Abstract: Condition identification is the cornerstone of induction motor state monitoring. However, existing non-intrusive methods suffer from varying degrees of challenges concerning precision, robustness, and generalizability. In response to these challenges, this paper proposes a new approach for identifying two operating parameters of induction motors: speed and load torque. This method is based on a hybrid physical-empirical knowledge model, amalgamating the advantages of clear mechanistic understanding from physical modeling and the practicality of empirical modeling. Our method introduces empirical information from two dimensions of stator current: frequency and RMS value. Commencing with the induction motor’s physical model, we derive mathematical relationships between the stator current's frequency and RMS value—these are the dependent variables—and the physical relationships between the induction motor's speed and torque load. These relationships are employed as a fitting function in subsequent analyses, and they are determined through least-squares fitting using prior empirical data on condition identification. Consequently, our method enables the virtual measurement of induction motor operating parameters solely using stator current signals. By incorporating multi-dimensional empirical information from stator current and utilizing a fitting function grounded in physical principles, our approach inherently possesses a high level of accuracy and robustness. Experimental results demonstrate that the operating parameters identified using our method closely match real values. Furthermore, comparative analyses with other existing condition identification methods validate the superior accuracy of our approach in condition identification.

Key words: Induction motor, torque measurement, speed measurement, condition monitoring, hybrid physical-empirical knowledge model