Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding

• Published in: Measurement : journal of the International Measurement Confederation Vol. 176; p. 109116
• Main Authors: Chen, Xu, Qi, Xiaoli, Wang, Zhenya, Cui, Chuangchuang, Wu, Baolin, Yang, Yan
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.05.2021; Elsevier Science Ltd
• Subjects: Algorithms; Ball bearings; Bearings; Embedding; Fault diagnosis; Fault tolerance; Feature extraction; Machine learning; Marine predators algorithm-based optimization support vector machine; Predators; Refined composite multiscale fuzzy entropy; Roller bearings; Rotating machinery; Self alignment; Signal processing; Support vector machines; Topology; Topology learning and out-of-sample embedding; ABC; GA-SVM; SVM; Isomap; LPP; DR; SA; Fault diagnosis; Refined composite multiscale fuzzy entropy; Topology learning and out-of-sample embedding; MPA-SVM; RCMFE; GA; AFSA-SVM; Marine predators algorithm-based optimization support vector machine; SS-Isomap; SSAE; ABC-SVM; SA -SVM; TLOE; PSO-SVM; AFSA; PSO; LTSA; MPA
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2021.109116

The long-term safe operation of rotating machinery is closely related to the stability of rolling bearings. This paper proposes a rolling bearing fault diagnosis method based on refined composite multiscale fuzzy entropy (RCMFE), topology learning and out-of-sample embedding (TLOE), and the marine predators algorithm based-support vector machine (MPA-SVM). First, the RCMFE algorithm is used to extract the features of the original rolling bearing fault signal and to construct the original high-dimensional fault feature set. Then, TLOE is used to reduce the dimensionality of the high-dimensional fault feature set. The low-dimensional sensitive fault features are extracted to construct a low-dimensional fault feature subset. Finally, fault-type discrimination is performed using the MPA-SVM. The Case Western Reserve University dataset and data from fault diagnosis experiments performed on 1210 self-aligning ball bearings were used to verify the proposed method. The results demonstrate the effectiveness of the fault diagnosis method, which can diagnose bearing faults with up to 100% accuracy.