Steady-State Sensor Fault Detection Models for Induction Motors Using Algebraic Equations

This paper presents an algebraic framework for detecting steady-state sensor faults in induction motors. Existing adaptive observer methods for identifying rotor speed sensor failures often lack clear analytical grounding and experimental validation. To address this, we develop a systematic algebraic formulation that isolates residuals and identifies sensor inconsistencies through polynomial relationships among stator currents, voltages, and flux variables. The proposed approach yields two distinct yet acceptable motor model remedies-corresponding to symmetrical algebraic solutions-both capable of accurately reconstructing rotor velocity and stator winding parameters. These dual solutions reflect the inherent structural symmetry in induction motor behavior during steady-state fault conditions. Simulation outcomes demonstrate that the algebraic approach achieves robust detection accuracy and residual sensitivity under varying resistive loads. The study further validates that an observable increase in residual error ensures reliable fault identification, thereby improving the resilience and diagnostic precision of adaptive observers in speed sensor fault scenarios.

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