A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery

The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD) can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD) technique based local characteristic-scale decomposition (LCD) was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN) and mean impact value (MIV), ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.

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Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

Shock and Vibration ◽

10.1155/2015/847802 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 13

Author(s):

Songrong Luo ◽

Junsheng Cheng ◽

HungLinh Ao

Keyword(s):

Fault Diagnosis ◽

Fault Location ◽

Roller Bearing ◽

Vibration Signal ◽

Singular Values ◽

Local Characteristic ◽

Support Vector ◽

Svm Classifier ◽

Feature Values ◽

Value Decomposition

Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.

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Rotating machinery vibration fault diagnosis model based on autoencoder

Proceedings of the 2020 International Conference on Aviation Safety and Information Technology ◽

10.1145/3434581.3434732 ◽

2020 ◽

Author(s):

Qin Yang ◽

Xinwei Cao ◽

Qing Ye

Keyword(s):

Fault Diagnosis ◽

Rotating Machinery ◽

Model Based ◽

Diagnosis Model

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Wavelet Neural Network Based on EKF Algorithm and its Application in Fault Diagnosis for Rotating Machinery

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.1741 ◽

2014 ◽

Vol 602-605 ◽

pp. 1741-1744

Author(s):

Xu Sheng Gan ◽

Hua Ping Li ◽

Hai Long Gao

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Wavelet Neural Network ◽

Model Based ◽

Proposed Model ◽

Reliable Diagnosis ◽

Diagnosis Model

It is difficult to realize an accurate and reliable diagnosis in the rotating machinery. To solve this problem, a Wavelet Neural Network (WNN) diagnosis model based on EKF algorithm is proposed. In the model, EKF algorithm is introduced to optimize the parameters of WNN, and then the built WNN model is used to diagnose the faults of the rotating machinery. The experiment shows that, the proposed model has a good diagnosis capability in the field of the rotating machinery.

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Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

Shock and Vibration ◽

10.1155/2009/519502 ◽

2009 ◽

Vol 16 (1) ◽

pp. 89-98 ◽

Cited By ~ 55

Author(s):

Junsheng Cheng ◽

Dejie Yu ◽

Jiashi Tang ◽

Yu Yang

Keyword(s):

Fault Diagnosis ◽

Feature Vector ◽

Roller Bearing ◽

Vibration Signal ◽

Singular Values ◽

Rotating Machinery ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition

Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

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A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis

Shock and Vibration ◽

10.1155/2019/1908485 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 6

Author(s):

Lei You ◽

Wenjie Fan ◽

Zongwen Li ◽

Ying Liang ◽

Miao Fang ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Vibration Signal ◽

Rotating Machinery ◽

Fault Classification ◽

Support Vector ◽

Local Optimum ◽

Classification Rate ◽

Wavelet Energy ◽

Diagnosis Model

Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time-spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog-leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA-SVM), and SFLA-SVM. The experiment results show that MSFLA-SVM achieves a much higher fault classification rate than BPNN, ACROA-SVM, and SFLA-SVM.

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Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽

10.3390/pr9060919 ◽

2021 ◽

Vol 9 (6) ◽

pp. 919

Author(s):

Wanlu Jiang ◽

Chenyang Wang ◽

Jiayun Zou ◽

Shuqing Zhang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Generative Adversarial Networks ◽

Extraction Ability ◽

One Dimensional ◽

Adversarial Networks ◽

Diagnosis Model ◽

The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

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Probabilistic Model-Based Fault Diagnosis of the Rotor System

ASME 2007 Power Conference ◽

10.1115/power2007-22072 ◽

2007 ◽

Author(s):

Jiye Shao ◽

Rixin Wang ◽

Jingbo Gao ◽

Minqiang Xu

Keyword(s):

Fault Diagnosis ◽

Bayesian Network ◽

Network Model ◽

Rotating Machinery ◽

Rotor System ◽

Bayesian Network Model ◽

Diagnosis System ◽

Model Based ◽

Core Components ◽

Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

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A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

Shock and Vibration ◽

10.1155/2014/825825 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 25

Author(s):

HungLinh Ao ◽

Junsheng Cheng ◽

Kenli Li ◽

Tung Khac Truong

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Roller Bearing ◽

Local Characteristic ◽

Support Vector ◽

Svm Classifier ◽

Outer Race ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

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Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1014.501 ◽

2014 ◽

Vol 1014 ◽

pp. 501-504 ◽

Cited By ~ 2

Author(s):

Shu Guo ◽

You Cai Xu ◽

Xin Shi Li ◽

Ran Tao ◽

Kun Li ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Roller Bearing ◽

Vibration Signal ◽

Classification Performance ◽

Outer Race ◽

Mode Decomposition ◽

Diagnosis Method ◽

Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

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