A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis

Rotatingmachinery is the main component of mechanical equipment. Nevertheless, due to variation of operating condition results in important detection performance deterioration. Therefore, fault detection and diagnosis of rotating machines is very critical for the reliable operation. In this paper, a novel classification technique is employed for fault detection of rotating machines based on kernelled support tensor machine (KSTM) and multilinear principal component analysis (MPCA). The vibration signal is firstly formulated as a 3-way tensor using trial, condition and channel. In order to process the rotating machines faults and identify the information classes in tensor space, the KSTM is then introduced from sets of binary support tensor machine classifiers by the one-against-one parallel strategy. The MPCA is utilized for reduction dimensionality of the high-dimensional signature space and reservation the tensorial structure information. The performance of the developed technique in classification faults of rotating machinery has been thoroughly evaluated through collecting signals on bearing and gear test-rigs. Experimental results showed that the proposed method can achieve the highest classification results among the six classification techniques investigated in this study.

Automated summary

A novel classification technique utilizing kernelled support tensor machine (KSTM) and multilinear principal component analysis (MPCA) is introduced for fault detection of rotating machines. Through the use of a 3-way tensor, KSTM, and MPCA, the technique effectively handles fault diagnosis and information classification in rotating machinery.