Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging

被引:39
作者
Li, Xin [1 ]
Li, Yong [1 ]
Yan, Ke [2 ]
Shao, Haidong [3 ]
Lin, Janet [4 ,5 ]
机构
[1] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Peoples R China
[2] Natl Univ Singapore, Coll Design & Engn, Dept Built Environm, 4 Architecture Dr, Singapore 117566, Singapore
[3] Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Peoples R China
[4] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden
[5] Malardalen Univ, Sch Innovat Design & Engn, S-63220 Eskilstuna, Sweden
基金
中国国家自然科学基金;
关键词
Intelligent fault diagnosis; Support matrix machine; Probability output strategy; Semi-supervised learning; Infrared imaging;
D O I
10.1016/j.ress.2022.108921
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Fault diagnosis is of great significance to ensure the reliability and safety of complex bevel gearbox systems. Most existing intelligent fault diagnosis approaches of bevel gearboxes are designed with vibration monitoring. However, the collected vibration data are vulnerable to noise pollution and machinery operating conditions. Besides, traditional fault diagnosis models highly rely on numerous labeled samples, and neglect the high cost of label annotation in real-world applications. Therefore, a novel fault diagnosis approach based on semi-supervised probability support matrix machine (SPSMM) and infrared imaging is proposed for bevel gearboxes in this paper, which has the following properties. Firstly, SPSMM classifies 2D matrix data directly without vectorization, thus fully utilizing the spatial information in infrared images. Secondly, a probability output strategy is designed for SPSMM to calculate the posterior class probability estimation of matrix inputs, and consequently enhance the diagnostic accuracy and robustness of the model. Thirdly, a semi-supervised learning (SSL) framework is pro-posed for SPSMM to carry out sample transfer from the unlabeled sample pool to the labeled sample pool, which can effectively alleviate the problem of insufficient labeled samples. The superiority of the proposed diagnosis approach is demonstrated with an infrared imaging dataset of a bevel gearbox.
引用
收藏
页数:11
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