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This paper investigates a new fault detection method for induction machines diagnosis. The proposed detection method is based on hypothesis testing. The decision is made between two hypotheses: the machine is healthy and the machine is faulty. The generalized likelihood ratio test is used to address this issue with unknown signal and noise parameters. To implement this detector, the unknown parameters are replaced by their estimates. Specifically, four estimations are required, which are model order, frequency, phase, and amplitude estimations. The model order is obtained using the Bayesian information criterion. Total least-squares estimation of signal parameters via rotational invariance techniques is used to estimate frequencies. Then, phases and amplitudes are obtained using the least-squares estimator. The proposed approach performance is assessed using simulation data by plotting the receiver operating characteristic curves. Two faults are considered: bearing and broken rotor bar faults. Experimental tests clearly show the effectiveness of the proposed detector. [ABSTRACT FROM PUBLISHER]

Broken rotor bars in induction motors can be dependably detected by analyzing the current signatures under sufficient motor load conditions. Detection becomes less dependable under light motor load conditions. There are also cases in which tolerable motor operating conditions generate current signatures similar to those of motors with broken rotor bars. These cases may present security concerns when the detection element is set to trip the motor and to send alarms. In this paper, we aim to achieve the following: show how broken rotor bars cause characteristic current signatures; show how to detect broken rotor bars with a zero-setting protection element, which uses the current signature method; use cases with different motor operating and fault conditions to analyze the performance of the zero-setting broken bar protection element; identify cases when the current signature method is dependable and cases when security is a concern, and present solutions to address security concerns. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to present a new approach for rotor bar failure diagnosis in induction machines. The method focuses on the study of an approximation signal resulting from the wavelet decomposition of the startup stator current. The presence of the left sideband harmonic is used as evidence of the rotor failure in most diagnosis methods based on the analysis of the stator current. Thus, a detailed description of the evolution of the left sideband harmonic during the startup transient is given in this paper; for this purpose, a method for calculating the evolution of the left sideband during the startup is developed, and its results are physically explained. This paper also shows that the approximation signal of a particular level, which is obtained from the discrete wavelet transform of the startup stator current, practically reproduces the time evolution of the left sideband harmonic during the startup. The diagnosis method proposed here consists of checking if the selected approximation signal fits well the characteristic shape of the left sideband harmonic evolution described in this paper. The method is validated through laboratory tests. The results prove that it can constitute a useful tool for the diagnosis of rotor bar breakages. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a method for the diagnosis of rotor bar failures in induction machines, based on the analysis of the stator current during the startup using the discrete wavelet transform (DWT). Unlike other approaches, the study of the high-order wavelet signals resulting from the decomposition is the core of the proposed method. After an introduction of the physical and mathematical bases of the method, a description of the proposed approach is given; for this purpose, a numerical model of induction machine is used in such a way that the effects of a bar breakage can clearly be shown, avoiding the influence of other phenomena not related with the fault. Afterward, the new diagnosis method is validated using a set of commercial induction motors. Several experiments are developed under different machine conditions (healthy machine and machine with different levels of failure) and operating conditions (no load, full load, pulsating load, and fluctuating voltage). In each case, the results are compared with those obtained using the classical approach, based on the analysis of the steady-state current using the Fourier transform. Finally, the results are discussed, and some considerations about the influence of the DWT parameters (type of mother wavelet, order of the mother wavelet, sampling rate, or number of levels of the decomposition) over the diagnosis are done. [ABSTRACT FROM AUTHOR]

A new algorithm is introduced for motor current signature analysis of induction machines operating during transients. The algorithm is able to extract the amplitude, phase and frequency of a single sinusoid embedded in a nonstationary wave- form. The algorithm is applied to the detection of broken rotor bars in induction machines during startup transients. The fundamental component of current, which varies in amplitude, phase, and frequency, is extracted using the algorithm. The residual current is then analyzed using wavelets for the detection of broken rotor bars. This method of condition monitoring does not require parameters such as speed or number of rotor bars, is not load dependent and can be applied to motors that operate continuously in the transient mode, e.g., wind generators or motor-operated valves. [ABSTRACT FROM AUTHOR]