Regrouping Particle Swarm Optimization-Based Neural Network for Bearing Fault Diagnosis

Bearing is one of the key components of a rotating machine. Hence, monitoring health condition of the bearing is of paramount importace. This paper develops a novel particle swarm optimization (PSO)-least squares wavelet support vector machine (PSO-LSWSVM) classifier, which is designed based on a combination between a PSO, a least squares procedure, and a new wavelet kernel function-based support vector machine (SVM), for bearing fault diagnosis. In this work, bearing fault classification is transformed into a pattern recognition problem, which consists of three stages of data processing. Firstly, a rich information dataset is built by extracting the features from the signals, which are decomposed by the nonlocal means (NLM) and empirical mode decomposition (EMD). Secondly, a minimum-redundancy maximum-relevance (mRMR) method is employed to determine a subset of feature that can provide an optimal performance. Thirdly, a novel classifier, namely LSWSVM, is proposed with the aid of a PSO, to provide higher classification accuracy. The key innovative science of this work is to propropose a new classifier with the aid of an new wavelet kernel type to increase the classification precision of bearing fault diagnosis. The merit features of the proposed approach are demonstrated based on a benchmark bearing dataset and a comprehensive comparison procedure.

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Bearing fault diagnosis based on improved particle swarm optimized VMD and SVM models

Advances in Mechanical Engineering ◽

10.1177/16878140211028451 ◽

2021 ◽

Vol 13 (6) ◽

pp. 168781402110284

Author(s):

Qingfeng Zhang ◽

Shuang Chen ◽

Zhan Peng Fan

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Particle Swarm ◽

Vibration Signal ◽

Support Vector ◽

Data Set ◽

Swarm Optimization ◽

Bearing Fault ◽

Bearing Fault Diagnosis

To improve the accuracy of fault diagnosis of bearing, the improved particle swarm optimization variational mode decomposition (VMD) and support vector machine (SVM) models are proposed. Aiming at the convergence effect of particle swarm optimization (PSO), dynamic inertia weight, and gradient information are introduced to improve PSO (IPSO). IPSO is used to optimize the optimal number of VMD modal components and the penalty factor, which is applied to the vibration signal decomposition. The fault sample set is constructed by calculating the multi-scale information entropy of each component signal obtained from the bearing vibration signals. At the same time, IPSO is used to optimize the support vector machine (IPSO-SVM), which is used to bearing fault diagnosis. The time-domain feature data set is used as the comparison data set, and the classical PSO, genetic algorithm, and cross-validation method are used as the comparison algorithm to verify the effectiveness of the method in this paper. The research results show that the optimized VMD can effectively decompose the vibration signal and can effectively highlight the fault characteristics. IPSO can increase the accuracy by 2% without adding additional costs. And the accuracy, volatility, and convergence error of IPSO are better than comparison algorithms.

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Quantitative Analysis for SF6 and its Compositions in GIS

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.562-564.1336 ◽

2012 ◽

Vol 562-564 ◽

pp. 1336-1339

Author(s):

Hai Lun Wang ◽

Jian Wei Shen

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Quantitative Analysis ◽

Fault Diagnosis ◽

Traditional Method ◽

Particle Swarm ◽

Prediction Errors ◽

Swarm Optimization ◽

Training Analysis ◽

Derivatives Of

In this paper, a method for GIS equipment fault diagnosis by the analysis of volume fractions of the derivatives of SF6 gas inside GIS equipment is presented. For the method, based on the differential spectra method, a neural network model and the particle swarm optimization are used for training analysis of infrared spectra, to realize the quantitative analysis of specific derivatives. The experimental results show that the prediction errors obtained by particle swarm optimization training are markedly superior to prediction errors obtained using the traditional method.

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Power Transform Fault Diagnosis Based on the Adaptive Mutation Particle Swarm Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3804 ◽

2013 ◽

Vol 756-759 ◽

pp. 3804-3808

Author(s):

Zhi Mei Duan ◽

Jia Tang Cheng

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Power Transformer ◽

Recognition Rate ◽

Particle Swarm ◽

Adaptive Mutation ◽

Bp Network ◽

Swarm Optimization ◽

Fault Recognition

In order to improve the accuracy of fault diagnosis of power transformer, in this paper, a method is proposed that optimize the weight of BP neural network by adaptive mutation particle swarm optimization (AMPSO). According to the characteristic of transformer fault, the optimized neural network is used to diagnose fault of the power transformer. Individual particles action is amended by this algorithm and local minima problems of the standard PSO and BP network are overcooked. The experimental results show that, the method can classify transformer faults, and effectively improve the fault recognition rate.

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