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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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Condition based maintenance systems (CBM) of induction machines (IMs) require fast and accurate models that can reproduce the fault related harmonics generated by different kinds of faults, in order to help in developing new diagnostic algorithms for detecting the faults at an early stage, to analyse the physical interactions between simultaneous faults of different types, or to train expert systems that can supervise and identify these faults in an autonomous way. To achieve these goals, such models must take into account the space harmonics of the air gap magnetomotive force (MMF) generated by the machine windings under fault conditions, due to the complex interactions between spatial and time harmonics in a faulty machine. One of the most common faults in induction machines is the rotor eccentricity, which can cause significant radial forces and, in extreme cases, produce destructive rotor-stator rub. But the development of a fast, analytic model of the eccentric IM is a challenging task, due to the nonuniformity of the air gap. In this paper, a new method is proposed to obtain such a fast model. This method, which is theoretically justified, enables a fast calculation of the self and mutual inductances of the stator and rotor phases for every rotor position. The proposed method is validated first using a finite elements method (FEM) model, and then, through an experimental test-bed using commercial induction motors with a forced mixed eccentricity fault.

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The diagnosis of induction machines through the use of methods based on the study of the startup current has become an issue of special interest. These techniques may provide, in certain situations (unbalanced supply voltages, load torque oscillations, variable load…) and for certain faults (broken bars, eccentricity, stator short circuit,) substantial advantages in comparison with the classical method, based on the Fourier spectrum of the steady-state current. Nevertheless, in the case of rotor asymmetries, these transient-based techniques have been mainly focused on the tracing of the lower sideband harmonic (LSH). In this paper, a wideband diagnosis method is proposed, in which the Wigner-Ville distribution is applied to the detection of eccentricity and other high-order components also introduced by the rotor asymmetry. It is shown that the proposed wide band analysis might help to reach a more reliable diagnosis conclusion in cases in which the tracing of commonly used harmonics may be difficult (inter-bar currents, load torque oscillations, non stationary regimes…). An evaluation 2 of the method is carried out through simulations and laboratory tests. The results show the potential of the tool for the detection and quantification of these components as a basis to diagnose such faults.