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http://hdl.handle.net/10603/454345
Title: | Certain investigation on fault Diagnosis of an induction motor for Various industrial application |
Researcher: | Sakthivel, G |
Guide(s): | Princewinston, D |
Keywords: | Engineering and Technology Engineering Engineering Electrical and Electronic nduction motor wavelet transform Bearing Fault |
University: | Anna University |
Completed Date: | 2022 |
Abstract: | Squirrel Cage Induction motors are high reliable electro mechanical devices used in fans, pumps, blowers, compressors and also in other industrial applications. Out of the total world electric power consumption around 85% is by induction motors. In industries the motor is subjected to thermal mechanical and electrical stresses which results in failure of the motor. Induction motor subjected to minor fault may not lead to catastrophic condition immediately but fault degrade the performance of the motor and lead to unplanned down time associated with cost and safety factors. To prevent break down time it is essential to develop fault diagnosis and condition monitoring system for an induction motor based on the running characteristics and maintenance schedule is to be predicted in well advance. Induction motor is subjected to several faults like stator fault, rotor fault and bearing fault. newlineMajor widespread fault in an induction motor is the stator fault. According to the studies from Institute of Electrical and Electronics Engineers (IEEE) and Electric Power Research Institute (EPRI) 28-36% faults in an induction motor is the stator fault that is due to either mechanical or electrical stress. The 5-10% faults in an induction motor is the rotor fault due to either damages in the rotor bar or in the end rings. Due to continuous stress in the bearing, nearly 42% faults in an induction motor are bearing fault and it results in vibration and cause eccentricity fault. newlineIn this research work a novel method is developed for finding fault in an induction motor which is highly reliable and accurate. The proposed method using intelligent techniques such as Neural Network and Support Vector Machine (SVM) for finding the faults in an induction motor. Now a days Industries have power quality issues like voltage Sag, Swell, Transients and Harmonics. The existing fault identification methods does not provide accurate results during the existence of power quality issues newline |
Pagination: | xvi,123p. |
URI: | http://hdl.handle.net/10603/454345 |
Appears in Departments: | Faculty of Electrical Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 29.34 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 1.55 MB | Adobe PDF | View/Open | |
03_content.pdf | 21.03 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 14.48 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 1.28 MB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 241.16 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 944.9 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 633.55 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 256.93 kB | Adobe PDF | View/Open | |
10_annexures.pdf | 121.75 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 76 kB | Adobe PDF | View/Open |
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