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

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Abstract

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.

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Acknowledgments

The financial sponsorship from the project of National Natural Science Foundation of China (51875032, 51965013) and Doctoral Research Foundation (UF20027Y).

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, 541004, China

    Chaofan Hu & Shuilong He

  2. Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Yanxue Wang

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  1. Chaofan Hu
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  2. Shuilong He
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Correspondence to Yanxue Wang.

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Hu, C., He, S. & Wang, Y. A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis. Appl Intell 51, 2609–2621 (2021). https://doi.org/10.1007/s10489-020-02011-9

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  • DOI: https://doi.org/10.1007/s10489-020-02011-9

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