Modeling and experimental study reveals quantitative detection boundaries for rotary machinery faults via stator current, suggesting improved predictive maintenance in industrial drivetrains.
Induction motor drivetrains are commonly utilized as the power unit of various industrial equipment. By taking the induction motor itself as a built-in sensor and analyzing the stator current, noncontact health condition monitoring of the rotary machinery in the drivetrain can be realized. Reported works on this topic have qualitatively validated the effectiveness of such technique, yet its applicable boundary has not been quantitatively elaborated. This article analytically models the strength of the rotary machinery fault signatures in stator current based on electromechanical coupling. The correctness of the proposed quantitative model and its nonlinear characteristics are numerically validated via finite-element simulations. Experiments under impulsive load torque oscillation and gear wear degradation further quantitatively evaluate the applicable boundary of the stator-current-based fault detection in real-world applications. The proposed analytical model and the revealed quantitative characteristics can help optimize the utilization of induction motor stator current analysis in the predictive maintenance of induction motor drivetrain.
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Chen et al. (2025) studied this question.
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