Journal ArticleDOI
A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art.
Yiqi Liu,Ali M. Bazzi +1 more
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TLDR
A comprehensive review of fault detection and diagnosis methods targeting all the four major types of faults in IMs, which presents recent developments, trends and remaining difficulties regarding to FDD of IMs to inspire novel research ideas and new research possibilities.Abstract:
Preventing induction motors (IMs) from failure and shutdown is important to maintain functionality of many critical loads in industry and commerce. This paper provides a comprehensive review of fault detection and diagnosis (FDD) methods targeting all the four major types of faults in IMs. Popular FDD methods published up to 2010 are briefly introduced, while the focus of the review is laid on the state-of-the-art FDD techniques after 2010, i.e. in 2011–2015 and some in 2016. Different FDD methods are introduced and classified into four categories depending on their application domains, instead of on fault types like in many other reviews, to better reveal hidden connections and similarities of different FDD methods. Detailed comparisons of the reviewed papers after 2010 are given in tables for fast referring. Finally, a dedicated discussion session is provided, which presents recent developments, trends and remaining difficulties regarding to FDD of IMs, to inspire novel research ideas and new research possibilities.read more
Citations
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Journal ArticleDOI
A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network.
TL;DR: By comparing EDBN and DBN under different network structures, the results show that EDBN has better feature extraction and fault classification performance than traditional DBN.
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
Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review
TL;DR: A survey of existing broken rotor bar fault detection techniques with new classification based on fault signature is presented and classified based on loading level, number of broken bars, validation and signal processing.
Journal ArticleDOI
A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
Omar AlShorman,Muhammad Irfan,Nordin Saad,Dong Zhen,Noman Haider,Adam Glowacz,Ahmad Alshorman +6 more
TL;DR: This work presents an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods, and highlights the advantages and performance limitations of each method.
Journal ArticleDOI
Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information
Zhuo Long,Xiaofei Zhang,Li Zhang,Guojun Qin,Shoudao Huang,Dianyi Song,Haidong Shao,Gongping Wu +7 more
TL;DR: The proposed motor fault diagnosis method using attention mechanism and improved AdaBoost driven by multi-sensor information can enhance the robustness, generalization ability and accuracy of fault diagnosis.
References
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Proceedings ArticleDOI
Motor bearing damage detection using stator current monitoring
TL;DR: In this article, the authors used motor current spectral analysis to detect rolling-element bearing damage in induction machines, where the bearing failure modes were reviewed and bearing frequencies associated with the physical construction of the bearings were defined.
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.
Proceedings ArticleDOI
Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors
Austin H. Bonnett,G.C. Soukup +1 more
TL;DR: In this article, the authors attempt to identify the various causes of stator and rotor failures in three-phase squirrel cage induction motors, and a specific methodology is proposed to facilitate an accurate analysis of these failures.
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
A Survey of Condition Monitoring and Protection Methods for Medium-Voltage Induction Motors
TL;DR: A comprehensive survey of the existing condition monitoring and protection methods in the following five areas: thermal protection and temperature estimation, stator insulation monitoring, bearing fault detection, broken rotor bar/end-ring detection, and air gap eccentricity detection is presented in this article.