Fault diagnosis of induction motors: an architecture for real-time assessment as a cyber-physical system

Induction motors (IMs) have a crucial and significant role in various industrial sectors. With the prolonged operation of IMs, faults tend to develop that can be classified into five major categories, i.e., broken rotor bars, stator winding faults, air-gap eccentricity, bearing faults, and load torque fluctuations. If the faults go undetected, it may lead to catastrophic failure. Hence, the predictive-based condition monitoring technique has evolved as a real-time fault diagnosis that exploits the revolutionary idea of cyber-physical system (CPS). Furthermore, motor current signature analysis (MCSA) is a non-invasive fault diagnosis technique of a motor that can be used to investigate the presence of five fault types. However, the major constraint that industries face today is the on-field implementation of MCSA-based fault diagnosis involving CPS-based architecture, executed in an automated manner. Hence, the present article depicts algorithms that aim at real-time monitoring of IMs through a CPS framework. The proposed methodology is automated, does not involve any human intervention, and has been validated with real-time experiments, depicting its effectiveness and practicality.