scholarly journals Fine-Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Guodong Sun ◽  
Yuan Gao ◽  
Kai Lin ◽  
Ye Hu
Keyword(s):  
Fault Diagnosis ◽  
Dictionary Learning ◽  
Nonnegative Matrix ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Time Frequency ◽  
Fine Grained ◽  
Diagnosis Method

To accurately diagnose fine-grained fault of rolling bearing, this paper proposed a new fault diagnosis method combining multisynchrosqueezing transform (MSST) and sparse feature coding based on dictionary learning (SFC-DL). Firstly, the high-resolution time-frequency images of raw vibration signals, including different kinds of fine-grained faults of rolling bearing, were constructed by MSST. Then, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC), and the trained basis dictionary was employed to extract features from time-frequency matrixes by using nonnegative linear equations. Finally, a linear support vector machine (LSVM) was trained with features of training samples, and the trained LSVM was employed to diagnosis the fault classification of test samples. Compared with state-of-the-art fault diagnosis methods, the proposed method, which was tested on the bearing dataset from Case Western Reserve University (CWRU), achieved the fine-grained classification of 10 mixed fault states. Meanwhile, the proposed method was applied on the dataset from the Machinery Failure Prevention Technology (MFPT) Society and realized the classification of 3 fault states under different working conditions. These results indicate that the proposed method has great robustness and could better meet the needs of practical engineering.

scholarly journals Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Guodong Sun ◽  
Ye Hu ◽  
Bo Wu ◽  
Hongyu Zhou
Keyword(s):  
Dictionary Learning ◽  
Nonnegative Matrix ◽  
Texture Features ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Time Frequency ◽  
Fault Recognition ◽  
S Transform ◽  
Diagnosis Method ◽  
Low Dimensional

Addressing the problem that it is difficult to extract the features of vibration signal and diagnose the fault of rolling bearing, we propose a novel diagnosis method combining multisynchrosqueezing S transform and faster dictionary learning (MSSST-FDL). Firstly, MSSST is adopted to transform vibration signals into high-resolution time-frequency images. Then, the local binary pattern (LBP) operator is introduced to extract the low-dimensional texture features of time-frequency images, which improves the speed of fault recognition. Finally, nonnegative matrix factorization (NMF) with only one hyperparameter and nonnegative linear equation are used to solve the dictionary learning and feature coding, respectively. The feature coding is input into the classifier for training and recognition. Experiments show that our method performs well on the rolling bearing dataset of Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT). Further, the proposed method is applied to the loudspeaker pure-tone detection dataset, and the loudspeaker anomaly diagnosis is achieved. The diagnosis results verify that our method can meet the needs of practical engineering.

scholarly journals An Intelligent Bearing Fault Diagnosis Method Based on SF-SVM

2021 ◽  
Vol 1210 (1) ◽  
pp. 012004
Author(s):  
Bao’an Qiu ◽  
Pan Sun ◽  
Lili Li
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Support Vector ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
Frequency Map ◽  
The Stability ◽  
Vibration Time ◽  
Short Time

Abstract Rolling bearing, as a key component of rotating machinery, its health status directly determines the stability and reliability of the whole machine. The research on its intelligent diagnosis method has important engineering value and academic significance. However, due to actual engineering conditions, the types of bearing failures and the amount of data are limited. Aiming at the difficulty of extracting and selecting bearing vibration features under limited sample constraints, this pa-per proposes an intelligent fault diagnosis method of SF-SVM. On the basis of the short-time Fourier change, the L2 regularized sparse filter is used to extract the unsupervised feature of the bearing vibration time-frequency map. After obtaining the typical features of the bearing, the support vector machine is used for diagnosis.

scholarly journals Compound Fault Diagnosis of Rolling Bearing Based on ALIF-KELM

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Ma ◽  
Shitong Liang ◽  
Zhengyu Du ◽  
Ming Chen
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Training Set ◽  
Sample Data ◽  
Diagnosis Method ◽  
Main Fault ◽  
Bp Model ◽  
Fault Information

Aiming at the shortcomings of difficult classification of rolling bearing compound faults and low recognition accuracy, a composite fault diagnosis method of rolling bearing combined with ALIF and KELM is proposed. First, the basic concepts of ALIF and KELM are introduced, and then ALIF is used to decompose the sample data of vibration signals of different bearing states so that each sample can get several IMFs, select the top K IMFs containing the main fault information from each sample, calculate the energy feature and sample entropy of each IMF, and construct a fault feature vector with a dimension of 2K. Finally, the feature vectors of the training set and the test set are input into the KELM model for fault classification. Experimental results show that, compared with EMD-KELM model, ALIF-ELM model, ALIF-BP model, and IFD-KELM model, the rolling bearing composite fault diagnosis method based on the ALIF-KELM model has higher classification accuracy.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

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.

Roller Bearing Fault Diagnosis Based on IMF Kurtosis and SVM

2013 ◽  
Vol 694-697 ◽  
pp. 1160-1166
Author(s):  
Ke Heng Zhu ◽  
Xi Geng Song ◽  
Dong Xin Xue
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
The Imf

This paper presents a fault diagnosis method of roller bearings based on intrinsic mode function (IMF) kurtosis and support vector machine (SVM). In order to improve the performance of kurtosis under strong levels of background noise, the empirical mode decomposition (EMD) method is used to decompose the bearing vibration signals into a number of IMFs. The IMF kurtosis is then calculated because of its sensitivity of impulses caused by faults. Subsequently, the IMF kurtosis values are treated as fault feature vectors and input into SVM for fault classification. The experimental results show the effectiveness of the proposed approach in roller bearing fault diagnosis.

scholarly journals A Fault Diagnosis Method of Rolling Mill Bearing at Low Frequency and Overload Condition Based on Integration of EEMD and GA-DBN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiang Ji ◽  
Chen Zhao ◽  
Yongqin Wang ◽  
Tuanmin Zhao ◽  
Xinyou Zhang
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Rolling Mill ◽  
Rolling Force ◽  
Low Frequency ◽  
Frequency Domain Analysis ◽  
Fault Classification ◽  
Low Speed ◽  
Time Frequency ◽  
Diagnosis Method

To solve the problems of difficult fault signal recognition and poor diagnosis effect of different damage in the same position in rolling mill bearing at low speed, a fault diagnosis method of rolling mill bearing based on integration of EEMD and DBN was proposed. The vibration signals in horizontal, axial, and vertical directions were decomposed and reconstructed by EEMD, and frequency domain analysis was carried out by using refined spectrum. Then, the signal's time-frequency domain index, rolling force, and torque component feature vector were input into genetic algorithm (GA) to optimize DBN model classification. In order to verify the effectiveness of the method, the experimental study was carried out on the two-high experimental rolling mill. The results show that EEMD combined with thinning spectrum can solve the problem of fault feature extraction well. Compared with time-frequency domain characteristic input, the prediction accuracy of DBN model is obviously improved. And the accuracy of GA-DBN model is higher, and the accuracy is 98.3%, and the time taken to diagnose is significantly reduced. Finally, the fault classification of different parts of bearings and the fault diagnosis of different damage in the same part are realized, which provides a good theoretical basis for the fault diagnosis of low-speed bearings and has important engineering significance.

A Novel Fault Diagnosis Method Based on Time-Frequency Image Recognition

2014 ◽  
Vol 687-691 ◽  
pp. 3569-3573 ◽  
Author(s):  
Wei Gang Wang ◽  
Zhan Sheng Liu
Keyword(s):  
Fault Diagnosis ◽  
Image Recognition ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Time Frequency ◽  
Rolling Element ◽  
Diagnosis Method ◽  
Element Bearing ◽  
Vibration Time ◽  
Pyramid Matching

A novel intelligent fault diagnosis method based on vibration time-frequency image recognition is proposed in this paper. First, Smooth pseudo Wigner-Ville distribution (SPWVD) is employed to represent the time-frequency distribution characteristics. Then, the features of time-frequency images are extracted by using locality-constrained linear coding (LLC) and spatial pyramid matching. Next, we use the support vector machine to identify these feature vectors for realizing intelligent fault detection. The promise of our algorithm is illustrated by performing above procedures on the vibration signals measured from rolling element bearing with sixteen operating states. Experimental results show that the proposed method can acquire higher diagnosis accuracy compared with the ScSPM method in rolling element bearing diagnosis.

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