ONLINE MONITORING AND ANALYSIS OF INDUCTION MOTOR USING CURRENT SIGNATURE ANALYSIS IMPLEMENTING WAVELET ANALYSIS AND FFT ANALYSIS

Three phase squirrel cage induction motors are widely used in industrial applications.Thus fault occurring in these motors will adversely affect the Industrial products and increase the shut down time. As time moves, the machine develops major faults and disturbs the production line leading to major financial losses and production instability. In order to overcome these losses at critical circumstances, the fault could be found out prior to it rather than becoming into a major fault. Condition Monitoring System helps in predicting and identifying the pre fault condition in faster and effective way, which prevents the unwanted breakdown timein working hours. Among the various Condition Monitoring Systems such as thermal monitoring, vibration monitoring and so on ,Motor Current Signature Analysis (MCSA) is the best possible solution.As among all the available solutions are intrusive in operation (need shutdown period for testing), maintenance of the measuring equipment, bulk initial capital required, very high sensitivity and unreliable source with even small unsuitable environmental factors, slower in fault detection .All the undesirable factors of the other methods made MCSA an undisputable, viable, feasible ,reliable and best solution for fault detection mechanism to be followed in even small scale industries. They are being followed in many places often due to it'snon-intrusive approach to detect the fault, in addition a fast identifying method using FFT analysis .Normally MCSA are performed with FFT analysis which is effective in cases of constant load but as of today it is being an emergent problem to find fault in changing and fluctuating load conditions, where FFT fails its viability and this condition made the solution to choose wavelet analysis. In this paper, we have designed a complete3 phase induction motor with matlab toolsand induced faults into the motor externally (by varying the input frequency) and analysed the problem by using wavelet analysis. By analyzing the frequency spectrums of the stator current, electrical torque and angular speed, we were able to determine the total harmonic distortion for different faults and it will be produces clear explanation about the fault that is being introduced into the machine which will finally pollute the power system by injecting harmful harmonics into it.This method helps in envisioning the fault and thus helps in reducing the occurrence of severe faults (Brinelling, Arcing, Rotor Failure etc.) in the motor. Thus it acts as an important tool to protect machines from falling into unrepairable form. The MCSA model takes the initial step in improving the efficiency of the system and the technique utilizes the result of spectral analysis of stator current, torque and speed characteristics of the motor model.