Fault Diagnosis of Rolling Bearing Based on the PSO-SVM of the Mixed-Feature

2013 ◽  
Vol 380-384 ◽  
pp. 895-901 ◽  
Author(s):  
Jun Ma ◽  
Jian De Wu ◽  
Yu Gang Fan ◽  
Xiao Dong Wang ◽  
Zong Kai Shao
Keyword(s):  
Fault Diagnosis ◽  
Kernel Function ◽  
Optimal Parameter ◽  
Monitoring Program ◽  
Rolling Bearing ◽  
Pso Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Swarm Optimization ◽  
Diagnosis Method

The rolling bearing is one of the most important and widely used parts in the rotating machinery. It is necessary to establish a reliable condition monitoring program which can avoid serious fault in the runtime and diagnose failure timely and accurately when it happens. This paper puts forward to a fault diagnosis method of rolling bearing based on the PSO-SVM of the mixed-feature. Firstly, we extract features in time domain, frequency domain, and order quenfrency domain. Secondly, select both Support Vector Machine (SVM) parameters by Particle Swarm Optimization (PSO) algorithm and kernel function of SVM classification model. Finally, classification model of SVM is designed by using the extracted salient features, kernel function and optimal parameter of PSO. The result verifies the effectiveness of the proposed method.

scholarly journals A rolling bearing fault diagnosis method based on LSSVM

2020 ◽  
Vol 12 (1) ◽  
pp. 168781401989956
Author(s):  
Xuejin Gao ◽  
Hongfei Wei ◽  
Tianyao Li ◽  
Guanglu Yang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Kernel Function ◽  
Dimensional Space ◽  
Rolling Bearing ◽  
Classification Model ◽  
Support Vector ◽  
Bearing Fault ◽  
Diagnosis Method

The fault characteristic signals of rolling bearings are coupled with each other, thus increasing the difficulty in identifying the fault characteristics. In this study, a fault diagnosis method of rolling bearing based on least squares support vector machine is proposed. First, least squares support vector machine model is trained with the samples of known classes. Least squares support vector machine algorithm involves the selection of a kernel function. The complexity of samples in high-dimensional space can be adjusted through changing the parameters of kernel function, thus affecting the search for the optimal function as well as final classification results. Particle swarm optimization and 10-fold cross-validation method are adopted to optimize the parameters in the training model. Then, with the optimized parameters, the classification model is updated. Finally, with the feature vector of the test samples as the input of least squares support vector machine, the pattern recognition of the testing samples is performed to achieve the purpose of fault diagnosis. The actual bearing fault data are analyzed with the diagnosis method. This method allows the accurate classification results and fast diagnosis and can be applied in the diagnosis of compound faults of rolling bearing.

scholarly journals A Fault Diagnosis Method of Rolling Bearing Integrated with Cooperative Energy Feature Extraction and Improved Least-Squares Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhang Xu ◽  
Darong Huang ◽  
Tang Min ◽  
Yunhui Ou
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Support Vector ◽  
Swarm Optimization ◽  
Energy Characteristics ◽  
Wavelet Energy ◽  
Energy Feature

To solve the problem that the bearing fault of variable working conditions is challenging to identify and classify in the industrial field, this paper proposes a new method based on optimization of multidimension fault energy characteristics and integrates with an improved least-squares support vector machine (LSSVM). First, because the traditional wavelet energy feature is difficult to effectively reflect the characteristics of rolling bearing under different working conditions, based on analyzing the wavelet energy feature extraction in detail, a collaborative method of multidimension fault energy feature extraction combined with the method of Transfer Component Analysis (TCA) is constructed, which improves the discrimination between the different features and the compactness between the same features of rolling bearing faults. Then, for solving the problem of the local optimal of particle swarm optimization (PSO) in fault diagnosis and recognition of rolling bearing, an improved LSSVM based on particle swarm optimization and wavelet mutation optimization is established to realize the collaborative optimization and adjustment of LSSVM dynamic parameters. Based on the improved LSSVM and optimization of multidimensional energy characteristics, a new method for fault diagnosis of rolling bearing is designed. Finally, the simulation and analysis of the proposed algorithm are verified by the experimental data of different working conditions. The experimental results show that this method can effectively extract the multidimensional fault characteristics under variable working conditions and has a high fault recognition rate.

scholarly journals Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 359 ◽  
Author(s):  
Jianghua Ge ◽  
Guibin Yin ◽  
Yaping Wang ◽  
Di Xu ◽  
Fen Wei
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Rolling Bearing ◽  
Support Vector ◽  
Distance Information ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Feature Evaluation ◽  
Diagnosis Method ◽  
Diagnosis Model

To improve the accuracy of rolling-bearing fault diagnosis and solve the problem of incomplete information about the feature-evaluation method of the single-measurement model, this paper combines the advantages of various measurement models and proposes a fault-diagnosis method based on multi-measurement hybrid-feature evaluation. In this study, an original feature set was first obtained through analyzing a collected vibration signal. The feature set included time- and frequency-domain features, and also, based on the empirical-mode decomposition (EMD)-obtained time-frequency domain, energy and Lempel–Ziv complexity features. Second, a feature-evaluation framework of multiplicative hybrid models was constructed based on correlation, distance, information, and other measures. The framework was used to rank features and obtain rank weights. Then the weights were multiplied by the features to obtain a new feature set. Finally, the fault-feature set was used as the input of the category-divergence fault-diagnosis model based on kernel principal component analysis (KPCA), and the fault-diagnosis model was based on a support vector machine (SVM). The clustering effect of different fault categories was more obvious and classification accuracy was improved.

scholarly journals Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 404 ◽  
Author(s):  
Wenlong Fu ◽  
Jiawen Tan ◽  
Yanhe Xu ◽  
Kai Wang ◽  
Tie Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Production Efficiency ◽  
Particle Swarm ◽  
Support Vector ◽  
Rolling Bearings ◽  
Swarm Optimization ◽  
Svm Model ◽  
Diagnosis Method ◽  
Sine Cosine Algorithm

Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings.

CREDIT SCORING USING MULTI-KERNEL SUPPORT VECTOR MACHINE AND CHAOS PARTICLE SWARM OPTIMIZATION

2012 ◽  
Vol 11 (03) ◽  
pp. 1250019 ◽  
Author(s):  
YUN LING ◽  
QIUYAN CAO ◽  
HUA ZHANG
Keyword(s):  
Particle Swarm Optimization ◽  
Kernel Function ◽  
Credit Scoring ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Classification Problem ◽  
Classification Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Swarm Optimization

Consumer credit scoring is considered as a crucial issue in the credit industry. SVM has been successfully utilized for classification in many areas including credit scoring. Kernel function is vital when applying SVM to classification problem for enhancing the prediction performance. Currently, most of kernel functions used in SVM are single kernel functions such as the radial basis function (RBF) which has been widely used. On the basis of the existing kernel functions, this paper proposes a multi-kernel function to improve the learning and generalization ability of SVM by integrating several single kernel functions. Chaos particle swarm optimization (CPSO) which is a kind of improved PSO algorithm is utilized to optimize parameters and to select features simultaneously. Two UCI credit data sets are used as the experimental data to evaluate the classification performance of the proposed method.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM

2010 ◽  
Vol 121-122 ◽  
pp. 813-818 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Fuzzy Support Vector Machine ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

Intelligent Fault Diagnosis Method Based on Vector-Bispectrum and SVDD

2012 ◽  
Vol 490-495 ◽  
pp. 1029-1033 ◽  
Author(s):  
Ling Jun Li ◽  
Wen Ping Lei ◽  
Jie Han ◽  
Wang Shen Hao
Keyword(s):  
Fault Diagnosis ◽  
Field Vector ◽  
Classification Model ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Nonlinear Properties ◽  
Channel Information ◽  
Diagnosis Method ◽  
Set Up

Support vector data description (SVDD) can be used to solve the problems of the insufficient fault samples in the fault diagnosis field. Vector-bispectrum is the bispectrum analysis method based on the full vector spectrum information fusion. It can be used to fuse the double-channel information of the rotary machines effectively and reflect the nonlinear properties in the signals more completely and accurately. In order to realize the aim that the faults of the machines can be diagnosed effectually and intelligently under the situation of the lack of the fault samples, the intelligent diagnosis method of the faults by combining the vector-bispectrum with SVDD is put forward. By using the vector-bispectrum to process the signals and extract the characteristic vectors, which can be used as the input parameters of SVDD. The classification model is set up and therefore the running states of the machines can also be classified. The method is applied to the gearbox fault diagnosis. The results indicate that the method can be effectively used to extract the characteristic information of the gearbox signals and increase the accuracy of SVDD in the fault diagnosis.

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