Graph-domain features and their application in rotating machinery fault diagnosis

Abstract To more effectively extract the non-stationary and non-linear fault features of mechanical vibration signals, a novel fault diagnosis method for rotating machinery is proposed combining time-domain, frequency-domain with graph-domain features. Different from the conventional time-domain and frequency-domain features, the graph-domain features generated from horizontal visibility graphs can extract the fault information hidden in the graph topology. Aiming at the problem that too many features will lead to information redundancy, the Fisher score algorithm is applied to select several of sensitive features which are then fed into the support vector machine to diagnose the faults of rotating machinery. Experimental results indicate features extracted from the three domains can be used to obtain higher diagnosis accuracy than that extracted from any single domain or dual domains.

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A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography

Complexity ◽

10.1155/2019/2619252 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Yongbo Li ◽

Xianzhi Wang ◽

Shubin Si ◽

Xiaoqiang Du

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Infrared Thermography ◽

Rotating Machinery ◽

Support Vector ◽

Intelligent Fault Diagnosis ◽

Fault Features ◽

Diagnosis Method ◽

Systematic Framework ◽

Machinery Fault Diagnosis

A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.

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

Shock and Vibration ◽

10.1155/2021/4395500 ◽

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%.

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Application of Xgboost Feature Extraction in Fault Diagnosis of Rolling Bearing

Mechanical Engineering Science ◽

10.33142/me.v1i2.1659 ◽

2020 ◽

Vol 1 (2) ◽

Author(s):

Xingang WANG ◽

Chao WANG

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Fault Diagnosis ◽

Frequency Domain ◽

Time Domain ◽

Vibration Signal ◽

Rolling Bearing ◽

Support Vector ◽

Time Frequency ◽

Diagnosis Accuracy

Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy, a fault diagnosis method based on Xgboost algorithm feature extraction is proposed. When the Xgboost algorithm classifies features, it generates an order of importance of the input features. The time domain features were extracted from the vibration signal of the rolling bearing, the time-frequency features were formed by the singular value of the modal components that were decomposed by the variational mode decomposition. Firstly, the extracted time domain and time-frequency domain features were input into the support vector machine respectively to observe the fault diagnosis accuracy. Then, Xgboost algorithm was used to rank the importance of features and got the accuracy of fault diagnosis. Finally, important features were extracted and the extracted features were input into the support vector machine to observe the fault diagnosis accuracy. The result shows that the fault diagnosis accuracy of rolling bearing is improved after important feature extraction in time domain and time-frequency domain by Xgboost.

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Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.470.683 ◽

2013 ◽

Vol 470 ◽

pp. 683-688

Author(s):

Hai Yang Jiang ◽

Hua Qing Wang ◽

Peng Chen

Keyword(s):

Fault Diagnosis ◽

Fault Detection ◽

Bayesian Network ◽

Frequency Domain ◽

Evaluation Method ◽

Roller Bearing ◽

Rotating Machinery ◽

Discrimination Index ◽

Vibration Signals ◽

Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

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A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Sensors ◽

10.3390/s18103521 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3521 ◽

Cited By ~ 6

Author(s):

Funa Zhou ◽

Po Hu ◽

Shuai Yang ◽

Chenglin Wen

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Real Time ◽

Time Domain ◽

Feature Fusion ◽

Rotating Machinery ◽

Time Domain Data ◽

Diagnosis Method ◽

The Time Domain ◽

Potential Frequency

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

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

Information ◽

10.3390/info10110359 ◽

2019 ◽

Vol 10 (11) ◽

pp. 359 ◽

Cited By ~ 2

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.

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Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer

Sensors ◽

10.3390/s21248168 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8168

Author(s):

Lihao Ye ◽

Xue Ma ◽

Chenglin Wen

Keyword(s):

Fault Diagnosis ◽

Knowledge Transfer ◽

Frequency Domain ◽

Rotating Machinery ◽

Domain Model ◽

Small Samples ◽

Target Domain ◽

Time Frequency ◽

Diagnosis Method ◽

Deep Cnn

Aiming at the problem of fault diagnosis when there are only a few labeled samples in the large amount of data collected during the operation of rotating machinery, this paper proposes a fault diagnosis method based on knowledge transfer in deep learning. First, we describe the data collected during the operation as a two-dimensional image with both time and frequency-domain characteristics. Second, we transform the trained source domain model into a shallow model suitable for small samples in the target domain, and we train the shallow model with small samples with labels. Third, we input a large number of unlabeled samples into the shallow model, and the output result of the system is regarded as the label of the input sample. Fourth, we combine the original data and the data annotated by the shallow model to train the new deep CNN fault diagnosis model so as to realize the migration of knowledge from the expert system to the deep CNN. The newly built deep CNN model is used for the online fault diagnosis of rotating machinery. The FFCNN-SVM shallow model tagger method proposed in this paper compares the fault diagnosis results with other transfer learning methods at this stage, and its correct rate has been greatly improved. This method provides new ideas for future fault diagnosis under small samples.

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Analog Circuit Fault Diagnosis Based on Adaptive Gaussian Deep Belief Network

MATEC Web of Conferences ◽

10.1051/matecconf/201817303090 ◽

2018 ◽

Vol 173 ◽

pp. 03090

Author(s):

WANG Ying-chen ◽

DUAN Xiu-sheng

Keyword(s):

Fault Diagnosis ◽

Adaptive Learning ◽

Analog Circuit ◽

Deep Belief Network ◽

Support Vector ◽

Belief Network ◽

Diagnosis Method ◽

Diagnosis Accuracy ◽

Network Application ◽

Circuit Fault Diagnosis

Aiming at the problem that the traditional intelligent fault diagnosis method is overly dependent on feature extraction and the lack of generalization ability, deep belief network is proposed for the fault diagnosis of the analog circuit; Then, by analyzing the deficiency of deep belief network application, a Gaussian deep belief network based on adaptive learning rate is proposed. The automatic adjustment learning step is adopted to further improve fault diagnosis efficiency and diagnosis accuracy; Finally, particle swarm support vector machine to extract the fault characteristics to identify. The simulation results of circuit fault diagnosis show that the algorithm has faster convergence speed and higher fault diagnosis accuracy.

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A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Entropy ◽

10.3390/e23010128 ◽

2021 ◽

Vol 23 (1) ◽

pp. 128

Author(s):

Chenbo Xi ◽

Guangyou Yang ◽

Lang Liu ◽

Hongyuan Jiang ◽

Xuehai Chen

Keyword(s):

Fault Diagnosis ◽

Single Channel ◽

Vibration Signal ◽

Rotating Machinery ◽

Support Vector ◽

Data Set ◽

Rotary Machine ◽

Gear Fault ◽

Diagnosis Method ◽

Low Sensitivity

In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

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An Intelligent Quadrotor Fault Diagnosis Method Based on Novel Deep Residual Shrinkage Network

Drones ◽

10.3390/drones5040133 ◽

2021 ◽

Vol 5 (4) ◽

pp. 133

Author(s):

Pu Yang ◽

Huilin Geng ◽

Chenwan Wen ◽

Peng Liu

Keyword(s):

Fault Diagnosis ◽

Window Method ◽

Signal Features ◽

Fault Features ◽

Diagnosis Method ◽

Diagnosis Accuracy ◽

Sliding Window Method ◽

Diagnosis Algorithm ◽

Soft Threshold ◽

Fitting Ability

In this paper, a fault diagnosis algorithm named improved one-dimensional deep residual shrinkage network with a wide convolutional layer (1D-WIDRSN) is proposed for quadrotor propellers with minor damage, which can effectively identify the fault classes of quadrotor under interference information, and without additional denoising procedures. In a word, that fault diagnosis algorithm can locate and diagnose the early minor faults of the quadrotor based on the flight data, so that the quadrotor can be repaired before serious faults occur, so as to prolong the service life of quadrotor. First, the sliding window method is used to expand the number of samples. Then, a novel progressive semi-soft threshold is proposed to replace the soft threshold in the deep residual shrinkage network (DRSN), so the noise of signal features can be eliminated more effectively. Finally, based on the deep residual shrinkage network, the wide convolution layer and DroupBlock method are introduced to further enhance the anti-noise and over-fitting ability of the model, thus the model can effectively extract fault features and classify faults. Experimental results show that 1D-WIDRSN applied to the minimal fault diagnosis model of quadrotor propellers can accurately identify the fault category in the interference information, and the diagnosis accuracy is over 98%.

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