Journal ArticleDOI
Motor square current signature analysis for induction motor rotor diagnosis
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TLDR
In this article, a new motor square current signature analysis (MSCSA) fault diagnosis methodology is presented, which is based on three main steps: first, the induction motor current is measured; secondly, the square of the current is computed; and finally a frequency analysis of the square current is performed.About:
This article is published in Measurement.The article was published on 2013-02-01. It has received 71 citations till now. The article focuses on the topics: Induction motor & Rotor (electric).read more
Citations
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Journal ArticleDOI
A sparse auto-encoder-based deep neural network approach for induction motor faults classification
TL;DR: Compared with traditional neural network, the SAE-based DNN can achieve superior performance for feature learning and classification in the field of induction motor fault diagnosis.
Journal ArticleDOI
A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
Akhand Rai,S. H. Upadhyay +1 more
TL;DR: In this article, the authors have presented the various signal processing methods applied to the fault diagnosis of rolling element bearings with the objective of giving an opportunity to the examiners to decide and select the best possible signal analysis method as well as the excellent defect representative features for future application in the prognostic approaches.
Journal ArticleDOI
Gearbox condition monitoring in wind turbines: A review
TL;DR: A review on different methods and techniques for gearbox condition monitoring in wind turbines aiming to increase lifetime expectancy of components while reducing operation and maintenance cost is gathered.
Journal ArticleDOI
Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review
TL;DR: In this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings is introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced.
Journal ArticleDOI
Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising
TL;DR: In this paper, a novel diagnosis scheme based on envelope analysis and wavelet de-noising with sigmoid function based thresholding is used to extract the fault related symptoms from noisy vibration signatures of defective ball bearings operating at slow speed.
References
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Journal ArticleDOI
Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review
TL;DR: A review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.
Journal ArticleDOI
A review of induction motors signature analysis as a medium for faults detection
TL;DR: The fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors are introduced.
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.
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
Vibration and current monitoring for detecting airgap eccentricity in large induction motors
TL;DR: In this paper, a study carried out to detect air gap eccentricity in large 3-phase induction motors was carried out and the philosophy of using a unified online monitoring strategy was presented and the reasons for selecting line current and frame vibration as the monitored parameters are discussed.