scholarly journals A Comprehensive Diagnosis Method of Rolling Bearing Fault Based on CEEMDAN-DFA-Improved Wavelet Threshold Function and QPSO-MPE-SVM

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1142
Author(s):  
Yi Wang ◽  
Chuannuo Xu ◽  
Yu Wang ◽  
Xuezhen Cheng
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Threshold Function ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Noise Interference ◽  
Comprehensive Diagnosis ◽  
Diagnosis Method

A comprehensive fault diagnosis method of rolling bearing about noise interference, fault feature extraction, and identification was proposed. Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuation analysis (DFA), and improved wavelet thresholding, a denoising method of CEEMDAN-DFA-improved wavelet threshold function was presented to reduce the distortion of the noised signal. Based on quantum-behaved particle swarm optimization (QPSO), multiscale permutation entropy (MPE), and support vector machine (SVM), the QPSO-MPE-SVM method was presented to construct the fault-features sets and realize fault identification. Simulation and experimental platform verification showed that the proposed comprehensive diagnosis method not only can better remove the noise interference and maintain the original characteristics of the signal by CEEMDAN-DFA-improved wavelet threshold function, but also overcome overlapping MPE values by the QPSO-optimizing MPE parameters to separate the features of different fault types. The experimental results showed that the fault identification accuracy of the fault diagnosis can reach 95%, which is a great improvement compared with the existing methods.

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.

A Research of MM-SVM Technique for Fault Diagnosis

2014 ◽  
Vol 556-562 ◽  
pp. 2633-2637
Author(s):  
Hong Yin ◽  
Shu Qiang Yang ◽  
Guo Ming Li ◽  
Ping Yin ◽  
Song Chang Jin
Keyword(s):  
Fault Diagnosis ◽  
Satellite Telemetry ◽  
Fault Identification ◽  
Support Vector ◽  
Physical Relationship ◽  
Vector Machines ◽  
Diagnosis Method ◽  
Multiple Support ◽  
Multiple Support Vector Machines ◽  
Satellite Equipment

With the satellite development of our country, higher accuracy and stability are requires, which makes the control systems becoming more complex and requiring more telemetry parameters. Data mining techniques do not consider the physical relationship between the various components, but use of satellite telemetry parameters of the satellite states the purpose of fault identification. In this paper, we give a model based on multiple support vector machines (MM-SVM) technology satellite fault diagnosis method. The experiment shows that our method is effective in satellite equipment fault diagnosis

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.

Rolling bearing fault diagnosis of PSO–LSSVM based on CEEMD entropy fusion

2020 ◽  
Vol 44 (3) ◽  
pp. 405-418
Author(s):  
Shuzhi Gao ◽  
Tianchi Li ◽  
Yimin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Kernel Principal Component Analysis ◽  
Permutation Entropy ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Taking aim at the nonstationary nonlinearity of the rolling bearing vibration signal, a rolling bearing fault diagnosis method based on the entropy fusion feature of complementary ensemble empirical mode decomposition (CEEMD) is proposed in combination with information fusion theory. First, CEEMD of the vibration signal of the rolling bearing is performed. Then the signal is decomposed into the sum of several intrinsic mode functions (IMFs), and the singular entropy, energy entropy, and permutation entropy are obtained for the IMFs with fault features. Second, the feature extraction method of entropy fusion is proposed, and the three entropy data obtained are input into kernel principal component analysis (KPCA) for feature fusion and dimensionality reduction to obtain complementary features. Finally, the extracted features are imported into the particle swarm optimization (PSO) algorithm to optimize the least-squares support vector machine (LSSVM) for fault classification. Through experimental verification, the proposed method can be used for roller bearing fault diagnosis.

scholarly journals Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

2019 ◽  
Vol 39 (4) ◽  
pp. 968-986
Author(s):  
Zhe Yuan ◽  
Tingting Peng ◽  
Dong An ◽  
Daniel Cristea ◽  
Mihai Alin Pop
Keyword(s):  
Fault Diagnosis ◽  
Time Scale ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Identification Accuracy ◽  
Threshold Function ◽  
Fluctuation Analysis ◽  
Intrinsic Time ◽  
Time Scale Decomposition ◽  
Proper Rotation

To effectively utilize a feature set to further improve fault diagnosis of a rolling bearing vibration signal, a method based on multi-fractal detrended fluctuation analysis (MF-DFA) and smooth intrinsic time-scale decomposition (SITD) was proposed. The vibration signal was decomposed into several proper rotation components by applying this new SITD method to overcome noise effects, preserve the effective signal, and improve the signal-to-noise ratio. Wavelet analysis was embedded in iteration procedures of intrinsic time-scale decomposition (ITD). For better results, an adaptive threshold function was used for signal recovery from noisy proper rotation components in the wavelet domain. Additionally, MF-DFA was used to reveal the multi-fractality present in the instantaneous amplitude of the proper rotation components. Finally, linear local tangent space alignment was applied for feature dimension reduction and to obtain fault characteristics of different types, further improving identification accuracy. The performance of the proposed method is determined to be superior to that of the ITD-MF-DFA method.

scholarly journals Fault Diagnosis Method for Rotating Machinery Based on Hierarchical Amplitude-Aware Permutation Entropy and Pairwise Feature Proximity

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ling Shu ◽  
Jinxing Shen ◽  
Xiaoming Liu
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Low Frequency ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Support Vector ◽  
Feature Vectors ◽  
Fault Features ◽  
Diagnosis Method ◽  
Frequency Features

With a view to solving the defect that multiscale amplitude-aware permutation entropy (MAAPE) can only quantify the low-frequency features of time series and ignore the high-frequency features which are equally important, a novel nonlinear time series feature extraction method, hierarchical amplitude-aware permutation entropy (HAAPE), is proposed. By constructing high and low-frequency operators, this method can extract the features of different frequency bands of time series simultaneously, so as to avoid the issue of information loss. In view of its advantages, HAAPE is introduced into the field of fault diagnosis to extract fault features from vibration signals of rotating machinery. Combined with the pairwise feature proximity (PWFP) feature selection method and gray wolf algorithm optimization support vector machine (GWO-SVM), a new intelligent fault diagnosis method for rotating machinery is proposed. In our method, firstly, HAPPE is adopted to extract the original high and low-frequency fault features of rotating machinery. After that, PWFP is used to sort the original features, and the important features are filtered to obtain low-dimensional sensitive feature vectors. Finally, the sensitive feature vectors are input into GWO-SVM for training and testing, so as to realize the fault identification of rotating machinery. The performance of the proposed method is verified using two data sets of bearing and gearbox. The results show that the proposed method enjoys obvious advantages over the existing methods, and the identification accuracy reaches 100%.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Modified Deep Metric Learning Method

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zengbing Xu ◽  
Xiaojuan Li ◽  
Hui Lin ◽  
Zhigang Wang ◽  
Tao Peng
Keyword(s):  
Fault Diagnosis ◽  
Metric Learning ◽  
Similarity Criterion ◽  
Rolling Bearing ◽  
Support Vector ◽  
Learning Method ◽  
Common Time ◽  
Diagnosis Method ◽  
Deep Metric Learning ◽  
Metric Function

A novel fault diagnosis method of rolling bearing based on deep metric learning and Yu norm is proposed in this paper, which is called a deep metric learning method based on Yu norm (DMN-Yu). In order to solve the misclassification caused by the traditional deep metric learning based on distance metric function, a similarity criterion based on Yu norm is introduced into the traditional deep metric learning. Firstly, the deep metric learning neural network (DMN) is used to adaptively extract the fault feature parameters. Secondly, considering that the data samples at the boundary between different fault categories can be misclassified, the marginal Fisher analysis method based on Yu norm is used to optimize the features. And then, BPNN classifier of DMN-Yu method is used to fine tune the network parameters and diagnose the fault category. Finally, the effectiveness and feasibility of the proposed DMN-Yu method is verified with the rolling bearing fault diagnosis test. And the superiority of the proposed diagnosis method is validated by comparing its diagnosis accuracy with the deep metric learning method based on Euclidean distance (DMN-Euc), traditional deep belief network (DBN), and support vector machine (SVM) combined with the common time-domain statistical features.

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