Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/284945
Title: Health Monitoring System for Three Phase Induction Motor Using Soft Computing Techniques
Researcher: Iqbal, Sharif
Guide(s): Chaturvedi, D. K.
Keywords: Engineering and Technology,Engineering,Engineering Electrical and Electronic
University: Dayalbagh Educational Institute
Completed Date: 2018
Abstract: Induction motor plays an important role as one of the best workhorses due its reliability, robustness and compact in structure. Due to its typical working environment induction motors are susceptible to many types of faults. If the faults are not identified at the earlier stage then problem may become catastrophic and the induction motor may suffer severe damage. Thus, unidentified motor faults may cascade into motor failure, which in turn may cause total shutdown of the system. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. newlineIn this thesis health monitoring system for three phase squirrel cage induction motor using soft computing techniques has been developed and tested. Two simultaneous techniques have been detailed regarding the health analysis of an induction motor. They are namely induction motor health monitoring techniques and induction motor faults diagnosis techniques. newlineA dedicated hardware set up is developed for the purpose of health monitoring of induction motor with the facility of all necessary sensor connected in that. Here health monitoring of induction motor has been done by using electrical exogenous variables and as well as non electrical exogenous variables. Both the system together will help to take a fine decision about the health condition of the motor. After acquiring all those variables by the sensors connected in hardware, they are processed and displayed in the computer monitor numerically and graphically online by using the LabVIEW software. Total 21 parameters including electrical and non electrical are displayed on line on the computer screen graphically and numerically and continuously are being saved in a chosen directory. For abnormalities of any parameters message of unhealthy condition is displayed on the front panel of the LabVIEW. newline Different soft computing techniques like neural approach, GNN, Quantum, and Fuzzy and their integrations has been implemented to get the better analysis.At the end results using different soft computing techniques are compared. newline newline
Pagination: 
URI: http://hdl.handle.net/10603/284945
Appears in Departments:Department of Electrical Engineering

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01_title.pdfAttached File23.3 kBAdobe PDFView/Open
02_certificate.pdf223.19 kBAdobe PDFView/Open
03_declaration.pdf151.46 kBAdobe PDFView/Open
04_abstract.pdf30.57 kBAdobe PDFView/Open
05_acknowledgement.pdf85.61 kBAdobe PDFView/Open
06_contents.pdf140.46 kBAdobe PDFView/Open
07_list_of_tables.pdf37.99 kBAdobe PDFView/Open
08_list_of_figures.pdf114.24 kBAdobe PDFView/Open
09_abbreviation.pdf81.75 kBAdobe PDFView/Open
10_chapter1.pdf281.74 kBAdobe PDFView/Open
11_chapter2.pdf452.01 kBAdobe PDFView/Open
12_chapter3.pdf246.64 kBAdobe PDFView/Open
13_chapter4.pdf1.16 MBAdobe PDFView/Open
14_chapter5.pdf3.81 MBAdobe PDFView/Open
15_conclusion.pdf102.66 kBAdobe PDFView/Open
16_references.pdf591.28 kBAdobe PDFView/Open
17_appendix.pdf1.44 MBAdobe PDFView/Open
18_summary.pdf98.03 kBAdobe PDFView/Open


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