the k-nearest neighbor (k-NN) classification algorithm is used for identifying the fault of unknown signal and validating the efficiency of the proposed feature extraction model. The experimental result shows that the proposed model outperforms state-of-art algorithms in terms of classification accuracy.