Research on rotor system fault diagnosis method based on vibration signal feature vector transfer learning. (September 2022)

• Record Type: Journal Article
• Authors: Wang, Shuai; Wang, Qingfeng; Xiao, Yang; Liu, Wencai; Shang, Minghu
• Is Part Of: Engineering failure analysis. Volume 139(2022)
• Publication Date: 2022-09
• Subjects: Fault diagnosis -- Multi-dimensional sensitive features -- Online feature transfer learning -- ReliefF -- Rotor system; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106424
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: A rotor system fault diagnosis model based on vibration signal feature vector transfer learning is proposed. The model was trained and verified using real fault data sets from four different machines which work under different operation conditions. In this paper, the intelligent diagnosis of rotor imbalance, misalignment, rubbing and oil whirl is realized for the first time in the rotor system. Compared with other methods, the diagnosis accuracy rate is higher. Abstract: Aiming at the common fault diagnosis problems of rotors in industrial applications. A rotor system fault diagnosis method based on vibration signal feature vector transfer learning is proposed. First, Statistical methods and wavelet packet decomposition are used for vibration signal feature extraction. Then, the ReliefF algorithm is used to evaluate the fault features and screen out the sensitive fault features set. Next, the training data and real time test data are mapped to the kernel Hilbert space using the transfer component analysis method. Finally, the weighted k-nearest neighbor method is used as the fault feature classifier for fault pattern recognition. Model training and validation using typical failure datasets of different equipment and different operating conditions. Compared with other related methods, the results indicate that the proposed method has better generalization and diagnostic accuracy. This research will promote the engineering application of intelligent fault diagnosis of rotor system.