Abstract
The fault diagnosis in motor is substantial as it results in breakdown of production line and the faults may damage motor and results in economic losses. Bearing failure, rotor eccentricity, shaft misalignment and load related faults are the most frequent failures under mechanical fault category. This paper addresses three such faults that may increase the stator temperature namely air gap eccentricity, shaft misalignment and cooling system failure. The thermography technique has been used widely for fault detection in induction motor. In three phase induction motor the thermal images are analyzed for healthy condition and the above mentioned faults conditions. This paper presents thermal pixels counting algorithm to calculate the diagnosis indicators and adaptive neuro-fuzzy inference system classifier is used to classify the faults based on the diagnosis indicator data base. Laboratory based experimental investigation are carried out to verify the accuracy of the proposed method. This method provides accurate diagnosis indicator that will be used as a bench mark value for preparing the maintenance schedule under non-destructive mode.
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06 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04050-1
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Acknowledgements
The author would like to thank the Department of Science and Technology (DST)—New Delhi, India for providing financial support under the FIST-(DST-FIST(SR/FST/college-235/2014) and thank the department of Electrical and Electronics Engineering, K.S. Rangasamy college of technology for providing permission to carry out the research.
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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-04050-1
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Devarajan, G., Chinnusamy, M. & Kaliappan, L. RETRACTED ARTICLE: Detection and classification of mechanical faults of three phase induction motor via pixels analysis of thermal image and adaptive neuro-fuzzy inference system. J Ambient Intell Human Comput 12, 4619–4630 (2021). https://doi.org/10.1007/s12652-020-01857-8
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DOI: https://doi.org/10.1007/s12652-020-01857-8