In this paper, a general methodology based on the application of the Discrete Wavelet Transform (DWT) to the diagnosis of cage motors condition using the transient stator currents is exposed. The approach is based on the identification of characteristics patterns introduced by fault components in the wavelets signals obtained from the DWT of transient stator currents. These signals enable a reliable detection of the corresponding fault, as well as a clear interpretation of the physical phenomenon taking place in the machine. A compilation of the application of the methodology to several fault cases such as the presence of rotor asymmetries or eccentricities, is done. Guidelines for the easy application of the methodology by any user are also provided under a didactic perspective.
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS. But a fine tuning of their parameters is needed in order to obtain a high resolution image of the fault in the time-frequency domain, and they also require a much higher processing effort than traditional diagnosis techniques, such as the Fourier Transform (FT). The new method proposed in this paper addresses both problems using the Gabor analysis of the current via the chirp z-transform (CZT), which can be easily adapted to generate high resolution time-frequency stamps of different types of faults. In this paper, it is used to diagnose broken bars of an IM using the current during a startup transient. This new approach is theoretically introduced and experimentally validated with a 1.1 kW commercial motor in faulty and healthy conditions.
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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