research-article

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

Authors:

Xiaoyuan Zhang,

Kai WangAuthors Info & Claims

Complexity, Volume 2019

https://doi.org/10.1155/2019/3264969

Published: 01 January 2019 Publication History

Abstract

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.

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Cited By

Wang XLuo LTang LYang Z(2021)Automatic representation and detection of fault bearings in in-wheel motors under variable load conditionsAdvanced Engineering Informatics10.1016/j.aei.2021.10132149:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.aei.2021.101321
Hu KJiang MZhang HCao SGuo Z(2021)Design of fault diagnosis algorithm for electric fan based on LSSVM and Kd-TreeApplied Intelligence10.1007/s10489-020-01830-051:2(804-818)Online publication date: 1-Feb-2021
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Fu WLu Q(2020)Multiobjective Optimal Control of FOPID Controller for Hydraulic Turbine Governing Systems Based on Reinforced Multiobjective Harris Hawks Optimization Coupling with Hybrid StrategiesComplexity10.1155/2020/92749802020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/9274980
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Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery

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cover image Complexity

Complexity Volume 2019, Issue

2019

8950 pages

ISSN:1076-2787

Issue’s Table of Contents

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.

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John Wiley & Sons, Inc.

United States

Publication History

Published: 01 January 2019

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Cited By

Wang XLuo LTang LYang Z(2021)Automatic representation and detection of fault bearings in in-wheel motors under variable load conditionsAdvanced Engineering Informatics10.1016/j.aei.2021.10132149:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.aei.2021.101321
Hu KJiang MZhang HCao SGuo Z(2021)Design of fault diagnosis algorithm for electric fan based on LSSVM and Kd-TreeApplied Intelligence10.1007/s10489-020-01830-051:2(804-818)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s10489-020-01830-0
Fu WLu Q(2020)Multiobjective Optimal Control of FOPID Controller for Hydraulic Turbine Governing Systems Based on Reinforced Multiobjective Harris Hawks Optimization Coupling with Hybrid StrategiesComplexity10.1155/2020/92749802020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/9274980
Xue XZhang NCao SJiang WZhou JLiu L(2020)New State Identification Method for Rotating Machinery under Variable Load Conditions Based on Hybrid Entropy Features and Joint Distribution AdaptationComplexity10.1155/2020/72471952020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/7247195
Fu WWang KTan JShao K(2020)Vibration Tendency Prediction Approach for Hydropower Generator Fused with Multiscale Dominant Ingredient Chaotic Analysis, Adaptive Mutation Grey Wolf Optimizer, and KELMComplexity10.1155/2020/45161322020Online publication date: 12-Feb-2020
https://dl.acm.org/doi/10.1155/2020/4516132
Jiang WZhou JXu YLiu JShan Y(2019)Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov ModelComplexity10.1155/2019/15768172019Online publication date: 31-Oct-2019
https://dl.acm.org/doi/10.1155/2019/1576817
Nezamivand Chegini SBagheri ANajafi F(2019)A new intelligent fault diagnosis method for bearing in different speeds based on the FDAF-score algorithm, binary particle swarm optimization, and support vector machineSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-019-04516-z24:13(10005-10023)Online publication date: 11-Nov-2019
https://dl.acm.org/doi/10.1007/s00500-019-04516-z

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