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.

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 Gears Based on Vibrations by Support Vector Machine Algorithms

2012 ◽  
Author(s):  
D. J. Bordoloi ◽  
Rajiv Tiwari
Keyword(s):  
Sensitivity Analysis ◽  
Support Vector Machine ◽  
Health Monitoring ◽  
Time Domain ◽  
Training Data ◽  
Support Vector ◽  
Vibration Signals ◽  
Time Domain Data ◽  
Testing Data ◽  
Speed Range

Health monitoring of gears is very critical for satisfactorily overall working of the complex machinery. Thus, the ability to detect gear faults and classify them based on their nature becomes very important aspect of health monitoring of machines. In this paper, SVM algorithms have been used for the multiclass prediction of faults with the help of time domain vibration signals obtained from the gearbox casing operated in a suitable speed range. Moreover, it tries to examine the performance of the SVM technique by optimizing its parameters on utilization of time domain data from multi-fault gear box. The SVM software was fed with the training data and testing data at similar operating speeds for three types of defects and no defect case, and classification ability of SVM was noted and found to be excellent. The sensitivity analysis of optimized parameters is studied and conclusions are drawn.

scholarly journals Bearing Fault Diagnosis in the Mixed Domain Based on Crossover-Mutation Chaotic Particle Swarm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tongle Xu ◽  
Junqing Ji ◽  
Xiaojia Kong ◽  
Fanghao Zou ◽  
Wilson Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Particle Swarm ◽  
Diagnostic Technique ◽  
Fault Classification ◽  
Support Vector ◽  
Time Frequency ◽  
Bearing Fault ◽  
Mutation Strategy

The classification frameworks for fault diagnosis of rolling element bearings in rotating machinery are mostly based on analysis in a single time-frequency domain, where sensitive features are not completely extracted. To solve this problem, a new fault diagnosis technique is proposed in the mixed domain, based on the crossover-mutation chaotic particle swarm optimization support vector machine. Firstly, fault features are generated using techniques in the time domain, the frequency domain, and the time-frequency domain. Secondly, the weighted maximum relevance minimum redundancy (WMRMR) algorithm is adopted to reduce the dimension of the feature set and to establish the representative feature set. Thirdly, a new crossover-mutation strategy is suggested to reduce the local minima in optimization, and an optimization disturbance is added. Finally, the support vector machine is optimized using the improved chaotic particle swarm to improve fault classification diagnosis. The effectiveness of the proposed new bearing fault diagnostic technique is verified by experimental tests under different bearing conditions. Test results showed that the bearing fault classification accuracy of CMCPSO-SVM in the mixed domain was much higher than those in a single feature domain.

scholarly journals Classification of Refrigerant Flow Noise of Air Conditioners Based on Continuous Wavelet Transform and Support Vector Machine

2020 ◽  
Vol 10 (11) ◽  
pp. 3959
Author(s):  
Un-Chang Jeong
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Support Vector ◽  
Continuous Wavelet ◽  
Air Conditioner ◽  
Time Frequency ◽  
Flow Noise ◽  
The Time Domain

This study proposes a classification method that uses the continuous wavelet transform and the support vector machine approach to classify refrigerant flow noises generated in an air conditioner. The air conditioning noise was identified as an abnormal signal by the use of the first- and second-order moments. The start and end times of refrigerant flow noises were identified by detecting the singularities of the continuous wavelet transform coefficient in the time domain and by means of listening to the measured sounds. Further, the time-frequency characteristics of refrigerant flow noise were analyzed with the continuous wavelet transform. For the support vector machine-based classification of refrigerant flow noise in an air conditioner, the grid search method was used to determine kernel hyperparameters. Five-fold cross validation was employed for the application of the support vector machine to the classification of air conditioner refrigerant noise. In addition, measured sound sources were modified based on classified refrigerant flow noise to compare the classification accuracy of a jury test with the results of the support vector machine.

Intelligent Identification Based on Wavelet Packet Decomposition and Adaptive Particle Swarm Optimization SVM

2013 ◽  
Vol 380-384 ◽  
pp. 4043-4046
Author(s):  
Qiang Wang ◽  
Li Jing Ren
Keyword(s):  
Support Vector Machine ◽  
Flow Regime ◽  
Wavelet Packet ◽  
Particle Swarm ◽  
Flow Regimes ◽  
Frequency Resolution ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Horizontal Pipe ◽  
Time Frequency

In this paper, a new Intelligent Identification method based on wavelet packet decomposition and APSO-SVM was put forward. As is known the characteristic of pressure drop is nonlinear and non-stationary. The wavelet packet transform can decompose signals to different frequency bands according to any time frequency resolution ratio, the features are extracted from the differential pressure fluctuation signals of the air-water two-phase flow in the horizontal pipe and the wavelet packet energy features of various flow regimes are obtained. The adaptive particle swarm ptimization support vector machine was trained using these eigenvectors as flow regime samples, and the flow regime intelligent identification was realized. The test results show the wavelet packet energy features can excellently reflect the difference between four typical flow regimes, and successful training the support vector machine can quickly and accurately identify four typical flow regimes. So a new way to identify flow regime by soft sensing is proposed.

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
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