Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip
ABSTRACT This paper proposes an online/offline induction motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms, based on the Hilbert transform. MCSA is a method for motor diagnosis with stator-current signals. Although it is one of the most powerful online methods for diagnosing motor faults, it has some drawbacks that can degrade the performance and accuracy of a motor-diagnosis system. In particular, it is very difficult to detect broken rotor bars when the motor is operating at low slip or under no load, due to fast Fourier transform (FFT) frequency leakage and the small amplitude of the current components related to the fault. Therefore, advanced signal-and-data-processing algorithms are proposed. They consist of a proper sample selection algorithm, a Hilbert transformation of the stator-sampled current, and spectral analysis via FFT of the modulus of the resultant time-dependent vector modulus for achieving MCSA efficiently. Experimental results obtained on a 1.1 kW three-phase squirrel-cage induction motor are discussed.
Conference Proceeding: Improvement of frequency resolution for three-phase induction machine fault diagnosis[show abstract] [hide abstract]
ABSTRACT: This paper deals with the use of the zoom FFT algorithm (ZFFTA) for the electrical fault diagnosis of squirrel-cage three-phase induction machines with a special interest in broken rotor bar situation. The machine stator current can be analysed to observe the side-band harmonics around the fundamental frequency. In this case, it is necessary to take a very long data sequence to get high frequency resolution. This is not always possible due to the hardware and software limitations. The proposed algorithm can be considered for solving high frequency resolution problem without increasing the initial data acquisition size. The ZFFTA is applied to detect incipient rotor fault in a three-phase squirrel-cage induction machine by using both stator current and stray flux sensors.Industry Applications Conference, 2005. Fourtieth IAS Annual Meeting. Conference Record of the 2005; 11/2005
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ABSTRACT: Previous work on condition monitoring of induction machines has focused on steady-state speed operation. Here, a new concept is introduced based on an analysis of transient machine currents. The technique centers around the extraction and removal of the fundamental component of the current and analyzing the residual current using wavelets. Test results of induction machines operating both as a motor and a generator shows the ability of the algorithm to detect broken rotor bars.IEEE Transactions on Energy Conversion 04/2005; · 2.43 Impact Factor
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ABSTRACT: Various applications of artificial intelligence (AI) techniques (expert systems, neural networks, and fuzzy logic) presented in the literature prove that such technologies are well suited to cope with on-line diagnostic tasks for induction machines. The features of these techniques and the improvements that they introduce in the diagnostic process are recalled, showing that, in order to obtain an indication on the fault extent, faulty machine models are still essential. Moreover, by the models, that must trade off between simulation result effectiveness and simplicity, it is possible to overcome crucial points of the diagnosis. With reference to rotor electrical faults of induction machines, a new and simple procedure based on a model which includes the speed ripple effect is developed. This procedure leads to a new diagnostic index, independent of the machine operating condition and inertia value, that allows the implementation of the diagnostic system with a minimum configuration intelligenceIEEE Transactions on Industry Applications 02/1998; · 1.67 Impact Factor