Due to their great benefits over other types of electric motors, induction motors are widely employed in industrial, commercial, and household applications. These motors are subjected to a wide range of operating stresses, which can lead to failure. The most common repeated failures in induction motors are bearing faults, stator interturn faults, and fractured rotor bars. For reliable and cost-effective operation, early diagnosis of induction motor defects is crucial. Induction motor faults and failures can cause long periods of downtime and significant maintenance and revenue losses. The cost of purchasing and installing equipment is often less than half of the entire cost of maintenance over the machine's lifetime. Maintenance costs range from 15% to 40% of the overall cost, and they might reach as high as 80% of the whole cost. The failure of a heavily loaded equipment can often bring an entire industry process to a standstill. The demand for automated manufacturing systems with effective monitoring and control capabilities has grown in response to the growing demand for high-quality, low-cost production.
An induction motor's condition monitoring and fault diagnosis are crucial in the production process. By enabling for the early detection of catastrophic failures, it can reduce maintenance costs and the danger of unanticipated breakdowns. Vibration monitoring, heat monitoring, chemical monitoring, and acoustic emission monitoring are just a few of the condition monitoring methods available, but they all require expensive sensors or specialised instruments. Current monitoring, on the other hand, does not necessitate the purchase of additional expensive sensors because basic electrical quantities such as voltage and current are easily measured by voltage and current transformers, which are always provided as part of the protective system. As a result, current monitoring is non-intrusive and can be used even if the motor is located far away from the control centre. As a result, MCSA demonstrates that it is a low-cost online nondestructive fault diagnostic and detection system that can accurately identify motor defects.
The findings of experiments using signal processing and artificial neural networks to identify numerous faults in induction motors are presented in this chapter. Motor line currents recorded under various fault circumstances were analysed using the continuous wavelet transform. For fault characterisation, a feedforward neural network was employed with fault features retrieved using the continuous wavelet transform.
Author(s) Details:
A. U. Jawadekar,
Department of Electrical Engineering, S. S. G. M. College of Engineering
Shegaon, (M.S.), India.
G. M. Dhole,
Department of Electrical Engineering, S. S. G. M. College of Engineering
Shegaon, (M.S.), India.
S. R. Paraskar.
Department of Electrical Engineering, S. S. G. M. College of Engineering
Shegaon, (M.S.), India.
S. S. Jadhao,
Department of Electrical Engineering, S. S. G. M. College of Engineering
Shegaon, (M.S.), India.
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