scholarly journals Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 8682-8690 ◽  
Author(s):  
Sana Ullah Jan ◽  
Young-Doo Lee ◽  
Jungpil Shin ◽  
Insoo Koo
Keyword(s):  
Support Vector Machine ◽  
Time Domain ◽  
Fault Classification ◽  
Support Vector ◽  
Sensor Fault ◽  
Statistical Time

Optimization of support vector machine based multi-fault classification with evolutionary algorithms from time domain vibration data of gears

2013 ◽  
Vol 227 (11) ◽  
pp. 2428-2439 ◽  
Author(s):  
DJ Bordoloi ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Time Domain ◽  
Angular Speed ◽  
Fault Classification ◽  
Support Vector ◽  
Vibration Data ◽  
Bee Colony ◽  
Time Domain Data ◽  
Grid Search Method

In the present work, a multi-fault classification of gears has been attempted by the support vector machine learning technique using the vibration data in time domain. A proper utilization of the support vector machine is based on the selection of support vector machine parameters. The main focus of this article is to examine the performance of the multiclass ability of support vector machine techniques by optimizing its parameters using the grid-search method, genetic algorithm and artificial bee colony algorithm. Four fault conditions were considered. A group of statistical features were extracted from time domain data. The prediction of fault classification is attempted at the same angular speed as the measured data as well as innovatively at the intermediate and extrapolated angular speed conditions. This is due to the fact that it is not feasible to have measurement of vibration data at all continuous speeds of interest. The classification ability is noted and it shows an excellent prediction performance.

Multi-Fault Classifier Based on Support Vector Machine and Its Application

2005 ◽  
Vol 293-294 ◽  
pp. 483-492 ◽  
Author(s):  
Zhou Suo Zhang ◽  
Minghui Shen ◽  
Wenzhi Lv ◽  
Zheng Jia He
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Time Domain ◽  
Simple Algorithm ◽  
Fault Classification ◽  
Support Vector ◽  
Turbine Generator ◽  
Signal Features ◽  
Multi Classification

Aiming at problem on limiting development of machinery fault intelligent diagnosis due to needing many fault data samples, this paper improves a multi-classification algorithm of support vector machine, and a multi-fault classifier based on the algorithm is constructed. Training the multi-fault classifier only needs a small quantity of fault data samples in time domain, and does not need signal preprocessing of extracting signal features. The multi-fault classifier has been applied to fault diagnosis of steam turbine generator, and the results show that it has such simple algorithm, online fault classification and excellent capability of fault classification as advantages.

Health Monitoring of Gear Elements Based on Time-Frequency Vibration by Support Vector Machine Algorithms

2013 ◽  
Author(s):  
D. J. Bordoloi ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Health Monitoring ◽  
Time Domain ◽  
Wavelet Packet ◽  
Optimization Methods ◽  
Fault Classification ◽  
Support Vector ◽  
Time Frequency ◽  
Bee Colony

Health monitoring of a gear box has been attempted by the support vector machine (SVM) learning technique with the help of time-frequency (wavelet) vibration data. Multi-fault classification capability of the SVM is suitably demonstrated that is based on the selection of SVM parameters. Different optimization methods (i.e., the grid-search method (GSM), the genetic algorithm (GA) and the artificial bee colony algorithm (ABCA)) have been performed for optimizing the SVM parameters. Four fault conditions have been considered including the no defect case. Time domain vibration signals were obtained from the gearbox casing operated in a suitable speed range. The continuous wavelet transform (CWT) and wavelet packet transform (WPT) are extracted from time domain signals. A set of statistical features are extracted from the wavelet transform. The classification ability is noted and compared against predictions when purely time domain data is used, and it shows an excellent prediction performance.

Experimental Time-Domain Vibration-Based Fault Diagnosis of Centrifugal Pumps Using Support Vector Machine

2017 ◽  
Vol 3 (4) ◽  
Author(s):  
Janani Shruti Rapur ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Time Domain ◽  
Flow Instability ◽  
Flow Instabilities ◽  
Fault Classification ◽  
Support Vector ◽  
Centrifugal Pumps ◽  
Vibration Data ◽  
Svm Algorithm ◽  
Rbf Kernel

When the hydraulic flow path is incompatible with the physical contours of the centrifugal pump (CP), flow instabilities occur. A prolonged operation in the flow-instability region may result in severe damages of the CP system. Hence, two of the major causes of flow instabilities such as the suction blockage (with five levels of increasing severity) and impeller defects are studied in the present work. Thereafter, an attempt is made to classify these faults and differentiate the physics behind the flow instabilities caused due to them. The tri-axial CP vibration data in time domain are employed for the fault classification. Multidistinct and multicoexisting fault classifications have been performed with different combinations of these faults using support vector machine (SVM) algorithm with radial basis function (RBF) kernel. Prediction results from the experiments and the developed methodology help to segregate the faults into appropriate class, identify the severity of the suction blockage, and substantiate the practical applicability of this study.

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 Research on Fault Diagnosis Model of Rotating Machinery Vibration Based on Information Entropy and Improved SVM

2019 ◽  
Vol 118 ◽  
pp. 02036 ◽  
Author(s):  
Hankun Bing ◽  
Yuzhu Zhao ◽  
Le Pang ◽  
Minmin Zhao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Time Domain ◽  
Information Entropy ◽  
Singular Value ◽  
Support Vector ◽  
Learning Platform ◽  
Diagnosis Model ◽  
Concept Of Information

Based on the concept of information entropy, this paper analyzes typical nonlinear vibration fault signals of steam turbine based on spectrum, wavelet and HHT theory methods, and extracts wavelet energy spectrum entropy, IMF energy spectrum entropy, time domain singular value entropy and frequency domain power spectrum entropy as faults. The feature is supported by a support vector machine (SVM) as a learning platform. The research results show that the fusion information entropy describes the vibration fault more comprehensively, and the support vector machine fault diagnosis model can achieve higher diagnostic accuracy.

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