Journal ArticleDOI
Neural networks application for induction motor faults diagnosis
TLDR
It is concluded that neural detectors for rotor and stator faults as well as for rolling bearings and supply asymmetry faults can be developed based on measurement data acquired on-line in the drive system.About:
This article is published in Mathematics and Computers in Simulation.The article was published on 2003-11-17. It has received 149 citations till now. The article focuses on the topics: Multilayer perceptron & Induction motor.read more
Citations
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Journal ArticleDOI
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
TL;DR: A fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body is proposed.
Journal ArticleDOI
A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier
TL;DR: Effectiveness and feasibility of the 1D CNN based fault diagnosis method is validated by applying it to two commonly used benchmark real vibration data sets and comparing the results with the other competing intelligent fault diagnosis methods.
Journal ArticleDOI
Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review
Purushottam Gangsar,Rajiv Tiwari +1 more
TL;DR: Overall, this paper includes review of system signals, conventional and advance signal processing techniques; however, it mainly covers, the selection of effective statistical features, AI methods, and associated training and testing strategies for fault diagnostics of IMs.
Journal ArticleDOI
Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review
TL;DR: A broad outlook on rotor fault monitoring techniques for the researchers and engineers can be found in this paper, where the authors review and summarize the recent researches and developments performed in condition monitoring of the induction machine with the purpose of rotor faults detection.
Journal ArticleDOI
An Effective Neural Approach for the Automatic Location of Stator Interturn Faults in Induction Motor
TL;DR: A neural approach to detect and locate automatically an interturn short-circuit fault in the stator windings of the induction machine by a feedforward multilayer-perceptron neural network trained by back propagation.
References
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Journal ArticleDOI
Neural-network-based motor rolling bearing fault diagnosis
TL;DR: Simulation and real-world testing results obtained indicate that neural networks can be effective agents in the diagnosis of various motor bearing faults through the measurement and interpretation of motor bearing vibration signatures.
Book
Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines
TL;DR: In this article, the effects of time harmonics on various space-phasor loci, harmonic amplitude estimation Monitoring of the rotor speed and the rotor angle Monitoring various machine parameters Diagnosis, condition monitoring Bibliography Index
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
AI techniques in induction machines diagnosis including the speed ripple effect
TL;DR: Various applications of artificial intelligence (AI) techniques (expert systems, neural networks, and fuzzy logic) presented in the literature prove that such technologies are well suited to cope with on-line diagnostic tasks for induction machines.
Book
Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-neural, and Genetic-Algorithm-based Techniques
TL;DR: This book is the first comprehensive discussion of AI applications to electrical machines and drives.