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

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
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.

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

2021 ◽  
Vol 1207 (1) ◽  
pp. 012008
Author(s):  
Yiyuan Gao ◽  
Wenliao Du ◽  
Xiaoyun Gong ◽  
Dejie Yu
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Mechanical Vibration ◽  
Rotating Machinery ◽  
Support Vector ◽  
Fisher Score ◽  
Fault Features ◽  
Diagnosis Method ◽  
Diagnosis Accuracy

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.

scholarly journals Sparse Representation and SVM Diagnosis Method Inter-Turn Short-Circuit Fault in PMSM

2019 ◽  
Vol 9 (2) ◽  
pp. 224 ◽  
Author(s):  
Siyuan Liang ◽  
Yong Chen ◽  
Hong Liang ◽  
Xu Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Permanent Magnet ◽  
Short Circuit ◽  
Small Sample ◽  
Support Vector ◽  
Feature Vectors ◽  
Diagnosis Method ◽  
Short Circuit Fault

Permanent magnet synchronous motors (PMSM) has the advantages of simple structure, small size, high efficiency, and high power factor, and a key dynamic source and is widely used in industry, equipment and electric vehicle. Aiming at its inter-turn short-circuit fault, this paper proposes a fault diagnosis method based on sparse representation and support vector machine (SVM). Firstly, the sparse representation is used to extract the first and second largest sparse coefficients of both current signal and vibration signals, and then they are composed into four-dimensional feature vectors. Secondly, the feature vectors are input into the support vector machine for fault diagnosis, which is suitable for small sample. Experiments on a permanent magnet synchronous motor with artificially set inter-turn short-circuit fault and a normal one showed that the method is feasible and accurate.

scholarly journals A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 626 ◽  
Author(s):  
Wenlong Fu ◽  
Jiawen Tan ◽  
Chaoshun Li ◽  
Zubing Zou ◽  
Qiankun Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Production Efficiency ◽  
Economic Benefits ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Support Vector ◽  
Industrial Manufacture ◽  
Sine Cosine Algorithm ◽  
Diagnosis Approach

As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.

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

Entropy ◽  
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.

scholarly journals A Multi-Stage Fault Diagnosis Method for Proton Exchange Membrane Fuel Cell Based on Support Vector Machine with Binary Tree

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6526
Author(s):  
Jiaping Xie ◽  
Chao Wang ◽  
Wei Zhu ◽  
Hao Yuan
Keyword(s):  
Fault Diagnosis ◽  
Binary Tree ◽  
Proton Exchange Membrane ◽  
Pem Fuel Cells ◽  
Proton Exchange ◽  
Support Vector ◽  
Multi Stage ◽  
Fault Features ◽  
Diagnosis Method ◽  
Exchange Membrane

The reliability and durability of the proton exchange membrane (PEM) fuel cells are vital factors restricting their applications. Therefore, establishing an online fault diagnosis system is of great significance. In this paper, a multi-stage fault diagnosis method for the PEM fuel cell is proposed. First, the tests of electrochemical impedance spectroscopy under various fault conditions are conducted. Specifically, prone recoverable faults, such as flooding, membrane drying, and air starvation, are included, and different fault degrees from minor, moderate to severe, are covered. Based on this, an equivalent circuit model (ECM) is selected to fit impedance spectroscopy by the hybrid genetic particle swarm optimization algorithm, and then fault features are determined by the analysis of each model parameter under different fault conditions. Furthermore, a multi-stage fault diagnosis model is constructed with the support vector machine with the binary tree, in which fault features obtained from the ECM are used as the characteristic inputs to realize the fault classification (including fault type and fault degree) online. The results show that the accuracy of the basic fault test and subdivided fault test can reach 100% and 98.3%, respectively, which indicates that the proposed diagnosis method can effectively identify flooding, drying, and air starvation of PEM fuel cells.

scholarly journals A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Xiao-hui He ◽  
Dong Wang ◽  
Yan-feng Li ◽  
Chun-hua Zhou
Keyword(s):  
Fault Diagnosis ◽  
Restricted Boltzmann Machine ◽  
Training Data ◽  
High Dimensional ◽  
Support Vector ◽  
Boltzmann Machine ◽  
Bearing Fault ◽  
Feature Vectors ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

To realize the fault diagnosis of bearing effectively, this paper presents a novel bearing fault diagnosis method based on Gaussian restricted Boltzmann machine (Gaussian RBM). Vibration signals are firstly resampled to the same equivalent speed. Subsequently, the envelope spectrums of the resampled data are used directly as the feature vectors to represent the fault types of bearing. Finally, in order to deal with the high-dimensional feature vectors based on envelope spectrum, a classifier model based on Gaussian RBM is applied. Gaussian RBM has the ability to provide a closed-form representation of the distribution underlying the training data, and it is very convenient for modeling high-dimensional real-valued data. Experiments on 10 different data sets verify the performance of the proposed method. The superiority of Gaussian RBM classifier is also confirmed by comparing with other classifiers, such as extreme learning machine, support vector machine, and deep belief network. The robustness of the proposed method is also studied in this paper. It can be concluded that the proposed method can realize the bearing fault diagnosis accurately and effectively.

Research on the Time-Frequency Analysis Method to Extract Early Fault Features of Rotating Machinery

2012 ◽  
Vol 190-191 ◽  
pp. 1371-1375
Author(s):  
Ping Hua Ju ◽  
Gen Bao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Mechanical Equipment ◽  
Analysis Method ◽  
Time Frequency ◽  
Fault Features ◽  
Common Diagnosis ◽  
Diagnosis Method ◽  
Weak Fault ◽  
Basic Features

Early fault features of rotating machinery is very weak and is disturbed by strong noise generally. how to more accurately extract early (weak) fault features from signals is still a hot and difficult point of research of the discipline. An intensive study is given to basic features of rotating machinery early faults and common diagnosis method, And also summarized the research status of early diagnosis in the field of mechanical equipment signal feature extraction and fault diagnosis, analyzed the current problems, and finally briefly pointed out the development of early fault diagnosis in machinery applications.

scholarly journals Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3312 ◽  
Author(s):  
Jie Wu ◽  
Tang Tang ◽  
Ming Chen ◽  
Tianhao Hu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Spectrum Analysis ◽  
Support Vector ◽  
Bearing Fault ◽  
Feature Vectors ◽  
Bearing Fault Diagnosis ◽  
Fixed Dimension ◽  
Fault Features ◽  
Self Adaptive

Bearings are critical parts of rotating machines, making bearing fault diagnosis based on signals a research hotspot through the ages. In real application scenarios, bearing signals are normally non-linear and unstable, and thus difficult to analyze in the time or frequency domain only. Meanwhile, fault feature vectors extracted conventionally with fixed dimensions may cause insufficiency or redundancy of diagnostic information and result in poor diagnostic performance. In this paper, Self-adaptive Spectrum Analysis (SSA) and a SSA-based diagnosis framework are proposed to solve these problems. Firstly, signals are decomposed into components with better analyzability. Then, SSA is developed to extract fault features adaptively and construct non-fixed dimension feature vectors. Finally, Support Vector Machine (SVM) is applied to classify different fault features. Data collected under different working conditions are selected for experiments. Results show that the diagnosis method based on the proposed diagnostic framework has better performance. In conclusion, combined with signal decomposition methods, the SSA method proposed in this paper achieves higher reliability and robustness than other tested feature extraction methods. Simultaneously, the diagnosis methods based on SSA achieve higher accuracy and stability under different working conditions with different sample division schemes.

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