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Volume 2019, Article ID 3264969, 17 pages
Research Article

Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery

1College of Electrical Engineering & New Energy, China Three Gorges University, Yichang, 443002, China
2Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang, 443002, China
3College of Electrical Engineering, Henan University of Technology, Zhengzhou, 450001, China

Correspondence should be addressed to Wenlong Fu; moc.621@gnolnewuf_ugtc

Received 14 January 2019; Accepted 27 March 2019; Published 17 April 2019

Academic Editor: Marcin Mrugalski

Copyright © 2019 Wenlong Fu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As a crucial and widely used component in industrial fields with great complexity, the health condition of rotating machinery is directly related to production efficiency and safety. Consequently, recognizing and diagnosing rotating machine faults remain to be one of the main concerns in preventing failures of mechanical systems, which can enhance the reliability and efficiency of mechanical systems. In this paper, a novel approach based on blind parameter identification of MAR model and mutation hybrid GWO-SCA optimization is proposed to diagnose faults for rotating machinery. Signals collected from different types of faults were firstly split into sets of intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the decomposing mode number K of which was preset with central frequency observation method. Then the multivariate autoregressive (MAR) model of all IMFs was established, whose order was determined by Schwartz Bayes Criterion (SBC), and all parameters of the model were identified blindly through QR decomposition, where key features were subsequently extracted via principal component analysis (PCA) to construct feature vectors of different fault types. Afterwards, a hybrid optimization algorithm combining mutation operator, grey wolf optimizer (GWO), and sine cosine algorithm (SCA), termed mutation hybrid GWO-SCA (MHGWOSCA), was proposed for parameter selection of support vector machine (SVM). The optimal SVM model was later employed to classify different fault samples. The engineering application and contrastive analysis indicate the availability and superiority of the proposed method.