Axial cooling air ducts in the rotor of large induction motors are known to produce magnetic asymmetry, and can cause steady state current or vibration spectrum analysis based fault detection techniques to fail. If the number of axial air ducts and poles are identical, frequency components that overlap with that of rotor faults can be produced for healthy motors. False positive rotor fault indication due to axial ducts is a common problem in the field that results in unnecessary maintenance cost. However, there is currently no known test method available for distinguishing rotor faults and false indications due to axial ducts other than off-line rotor inspection or testing. Considering that there is no magnetic asymmetry under high slip conditions due to limited flux penetration into the rotor yoke, detection of broken bars under the startup transient is investigated in this paper. A wavelet-based detection method is proposed and verified on custom-built lab motors and 6.6 kV motors misdiagnosed with broken bars via steady state spectrum analysis. It is shown that the proposed method provides reliable detection of broken bars under the startup transient independent of axial duct influence.

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The diagnosis of induction motors through the spectral analysis of the stator current allows for the online identification of different types of faults. One of the major difficulties of this method is the strong influence of the mains component of the current, whose leakage can hide fault harmonics, especially when the machine is working at very low slip. In this paper, a new method for demodulating the stator current prior to its spectral analysis is proposed, using the Teager-Kaiser energy operator. This method is able to remove the mains component of the current with an extremely low usage of computer resources, because it operates just on three consecutive samples of the current. Besides, this operator is also capable of increasing the signal-to-noise ratio of the spectrum, sharpening the spectral peaks that reveal the presence of the faults. The proposed method has been deployed to a PC-based offline diagnosis system and tested on commercial induction motors with broken bars, mixed eccentricity, and single-point bearing faults. The diagnostic results are compared with those obtained through the conventional motor current signature analysis method.

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Unlike single cage rotor fault detection, FFTbased steady state spectrum analysis techniques can fail to detect outer cage faults in double cage induction motors due to the small outer cage current under running conditions. Double cage motors are typically employed in applications that require loaded starts. This makes the outer cage vulnerable to fatigue failure since it must withstand the high starting current and long startup time frequently. However, there are only a few publications that investigate detection techniques specifically for double cage motors. In this paper, considering that the influence of the faulty outer cage is strong at startup due to the large outer cage current, detection of outer cage faults under the startup transient is investigated. A Discrete Wavelet Transform-based method is proposed as a viable solution to detection of outer cage faults for double cage motors. An experimental study on fabricated copper double cage induction motors shows that the proposed method provides sensitive and reliable detection of double cage rotor faults compared to FFT.