There are several applications where the motor is operating in continuous nonstationary operating conditions. Actuators and servo motors in the aerospace and transportation industries are examples of this kind of operation. Detection of faults in such applications is, however, challenging because of the need for complex signal processing techniques. Two novel methods using windowed Fourier ridges and Wigner-Ville-based distributions are proposed for the detection of rotor faults in brushless dc motors operating under continuous nonstationarity. Experimental results are presented to validate the concepts and illustrate the ability of the proposed algorithms to track and identify rotor faults. The proposed algorithms are also implemented on a digital signal processor to study their usefulness for commercial implementation.

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Abstract-A new method using the analytic wavelet transform of the stator-current signal is proposed for detecting dynamic eccentricity in brushless direct current (BLDC) motors operating under rapidly varying speed and load conditions. As wavelets are inherently suited for nonstationary signal analysis, this method does not require the use of any windows, nor is it dependent on any assumption of local stationarity as in the case of the short-time Fourier transform. The proposed technique uses analytic wavelets, which are smooth wavelets that possess both magnitude and phase information. This makes them particularly suitable for motorfault diagnostics. Experimental results are provided to show that the proposed method works over a wide speed range of motor operation and provides an effective and robust way of detecting rotor faults such as dynamic eccentricity in BLDC motors.