Data beholding and fault diagnosis of induction motors

Electrical engineering

The condition beholding of induction motor has been a challenging task for the engineers and researchers mainly in industries. There are many methods in condition beholding, including vibration beholding, thermal beholding, chemical beholding, acoustic emission beholding but, all these beholding methods require expensive sensors or specialized tools whereas current beholding out of all does not require additional sensors. Current beholding techniques are usually applied to track the various types of induction motor faults such as rotor fault, short winding fault, air gap eccentricity fault, bearing fault, load fault etc. In current beholding, no additional sensors are necessary. This is because the completely necessary electrical quantities related to electromechanical plants like current and voltage are readily measured by tapping into the prevailing voltage and current transformers that are always installed as a part of the protection system. As a current beholding is non-invasive and may even be implemented in the motor control centre remotely from the motors being monitored The MCSA uses the current spectrum of the machine for locating characteristic fault frequencies. When a fault is present, the frequency spectrum of the road current becomes different from healthy motor. Such fault modulates the air-gap and produces rotating frequency harmonics within the self and mutual inductances of the machine. It depends upon locating specific harmonic component within the line current.[2] An extensive literature survey has been finished understanding the varied faults and signal processing techniques available. It was observed that fault frequencies occur within the motor current spectra are unique for various motor faults. These fault frequencies can be easily tracked with help of Motor Current Signature analysis (MCSA). Therefore, MCSA based techniques are used to present work for diagnosis of the common faults of induction motor. There are three type of Fourier transform, such as Fast Fourier Transform algorithm (FFT), Short Time Fourier transform algorithm and Wavelet Transform based multi resolution analysis algorithm. FFT Method is easy to implement. More interesting signals contain numerous transitory characteristics like drift, trends, and abrupt changes. These characteristics are often the most important part of the signal, and the Fourier analysis is not suitable for their diagnosis. Therefore, other methods for signal analysis, such as STFT, Wavelet transform are subsequently used to track the rotor faults experimentally.[1]

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