Bearing fault diagnosis using a novel coding-statistic feature combined with NNC

Abstract

The failures of rolling bearings usually cause the breakdown of rotating machinery. Therefore, bearing fault diagnosis is receiving more and more attentions. In this paper, a new coding-statistic feature is proposed for bearing fault diagnosis. Firstly, a waveform coding matrix (WCM) is drawn from each signal using a coding algorithm then a statistical feature is extracted from the WCM with a pre-defined dictionary. Secondly, all statistical features are processed using two-dimensional principal component analysis (2DPCA) to reduce redundant information and dimensionality. Finally, a nearest neighbor classifier (NNC) is employed to classify the bearing faults. Two bearing fault classification problems are utilized to demonstrate the effectiveness of the proposed scheme. Experimental results show that an excellent performance could be accomplished with the proposed scheme.

Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

“…Moreover, 2DPCA generally outperforms PCA in image reconstruction and recognition [13]. Due to its simple formulation and superior performance, 2DPCA has been successfully applied in various fields such as image retrieval [14,15], fault detection [16], iris recognition [17], target recognition based on synthetic aperture radar images [18], bearing fault diagnosis [19], brain tumor diagnosis [20], etc.…”