Article

Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip

Dept. of Electr. Eng., Univ. Politec. de Valencia, Valencia
IEEE Transactions on Energy Conversion (Impact Factor: 3.35). 04/2009; 24(1):52 - 59. DOI: 10.1109/TEC.2008.2003207
Source: IEEE Xplore

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.

0 Bookmarks
 · 
76 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We propose a new model order and spectral estimation technique to detect fault frequency signatures.•The proposed technique is asymptotically optimal and exhibits high-resolution capabilities.•We demonstrate the appropriateness of the approach on major faults in an induction machine.•We prove the effectiveness of the proposed technique on simulated and experimental data.
    Mechanical Systems and Signal Processing 02/2015; 52. DOI:10.1016/j.ymssp.2014.06.015 · 1.91 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper deals with the comprehensive detailed concepts of the rotor broken bars fault in industrial induction motors. It reviews the most important and applicable techniques for fault detection, and addresses fault diagnosing procedures at different supply modes including line-start and inverter-fed modes. Moreover, new analytical and experimental aspects of fault are proposed using the time and frequency domain variations of the motor variables such as current, voltage, electromagnetic torque and speed. Since the faulty motor behavior cannot be correctly identified without considering the motor operating condition, and the capability of the previous fault indicators are studied deeply in order to investigate their applicability at different conditions. These conditions include various faults, load and reference speed levels and also fault location. All in all, a precise condition assessment of the rotor broken bar induction motors, suitable for industrial purposes, is presented considering motor supply and conditions changes.
    Mechanical Systems and Signal Processing 02/2015; 54-55. DOI:10.1016/j.ymssp.2014.08.022 · 2.47 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Because of the extensive application of the non-Linear electrical equipment, aircraft power system contains a number of harmonic and inter-harmonic components. Aiming at the situation that Fourier transform and wavelet transform method could not effectively extract and analyze these harmonic and inter-harmonic signal, made use of Hilbert-Huang Transform (HHT) to extract and analyze harmonic and inter-harmonic components for the aircraft power system. Meanwhile, in order to solve the question of overshoots/undershoots existing during empirical mode decomposition(EMD), made use of cubic Hermite polynomial interpolation instead of cubic spline interpolation to extract the envelope curve. The result of simulation shows that HHT can effectively extract and analyze harmonic and inter-harmonic components for the aircraft power system.
    2011 Second International Conference on Digital Manufacturing and Automation (ICDMA); 08/2011