Intelligent Fault Diagnosis of Multichannel Motor-Rotor System Based on Multimanifold Deep Extreme Learning Machine

• Published in: IEEE/ASME transactions on mechatronics Vol. 25; no. 5; pp. 2177 - 2187
• Main Authors: Zhao, Xiaoli, Jia, Minping, Ding, Peng, Yang, Chen, She, Daoming, Liu, Zheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Data integration; Data mining; Fault diagnosis; Feature extraction; IEEE transactions; information fusion; intraclass and interclass information; Machine learning; Manifolds; Mechatronics; Military helicopters; motor–rotor system; multimanifold deep extreme learning machine (MDELM); Rotors
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2020.3004589

Nowadays, the measurement technology of multichannel information fusion provides a solid research foundation for digital and intelligent fault diagnosis of mechatronics equipment. To implement the rapid fusion of multichannel data and intelligent diagnosis, a new fault diagnosis method for multichannel motor-rotor system via multimanifold deep extreme learning machine (MDELM) algorithm is first proposed in this article. Specifically, the designed MDELM algorithm is divided into two main components: 1) unsupervised self-taught feature extraction via the designed extreme learning machine based-modified sparse filtering feature extractor; 2) semisupervised fault classification via the designed MELM classifier with multimanifold constraints to mine the intraclass and interclass discriminant feature information. Experimental and industrial data from motor-rotor system demonstrates the superiority of the proposed method and algorithms. Compared with other fault diagnosis methods, the proposed MDELM algorithm has better learning efficiency, and it is more suitable for intelligent diagnosis of multichannel data fusion.