An automatic rotor bar fault diagnosis using fuzzy logic and DWT-energy for backstepping control driven induction motor in low-speed operation

This project looks at how to detect and classify rotor‑bar faults in induction‑motor drives that run at low speed and with changing load torque. To handle uncertainties and keep speed tracking reliable at low speeds, we use a non‑linear backstepping control algorithm. The diagnostic method applies the discrete wavelet transform (DWT) to break down the speed regulator’s output signal. It extracts energy‑approximation eigenvalues that represent fault signatures. Those energy features are fed into a fuzzy‑logic classifier, which evaluates and categorizes the severity of rotor‑bar faults, especially under low‑load or no‑load conditions. We will test the combined control and diagnostic system with MATLAB/Simulink simulations and real‑time hardware‑in‑the‑loop experiments using a dSPACE

DS1104 controller board. This work gives clear guidance for analyzing transient motor signatures and provides a reliable way to monitor machine condition and design fault‑tolerant control systems. By focusing on the speed regulator output instead of stator‑current signatures, the method avoids the usual problems of fault detection at low speed and low load.

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