Journal ArticleDOI
Optimal MLP neural network classifier for fault detection of three phase induction motor
V. N. Ghate,Sanjay V. Dudul +1 more
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TLDR
In this article, an optimal MLP NN based classifier is proposed for fault detection which is inexpensive, reliable, and noninvasive by employing more readily available information such as stator current.Abstract:
Induction motors are critical components in commercially available equipments and industrial processes due to cost effective and robust performance. Under various operating stresses, motors deteriorate their conditions which result into various faults. Early detection and diagnosis of these faults are desirable for online condition assessment, product quality assurance and improved operational efficiency. From the related work reported so far it is observed that researchers used vibration analysis, harmonics present in stator current, chemical analysis, electromagnetic analysis, etc. As these approaches are complex in view of the requirement of precise measurement and mathematical modeling. As compared to analytical methods, AI based schemes are more efficient and accurate. In this paper optimal MLP NN based classifier is proposed for fault detection which is inexpensive, reliable, and noninvasive by employing more readily available information such as stator current. Detailed design procedure for MLP and SOM NN models is given for which simple statistical parameters are used as input feature space and Principal Component Analysis is used for reduction of input dimensionality. Robustness of classifier to noise is verified on unseen data by introducing controlled Gaussian and Uniform noise in input and output.read more
Citations
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Induction motors bearing fault detection using pattern recognition techniques
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Batch process monitoring based on support vector data description method
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Proceedings ArticleDOI
Twitter spam detection based on deep learning
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Journal ArticleDOI
Stator fault analysis of three-phase induction motors using information measures and artificial neural networks
Gustavo Henrique Bazan,Gustavo Henrique Bazan,Paulo Rogério Scalassara,Wagner Endo,Alessandro Goedtel,Wagner Fontes Godoy,Rodrigo Henrique Cunha Palácios +6 more
TL;DR: In this paper, the authors presented a pattern recognition method for the detection of stator windings short circuits based on measures of mutual information between the phase current signals in three-phase induction motors.
Journal ArticleDOI
Just‐in‐time reorganized PCA integrated with SVDD for chemical process monitoring
Qingchao Jiang,Xuefeng Yan +1 more
TL;DR: In this paper, a just-in-time reorganized PCA model that objectively selects the principal components (PCs) online for process monitoring is proposed, and the importance of the PCs is evaluated by kernel density estimation.
References
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Journal ArticleDOI
Current signature analysis to detect induction motor faults
W.T. Thomson,M. Fenger +1 more
TL;DR: In this paper, the industrial application of motor current signature analysis (MCSA) to diagnose faults in three-phase induction motor drives is discussed, which is a noninvasive, online monitoring technique for the diagnosis of problems in induction motors.
Journal ArticleDOI
Online Diagnosis of Induction Motors Using MCSA
TL;DR: An online induction motor diagnosis system using motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms is proposed, able to ascertain four kinds of motor faults and diagnose the fault status of an induction motor.
Journal ArticleDOI
Recent developments of induction motor drives fault diagnosis using AI techniques
TL;DR: A review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI) covers the application of expert systems, artificial neural networks (ANNs), and fuzzy logic systems that can be integrated into each other and also with more traditional techniques.
Journal ArticleDOI
What stator current processing-based technique to use for induction motor rotor faults diagnosis?
Mohamed Benbouzid,G. B. Kliman +1 more
TL;DR: In this article, a comparison of signal processing-based techniques for the detection of broken bars and bearing deterioration in induction motors is presented, which are then analyzed and compared to deduce the most appropriate technique for induction motor rotor rotor fault detection.
Journal ArticleDOI
A new approach to intelligent fault diagnosis of rotating machinery
Yaguo Lei,Zhengjia He,Yanyang Zi +2 more
TL;DR: The proposed approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS) can reliably recognise different fault categories and severities.