Self-Sensing Induction Motors for Condition Monitoring

Motors have been exploited as their own sensors for diagnostic and operating conditions at least, since the dawn of modern computing. Contracting systems theory offers a new level of precision in detecting small parameter and state changes in an electric machine for load fault detection and diagnostics from the motor terminals. The presented offline method successfully solves the motor inverse problem to reconstruct the characteristic instantaneous angular speed and load torque signals of the motor during periodic operation. The solution includes a motor parameter estimation step that reflects the specific temperatures and magnetic saturation of the motor during data acquisition. This identification or inversion method is suitable for induction motors driving periodic loads with and without rotor angle-dependent loading. A practical condition monitoring application is demonstrated: valve and cylinder fault detection in reciprocating compressors.