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A Hybrid Monitoring Technique for Diagnosis of Mechanical Faults in Induction Motor
The paper aims to engineer an efficient technique for the condition monitoring of induction motor that not only provides the detection and identification of the faults but also assesses the operational condition of the motor. Induction motors possess one of the most important roles industrially and commercially. The developing faults in the motors can become catastrophic, if remain unanalyzed. The paper presents an effective novel solution to diagnose the major mechanical faults at the early possible stage by utilizing two efficient condition monitoring techniques to effectively deploy the strategies for the predictive maintenance. Primarily it employs the MCSA (Motor Current Signature Analysis), in which the faults are located by the spectral analysis of the particular harmonic components in the line current at specific characteristic frequencies generated by specific faults as the unique rotating flux. Fuzzy logic system has also been utilized, which assesses severity of the fault and operating condition of machine. The induction machine’s modular Simulink implementation has been presented that unlike other approaches provides the access to almost all parameters of the machine for analysis and control purposes. The mechanical faults specially bearing and eccentricity faults are simulated and successfully detected and localized in the results along with the severity assessment of the operational condition due to simulated faults.
Bearing faults, Condition Monitoring, Eccentricity, Fault Diagnosis, Fuzzy Logic, MCSA.
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