Fault Indexing Parameter Based Fault Detection in Induction Motor via MCSA with Wiener Filtering

Fault detection in an induction motor, particularly at premature stage has become necessary to avoid unexpected damage in industrial process. In this paper, an approach to detect the early stage faults in induction machine using motor current signature analysis (MCSA) is presented. It is proposed to...

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Bibliographic Details
Published in:Electric power components and systems 2021-06, Vol.48 (19-20), p.2048-2062
Main Authors: Deekshit, Kompella K. C., Rao, Mannam V. Gopala, Rao, Rayapudi S.
Format: Article
Language:English
Subjects:
Adaptive filters
bearing fault
Fault detection
fault indexing parameter
Induction motors
motor current signature analysis
Noise
noise cancellation
Noise reduction
rotor faults
Rotors
Signature analysis
stator faults
Stators
Topology
Wiener filter
Wiener filtering
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Summary:Fault detection in an induction motor, particularly at premature stage has become necessary to avoid unexpected damage in industrial process. In this paper, an approach to detect the early stage faults in induction machine using motor current signature analysis (MCSA) is presented. It is proposed to estimate the fault severity from stator current using noise cancelation by an adaptive filter (Wiener filter). Wavelet De-noising technique is implemented to reduce the effect of noise floor in noise canceled stator current. Different categories of bearing faults, broken rotor fault and stator inter turn faults in induction motor are estimated with and without de-nosing using pre-fault component cancelation (Noise cancelation). In addition, fault index based on standard deviation (SD) and simple square integral (SSI) value of noise canceled stator current are proposed. The proposed fault detection topology is examined using simulations and experiments on a 3HP, 1HP and 0.5HP induction motors for bearing, broken rotor and stator inter turn faults respectively.
ISSN:1532-5008
1532-5016
DOI:10.1080/15325008.2021.1910376