Misalignment Fault Prediction of Motor-Shaft Using Multiscale Entropy and Support Vector Machine

2015 ◽  
pp. 359-370 ◽  
Author(s):  
Alok Kumar Verma ◽  
Somnath Sarangi ◽  
Mahesh Kolekar
Keyword(s):  
Support Vector Machine ◽  
Fault Prediction ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Motor Shaft ◽  
Misalignment Fault

scholarly journals Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 692
Author(s):  
Zhe Hua ◽  
Yancai Xiao ◽  
Jiadong Cao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Frequency Domain ◽  
Wind Turbines ◽  
Development Trend ◽  
Fault Prediction ◽  
Support Vector ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Misalignment Fault

A misalignment fault is a kind of potential fault in double-fed wind turbines. The reasonable and effective fault prediction models are used to predict its development trend before serious faults occur, which can take measures to repair in advance and reduce human and material losses. In this paper, the Least Squares Support Vector Machine optimized by the Improved Artificial Fish Swarm Algorithm is used to predict the misalignment index of the experiment platform. The mixed features of time domain, frequency domain, and time-frequency domain indexes of vibration or stator current signals are the inputs of the Least Squares Support Vector Machine. The kurtosis of the same signals is the output of the model, and theprinciple of the normal distribution is adopted to set the warning line of misalignment fault. Compared with other optimization algorithms, the experimental results show that the proposed prediction model can predict the development trend of the misalignment index with the least prediction error.

Erratum to: Prognostics of Induction Motor Shaft Based on Feature Importance and Least Square Support Vector Machine Regression

2021 ◽  
Vol 18 (2) ◽  
pp. 8751
Author(s):  
Didik Djoko Susilo ◽  
A. Widodo ◽  
T. Prahasto ◽  
M. Nizam
Keyword(s):  
Support Vector Machine ◽  
Induction Motor ◽  
Mechanical Engineering ◽  
Least Square ◽  
Support Vector ◽  
Motor Shaft ◽  
Support Vector Machine Regression ◽  
Related Article ◽  
Feature Importance

This is an erratum to International Journal of Automotive and Mechanical Engineering 2021; 18(1): 8464–8477. Please refer to the related article: https://doi.org/10.15282/ijame.18.1.2021.06.0641

Research on Fault Prediction Technology of Complicated Equipment

2013 ◽  
Vol 347-350 ◽  
pp. 448-452 ◽  
Author(s):  
Sai Sai Jin ◽  
Kao Li Huang ◽  
Guang Yao Lian ◽  
Bao Chen Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Square ◽  
Fault Prediction ◽  
Support Vector ◽  
Hydraulic Pump ◽  
Fault Information

For the problems of not enough fault information for the complicated equipment and difficult to predict the fault, we apply Support Vector Machine (SVM) to build the fault prediction model. On the basis of analyzing regression algorithm of SVM, we use Least Square Support Vector Machine (LS-SVM) to build the fault prediction model.LS-SVM can effectively debase the complication of the model. Finally, we take the fault data of a hydraulic pump to validate this model. By selecting appropriate parameters, this model can make better prediction for the fault data, and it has higher prediction precision. It is proved that the fault prediction model which based on LS-SVM can make better prediction for fault trend of complicated equipment.

scholarly journals Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Shuang Pan ◽  
Tian Han ◽  
Andy C. C. Tan ◽  
Tian Ran Lin
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Sample Entropy ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Vibration Signals ◽  
Vibration Data ◽  
Motor Vibration

An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine. Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals. Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales. The entropy features are directly extracted from the nonlinear, nonstationary induction motor vibration signals which are then sorted by using mutual information so that the elements in the feature vector are ranked according to their importance and relevant to the faults. The first five most important features are selected from the feature vectors and classified using support vector machine. The proposed method is then employed to analyze the vibration data acquired from a motor fault simulator test rig. The classification results confirm that the proposed method can effectively diagnose various motor faults with reasonable good accuracy. It is also shown that the proposed method can provide an effective and accurate fault diagnosis for various induction motor faults using only vibration data.

Effective fault prediction model developed using Least Square Support Vector Machine (LSSVM)

2018 ◽  
Vol 137 ◽  
pp. 686-712 ◽  
Author(s):  
Lov Kumar ◽  
Sai Krishna Sripada ◽  
Ashish Sureka ◽  
Santanu Ku. Rath
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Square ◽  
Fault Prediction ◽  
Support Vector
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