scholarly journals Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 527
Author(s):  
Huibin Shi ◽  
Wenlong Fu ◽  
Bailin Li ◽  
Kaixuan Shao ◽  
Duanhao Yang
Keyword(s):  
Feature Extraction ◽  
Normal Operation ◽  
Fault Identification ◽  
Support Vector ◽  
Mechanical Equipment ◽  
Rolling Bearings ◽  
Swarm Optimization ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Factor C

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.

scholarly journals Bearing Fault Identification Based on Deep Convolution Residual Network

Mechanika ◽  
2021 ◽  
Vol 27 (3) ◽  
pp. 229-236
Author(s):  
Tong ZHOU ◽  
Yuan LI ◽  
Yijia JING ◽  
Yifei TONG
Keyword(s):  
Feature Extraction ◽  
Deep Neural Networks ◽  
High Accuracy ◽  
Normal Operation ◽  
Fault Identification ◽  
Mechanical Equipment ◽  
Residual Network ◽  
Bearing Fault ◽  
Fitting Ability ◽  
Very High

Bearings are important parts in industrial production and are related to the normal operation of mechanical equipment. For bearing fault identification， traditional method often includes feature extraction, which involves professional prior knowledge and is time-consuming. This paper proposes the deep convolution residual network (DCRN) to identify the bearing fault. Based on the end-to-end learning characteristics of deep neural networks, this method can directly use raw data for training, and does not require feature extraction. Moreover, under the effect of skip connection, DCRN can exert the powerful fitting ability of deep neural network. In this paper, by stacking residual blocks, three different architecture of DCRN are designed and all three achieve very high accuracy, respectively 99.60%, 99.71% and 99.81%. Compared with other methods, DCRN have better generalization performance.

scholarly journals Heating load prediction based on particle swarm optimization support vector machine

2021 ◽  
Vol 2087 (1) ◽  
pp. 012058
Author(s):  
Xiuchao Chen ◽  
Shenghui Wang ◽  
Xing Jin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Training Model ◽  
Function Parameter ◽  
Support Vector ◽  
Load Prediction ◽  
Swarm Optimization ◽  
Penalty Factor ◽  
Heating Load ◽  
Factor C

Abstract Heating load is affected by many uncertain factors, which makes it show certain randomness. To further improve the heating load forecasting accuracy, reduce the prediction error, using cross validation (CV) ideology in the choice of a model of performance evaluation and the superiority, combined with the advantages of particle swarm optimization (PSO), which is easy to implement and has stronger global optimization ability, the important parameters (penalty factor C and RBF kernel function parameter γ) are optimized, and the best parameters are automatically found in the training set, so as to obtain the best training model. Compared with other algorithms, the model precision of this method is improved a lot, and the prediction result is more accurate.

scholarly journals Cell Group Recognition Method Based on Adaptive Mutation PSO-SVM

Cells ◽  
2018 ◽  
Vol 7 (9) ◽  
pp. 135 ◽  
Author(s):  
Yue Wang ◽  
Xiaochen Meng ◽  
Lianqing Zhu
Keyword(s):  
Group Identification ◽  
Pso Algorithm ◽  
Multidimensional Data ◽  
Cell Group ◽  
Adaptive Mutation ◽  
Support Vector ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Svm Model ◽  
Factor C

The increased volume and complexity of flow cytometry (FCM) data resulting from the increased throughput greatly boosts the demand for reliable statistical methods for the analysis of multidimensional data. The Support Vector Machines (SVM) model can be used for classification recognition. However, the selection of penalty factor c and kernel parameter g in the model has a great influence on the correctness of clustering. To solve the problem of parameter optimization of the SVM model, a support vector machine algorithm of particle swarm optimization (PSO-SVM) based on adaptive mutation is proposed. Firstly, a large number of FCM data were used to carry out the experiment, and the kernel function adapted to the sample data was selected. Then the PSO algorithm of adaptive mutation was used to optimize the parameters of the SVM classifier. Finally, the cell clustering results were obtained. The method greatly improves the clustering correctness of traditional SVM. That also overcomes the shortcomings of PSO algorithm, which is easy to fall into local optimum in the iterative optimization process and has poor convergence effect in dealing with a large number of data. Compared with the traditional SVM algorithm, the experimental results show that, the correctness of the method is improved by 19.38%. Compared with the cross-validation algorithm and the PSO algorithm, the adaptive mutation PSO algorithm can also improve the correctness of FCM data clustering. The correctness of the algorithm can reach 99.79% and the time complexity is relatively lower. At the same time, the method does not need manual intervention, which promotes the research of cell group identification in biomedical detection technology.

Fault Diagnosis of Feedwater Pump in Nuclear Power Plants Using Parameter-Optimized Support Vector Machine

2016 ◽  
Author(s):  
Rui Yu ◽  
Xianling Li ◽  
Mo Tao ◽  
Zhiwu Ke
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Nuclear Power ◽  
Power Plants ◽  
Superior Performance ◽  
Support Vector ◽  
Feedwater Pump ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Factor C

The condition monitoring of the feedwater pump in secondary circuit is critical to the safe operation of the nuclear power plant. This article presents a fault diagnosis method of feedwater pump by using parameter-optimized support vector machine (SVM). While the fault features of feedwater pump are reflected from the power spectrum of the vibration signals, we trained and diagnosed the fault feature table with support vector machine. The optimal penalty factor C and kernel parameter γ of support vector machine are selected by grid search and k-fold cross validation. Then the faults are diagnosed by the SVM model under the optimal parameters. Diagnostic results show that the parameter-optimized SVM method achieves higher diagnostic accuracy than the PNN method, exhibiting superior performance to effectively diagnose the faults of feedwater pump.

scholarly journals Parameter-identification investigations on the hysteretic Preisach model improved by the fuzzy least square support vector machine based on adaptive variable chaos immune algorithm

2017 ◽  
Vol 36 (3) ◽  
pp. 227-242 ◽  
Author(s):  
E Jiaqiang ◽  
Cheng Qian ◽  
Hao Zhu ◽  
Qingguo Peng ◽  
Wei Zuo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Immune Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Preisach Model ◽  
Model Based ◽  
Weight Factors ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Factor C

In order to solve the hysteretic character of the piezoelectric material for application, the initial weight factors of the hysteretic units are calculated by the Preisach theory and the first-order reversal curves test data, a hysteretic Preisach model based on the improved fuzzy least square support vector machine (improved FLS-SVM) is established. In the established model, the fuzzy least square support vector machine is introduced to calculate more weight factors of the hysteretic units and the adaptive variable chaos immune algorithm is introduced to optimize the penalty factor and the kernel parameter of the FLS-SVM (the penalty factor c = 35 and the kernel parameter σ = 1.35 are obtained). Moreover, the quadratic polynomial interpolation method is used to eliminate the sawtooth phenomenon. The validity of established model reveals that fuzzy least square support vector machine method based on adaptive variable chaos immune algorithm (FLS-SVMAVCIA) is more accurate than FLS-SVM method according to application results of the real actuators (the absolute mean error of the FLS-SVMAVCIA model is less than 1 µm and its maximum error is less than 2 µm). As a result, the hysteretic phenomenon can be effectively eliminated by the hysteretic Preisach model based on the FLS-SVMAVCIA method.

scholarly journals Application of SVM Optimized by IPSO in Rolling Bearing Fault Diagnosis

2018 ◽  
Vol 227 ◽  
pp. 02007
Author(s):  
Mingzhu Lv ◽  
Xiaoming Su ◽  
Shixun Liu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Neighborhood Search ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Effect Of Support ◽  
Factor C

Aiming at the problem that the classification effect of support vector machine (SVM) is not satisfactory due to improper selection of penalty factor C and kernel parameter g, this paper proposes a new modified classifier that uses the improved particle swarm optimization (IPSO) to optimize the parameter of SVM (IPSO-SVM) by introducing the dynamic inertia weight, global neighborhood search, population shrinkage factor and particle mutation probability. The classification result is tested by Common data sets named BreastTissue、 Heart and Wine from the Libsvm toolbox, the results show that IPSO-SVM classifier is obviously superior to SVM and PSO-SVM classifier in terms of prediction accuracy and classification time. Then it is applied to the fault diagnosis in two classification problems and multiple classification problems of rolling bearings. The simulation results show that the IPSO-SVM classifier has stronger global convergence ability and faster convergence speed, and the ideal classification results can be obtained. Finally, the IPSO-SVM classifier is used to diagnose the fault of the actual bearing. The results show that the classifier has a better classification stability and is worthy of further promotion in engineering field.

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 Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haibo Liu ◽  
Yujie Dong ◽  
Fuzhong Wang
Keyword(s):  
Correlation Analysis ◽  
Prediction Model ◽  
Least Square ◽  
Support Vector ◽  
Entropy Weight ◽  
Grey Correlation ◽  
Gas Outburst ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Grey Correlation Analysis

This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. The case study demonstrates that the prediction accuracy of the proposed model is 92%, which is improved compared with that of the SVM model and GA-LSSVM model.

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.

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