Jean Faucher

On-Line Monitoring of Mechanical Faults in Variable-Speed Induction Motor Drives Using the Wigner Distribution

This paper deals with the detection of mechanical load faults in induction motors during speed transients. The detection strategy is based on stator current analysis. Mechanical load faults generally lead to load torque oscillations at specific frequencies related to the mechanical rotor speed. The torque oscillations produce a characteristic sinusoidal phase modulation of the stator current. Speed transients result in time-varying supply frequencies that prevent the use of classical, Fourier transform-based spectral estimation. This paper proposes the use of a time-frequency distribution, the Wigner Distribution, for stator current analysis. Fault indicators are extracted from the distribution for on-line condition monitoring. The proposed methods are implemented on a low-cost digital signal processor. Experimental results in a steady-state and during transients with load torque oscillations and load imbalance are presented.

Distinguishing Load Torque Oscillations and Eccentricity Faults in Induction Motors Using Stator Current Wigner Distributions

This paper proposes a novel diagnosis method for detection and discrimination of two typical mechanical failures in induction motors by stator current analysis: load torque oscillations and dynamic rotor eccentricity. A theoretical analysis shows that each fault modulates the stator current in a different way: torque oscillations lead to stator current phase modulation, whereas rotor eccentricities produce stator current amplitude modulation. The use of traditional current spectrum analysis involves identical frequency signatures with the two fault types. A time-frequency analysis of the stator current with the Wigner distribution leads to different fault signatures that can be used for a more accurate diagnosis. The theoretical considerations and the proposed diagnosis techniques are validated on experimental signals.

Comparison Between Stator Current and Estimated Mechanical Speed for the Detection of Bearing Wear in Asynchronous Drives

This paper deals with the detection of worn rolling bearings in asynchronous machines using electrical measurements and estimated mechanical variables. These two approaches are based on the use of the available electrical quantities, e.g., the machine stator currents, which are often already measured for control and protection purposes. Considering that bearing faults induce load-torque oscillations, a theoretical stator-current model, in case of load-torque oscillations, is recalled. Then, a theoretical estimated rotor flux and estimated speed model demonstrates the presence of harmonics related to load-torque oscillations. Phase-modulation components on stator current and harmonics on estimated speed can be used for detection purposes. The frequency behavior of monitored quantities with regard to the load-torque oscillation frequency is particularly investigated. Fault detectors are then proposed on monitored variables. The efficiency of the indicators is studied for different operating points considering the frequency behavior of the system. Finally, the use of detectors is discussed regarding the supply-frequency range usable for the detection.

On-line parameter estimation of PMSM in open loop and closed loop

The paper discusses the multi-models approach for on-line parameter estimation of permanent magnet synchronous motor (PMSM). The study is focused on a PMSM with non sinusoidal electromotive force (emf) in open loop and closed loop conditions. The electrical parameters of 2-phases models in Extended Park frame are estimated by using the recursive least squares algorithm (RLS).

Indicator for bearing fault detection in asynchronous motors using stator current spectral analysis

This paper deals with the application of motor current spectral analysis for the detection of artificially damaged rolling bearings in asynchronous machine. Vibration monitoring of mechanical characteristic frequencies related to the bearings is widely used to detect faulty operations. However, vibration measurement is expensive and can not always be performed. An alternative is to base the monitoring on the available electrical quantities e.g. the machine stator current which is often already measured for control and protection purposes. The bearing faults reveal the presence of mechanical load torque oscillations. A theoretical stator current model in case of load torque oscillations demonstrates the presence of phase modulation. Related sideband components appear in the current spectrum and can be used for detection. Experimental measurements show that their amplitudes are linked to the fault frequency by a transfer function including resonance. This singularity will be used to improve the detection efficiency. Fault detectors using the energy of stator current in specific frequency ranges are then proposed. The efficiency of indicators is studied on long and short data records of experimental current for different bearing faults. The most significant of the investigated indicators is finally improved to guarantee a higher reliability of the detection.

Wigner distribution for the diagnosis of high frequency amplitude and phase modulations on stator currents of induction machine

This paper deals with mechanical fault monitoring in induction machines from stator current measurements. The considered faults lead to amplitude and/or phase modulations of the measured stator current. The different faults can be characterized by their time-frequency signatures via the Wigner distribution. The time-frequency representations apply to complex signals that may be obtained through the Hubert transform of the real measured signal. In case of high frequency modulations, it can not be taken advantage of the time frequency signatures. This study proposes an alternative complex signal representation for modulated stator currents. The so-called space vector is obtained through the Concordia transform. From three stator current measurements, the Concordia transform builds a complex vector which conveniently carries the information about phase and amplitude modulations. This paper applies and compares the Wigner distribution computed with the Hilbert and Concordia transforms in case of simulated and experimental signals with various modulation frequency ranges.

Maximum-Likelihood Parameter Estimation for Current-Based Mechanical Fault Detection in Induction Motors

This paper proposes a new method for mechanical fault detection in induction motors. The detection strategy is based on the estimation of a particular stator current parameter. The considered mechanical faults cause periodic load torque oscillations leading to a sinusoidal phase modulation of the stator current. The modulation index is related to the fault severity and can be used as a fault indicator. First, a simplified stator current model is proposed. The problem is then equivalent to the parameter estimation of a sinusoidal phase mono-component signal. Second, the maximum likelihood estimator is implemented using evolution strategies for optimization. The Cramer-Rao lower bounds are calculated and compared to the estimator performance through simulations. The estimation procedure is studied on experimental stator current signals from faulty and healthy motors