Induction motor condition monitoring using machine learning

An industrial drive comprising:

• drive circuitry configured to supply power to an induction motor in an industrial operation;
• a controller coupled with the drive circuitry and configured to control the power supplied to the induction motor based at least in part on runtime signal data associated with the induction motor;
• and
• a condition monitoring module configured to:
• obtain the runtime signal data from the controller;
• derive runtime metrics from the runtime signal data based on an induction motor fault condition;
• provide the runtime metrics as input to a machine learning model constructed to identify a status of the induction motor based on the runtime metrics and output the status;
• and
• monitor the induction motor fault condition based on the status of the induction motor output by the machine learning model.

One or more computer-readable storage media having program instructions stored thereon to perform condition monitoring in an industrial automation environment, wherein the program instructions, when read and executed by a processing system, direct the processing system to at least:

• in a motor drive configured to supply power to an induction motor in an industrial operation based at least in part on runtime signal data, obtain the runtime signal data;
• in the motor drive, derive runtime metrics from the runtime signal data based on an induction motor fault condition;
• in the motor drive, provide the runtime metrics as input to a machine learning model constructed to identify a status of the induction motor based on the runtime metrics and output the status;
• and
• in the motor drive, monitor the induction motor fault condition based on the status of the induction motor output by the machine learning model.