Induction motor modeling using modified winding function for fault simulation under variable speeds and loads using regime normalization

In the case of rotating machine monitoring systems, classical approaches for health monitoring and fault detection become obsolete. These classical approaches require sensors to collect enough data. This data collection process is not always a viable option, especially in new and special equipment such as hosting unit in nuclear reactors, gantry cranes for shipbuilding, etc. For this purpose, an analytical model based on the modified winding function (MWF) of an induction motor is proposed to generate the data, and to simulate the different types of faults such as eccentricity and broken rotor bars under variable conditions. Correct implementation of faults in the model can be detected from the generated stator current. This model uses turn and winding functions to describe the distribution of windings, as well as an air gap function incorporating stator and rotor slot effects. These functions are used to calculate inductances and flux distribution in the air gap, and are saved offline in a lookup-table for a high number of rotor positions. This lookup-table is used to calculate currents, torque, speed, rotor position in a reduced simulation time online. This paper proposes an induction motor model for current simulation under variable regimes and different types of faults (healthy, broken rotor bar, static, dynamic and mixed eccentricities). These simulations are used for a robust health indicator (HI) extraction coupled with a regime identification and regime normalization steps to reduce class dispersion, and to separate different health states. Results are compared with the ones calculated using FEM under healthy, broken rotor bar, and static, dynamic, mixed eccentricities.