Induction motors play a very important and crucial role in the development and overall productivity of industrial drives. Despite of the modern development the induction motor suffers from numerous faults and these faults consequence into lowering of overall industrial productivity and increased shutdown period. Thus, there is the need of pre-emptive detection of numerous faults while in operation. The prior detection of faults in induction motor and its optimum diagnosis can facilitate the industry to operate with least unexpected maintenance and industrial shutdown. An extensive literature survey conducted has indicated that the most common faults that could take place in induction motor are (i) inter-turn short circuit in stator winding, (ii) broken rotor bars, (iii) bearing failures and (iii) eccentricity faults. The presence of these faults in incipient phase may not necessarily deteriorate motor’s performance; however, it points out that the component should be replaced before the likelihood of a catastrophic failure. Hence, it is necessary to detect these faults as soon as possible. The paper the diagnosis of these faultswith the state-of-the-art signal processing techniques. The condition monitoring and faults diagnosis mechanism are required to formulate a well-defined and skilled map in between motor signals as well as indications of the fault state of the induction motor. Hence, a number of advanced and optimum approaches have been identified and employed for detecting various faults on-line in a squirrel-cage induction motor. The paper presents the experimental investigations that are implemented in different setups and the respective results have been presented. Faults in the motor pose their signature frequencies as harmonics in the motor current spectrum. In Motor Current Signature Analysis, various approaches like Park’s vector scheme, Fast Fourier Transformation (FFT), Discrete Wavelet Transform (DWT) based digital signal processing techniques have been taken into consideration.
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