Diagnosis of Three-Phase Electrical Machines Using Multidimensional Demodulation Techniques

International audienceThis paper deals with the diagnosis of three-phase electrical machines and focuses on failures that lead to stator- current modulation. To detect a failure, we propose a new method based on stator-current demodulation. By exploiting the configuration of three-phase machines, we demonstrate that the demodulation can be efficiently performed with low-complexity multidimensional transforms such as the Concordia transform (CT) or the principal component analysis (PCA). From a practical point of view, we also prove that PCA-based demodulation is more attractive than CT. After demodulation, we propose two statistical criteria aiming at measuring the failure severity from the demodulated signals. Simulations and experimental results highlight the good performance of the proposed approach for condition monitoring

Condition Monitoring of Induction Motors Based on Stator Currents Demodulation

International audienceOver the past several decades, induction machine condition monitoring have received increasing attention from researchers and engineers. Several induction machine faults detection techniques have been proposed that are based on vibration, temperature, and currents/power monitoring, etc. Motor current signature analysis is a cost-effective method, which has been widely investigated. Specifically, it has been demonstrated that mechanical and electrical induction machine faults can be effectively diagnosed using stator currents demodulation. Therefore, this paper proposes to investigate the use of demodulation techniques for bearing faults detection and diagnosis based on stator currents analysis. If stator currents are assumed to be mono-component signals, the demodulation techniques include the synchronous demodulator, the Hilbert transform, the Teager energy operator, the Concordia transform, the maximum likelihood approach and the principal component analysis. For a multi-component signal, further preprocessing techniques are required such as the Empirical Mode Decomposition (EMD) or the Ensemble EMD (EEMD). The studied demodulation techniques are demonstrated for bearing faults diagnosis using simulation data, issued from a coupled electromagnetic circuits approach-based simulation tool, and experiments on a 0.75kW induction machine test bed

Stator current demodulation for induction machine rotor faults diagnosis

International audienceSeveral studies have demonstrated that induction machine faults introduce phase and/or amplitude modulation of the stator currents. Hence, demodulation of the stator currents is of high interest for induction machines faults detection and diagnosis. The demodulation techniques can be classified into mono-dimensional and multi-dimensional approaches. The monodimensional techniques include the synchronous demodulator, the Hilbert transform, the Teager energy operator and other approaches. The multi-dimensional approaches include the Concordia transform and the Principal Component Analysis. Once the demodulation has been performed, demodulated signals are further processed in order to measure failure severity. In this paper, we present a comprehensive comparison of these demodulation techniques for eccentricity and broken rotor bars faults detection

EEMD-based windturbinebearingfailuredetectionusing the generatorstatorcurrenthomopolarcomponent

International audienceFailure detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a failure detection techniques based on the homopolar component of the generator stator current and attempts to highlight the use of the ensemble empirical mode decomposition as a tool for failure detection in wind turbine generators for stationary and non stationary cases

Performance Analysis of an EEMD-based Hilbert Huang Transform as a Bearing Failure Detector in Wind Turbines

International audienceSustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. The most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the degeneration of the generator health, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper deals then with the assessment of a demodulation technique for bearing failure detection through wind turbines generator stator current. The proposed technique is based on a modified version of the Hilbert Huang transform. In this version, the use of the EEMD algorithm allows overcoming the well-known mixed mode

Bearing Fault Diagnosis for Direct-Drive Wind Turbines via Current-Demodulated Signals

Bearing faults account for a large portion of all faults in wind turbine generators (WTGs). Current-based bearing fault diagnosis techniques have great economic benefits and are potential to be adopted by the wind energy industry. This paper models the modulation effects of bearing faults on the stator currents of a direct-drive wind turbine equipped with a permanent-magnet synchronous generator (PMSG) operating with a variable shaft rotating frequency. Based on the analysis, a method consisting of appropriate current frequency and amplitude demodulation algorithms and a 1P-invariant power spectrum density algorithm is proposed for bearing fault diagnosis of variable-speed direct-drive wind turbines using only one-phase stator current measurements, where 1P frequency stands for the shaft rotating frequency of a wind turbine. Experimental results on a direct-drive wind turbine equipped with a PMSG operating in a wind tunnel are provided to verify the proposed fault diagnosis method. The proposed method is demonstrated to have advantages over the method of directly using stator current measurements for WTG bearing fault diagnosis

Induction Machine Diagnosis using Stator Current Advanced Signal Processing

International audienceInduction machines are widely used in industrial applications. Safety, reliability, efficiency and performance are major concerns that direct the research activities in the field of electrical machines. Even though the induction machines are very reliable, many failures can occur such as bearing faults, air-gap eccentricity and broken rotor bars. Therefore, the challenge is to detect them at an early stage in order to prevent breakdowns. In particular, stator current-based condition monitoring is an extensively investigated field for cost and maintenance savings. In fact, several signal processing techniques for stator current-based induction machine faults detection have been studied. These techniques can be classified into: spectral analysis approaches, demodulation techniques and time-frequency representations. In addition, for diagnostic purposes, more sophisticated techniques are required in order to determine the faulty components. This paper intends to review the spectral analysis techniques and time-frequency representations. These techniques are demonstrated on experimental data issued from a test bed equipped with a 0.75 kW induction machine. Nomenclature O&M = Operation and Maintenance; WTG = Wind Turbine Generator; MMF = Magneto-Motive Force; MCSA = Motor Current signal Analysis; PSD = Power Spectral Density; FFT = Fast Fourier Transform; DFT = Discrete Fourier Transform; MUSIC = MUltiple SIgnal Characterization; ESPRIT = Estimation of Signal Parameters via Rotational Invariance Techniques; SNR = Signal to Noise Ratio; MLE = Maximum Likelihood Estimation; STFT = Short-Time Fourier Transform; CWT = Continuous Wavelet Transform; WVD = Wigner-Ville distribution; HHT = Hilbert-Huang Transform; DWT = Discrete Wavelet Transform; EMD = Empirical Mode Decomposition; IMF = Intrinsic Mode Function; AM = Amplitude Modulation; FM = Frequency Modulation; IA = Instantaneous Amplitude; IF = Instantaneous Frequency; í µí± ! = Supply frequency; í µí± ! = Rotational frequency; í µí± ! = Fault frequency introduced by the modified rotor MMF; í µí± ! = Characteristic vibration frequencies; í µí± !"# = Bearing defects characteristic frequency; í µí± !" = Bearing outer raceway defect characteristic frequency; í µí± !" = Bearing inner raceway defect characteristic frequency; í µí± !" = Bearing balls defect characteristic frequency; í µí± !"" = Eccentricity characteristic frequency; í µí± ! = Number of rotor bars or rotor slots; í µí± = Slip; í µí°¹ ! = Sampling frequency; í µí± = Number of samples; í µí±¤[. ] = Time-window (Hanning, Hamming, etc.); í µí¼ = Time-delay; í µí¼ ! = Variance; ℎ[. ] = Time-window

Enhanced Frequency Adaptive Demodulation Technique For Grid-Connected Converters

This paper presents an enhanced frequency adaptive demodulation technique for grid-synchronization of grid-connected converters (GCC) in variable frequency condition. Demodulation works by generating demodulated voltages which contain undesired double frequency components. As a result, high-order low-pass filters (LPF) with high cutoff frequency are required to eliminate the undesired components. This reduces the dynamic performance. Frequency adaptive demodulation technique enhances the dynamic performance by rejecting the double frequency components as opposed to filtering, however, at the cost of additional computational complexity. This paper overcomes this problem by using double demodulation without recreating the double frequency component for rejection purpose. This reduces the computational complexity significantly. Suitability of proposed method is verified through numerical simulation and experimental study. Comparative study with existing frequency adaptive demodulation and second-order generalized integrator phase-locked loop (SOGI-PLL) techniques demonstrate the validity and performance improvement by the proposed technique

Stator Current Analysis by Subspace Methods for Fault Detection in Induction Machines

International audienceThis paper aims to develop a condition monitoring architecture for induction machines, with focus on bearing faults. The main objective of this paper is to identify fault signatures at an early stage by using high-resolution frequency estimation techniques. In particular, we present two subspace methods, which are Root-MUSIC and ESPRIT. Once the frequencies are determined, the amplitude estimation is obtained by using the Least Squares Estimator (LSE). Finally, the amplitude estimation is used to derive a fault severity criterion. The experimental results show that the proposed architecture has the ability to measure the faults severity