Estimation Spectrale Paramétrique Dédiée au Diagnostic de la Génératrice Asynchrone dans un Contexte Éolien

National audienceLe développement des éoliennes o shores et des hydroliennes implique la nécessité de minimiser et de prévoir les opérations de maintenance. Par conséquent, des techniques de traitement de signal avancées sont requises pour détecter la présence et diagnostiquer une défaillance à partir de mesures vibratoires, acoustiques, ou à travers l'acquisition des courants statoriques. La génératrice asynchrone est largement utilisées dans les systèmes éoliens. Malgré sa robustesse et sa fiabilité, la machine asynchrone est assujettie à des défaillances diverses et variées. L'objectif est donc de les détecter à un stade précoce afin de prévenir d'éventuelles pannes et d'assurer la continuité de la production d'énergie. Cet article s'intéresse donc à la détection des défauts des génératrices asynchrones en se basant sur l'analyse des courants statoriques. Par ailleurs, un schéma de détection et caractérisation des défauts est proposé et ses performances analysées. L'intérêt de cette nouvelle approche est démontré en utilisant des données de simulation issus d'un modèle de la génératrice basé sur les circuits électriques magnétiquement couplés pour la détection des défauts de rupture de barres et d'excentricité mécaniques

Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines

International audienceCost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for failure detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed failure detection technique has been validated experimentally regarding bearing failures. Indeed, a large fraction of wind turbine downtime is due to bearing failures, particularly in the generator and gearbox

A Fitted empirical demodulation for low frequency electrical signal evaluation

Empirical Demodulation (ED) is a technique used to build a discrete signal, called an empirical envelope, from a modulated time sequence. For example, in three-phase induction motors, this envelope can carry fault frequency data that allows the machine health status to be evaluated during a spectral analysis. However, due to mathematical reasons, the method is very sensitive to the amplitude oscillations within the signal. When these oscillations are unwanted, as in the presence of measurement noise, the results can be strongly affected. This work proposes an iterative and adjustable version of the ED that considerably reduces its sensitivity to the presence of high frequency noise, thus eliminating the need for signal pre-filtering. To prove the effectiveness of the Fitted Empirical Demodulation, the authors applied the new tool in motor current signals for analysis of the rotor bars conditions

A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research

A Novel Transform Demodulation Algorithm for Motor Incipient Fault Detection

Faults, such as broken rotor bars, in induction motors may be detected by estimating the spectral signature of the stator currents, particularly the sidebands around the supply line frequency. However, the amplitude of the fundamental frequency (50 Hz) is considerably greater than the sideband amplitude. How to demodulate the signature frequency components under the heavy background of fundamental frequency, or how to remove the fundamental frequency, is becoming a key problem in motor current signature analysis. This paper puts forward a novel transform demodulation algorithm to solve the problem. The three-phase currents are transformed to a magnetic-torque (M-T) coordinate using this algorithm. It is found that the signature frequency components are demodulated in the magnetizing and torque-producing currents obtained by the transformation. Thus, the two demodulated M-T currents can be used to extract the enhanced signature frequency components of faults, and the incipient fault detection of induction motors is easy to realize. With both simulated and experimental data of broken rotor bars, it shows that the proposed algorithm can extract more detailed fault signature frequency components and realize the incipient fault detection of induction motors

Motor Fault Diagnosis Using Higher Order Statistical Analysis of Motor Power Supply Parameters

Motor current signature analysis (MCSA) has been an effective method to monitor electrical machines for many years, predominantly because of its low instrumentation cost, remote implementation and comprehensive information contents. However, it has shortages of low accuracy and efficiency in resolving weak signals from incipient faults, such as detecting early stages of induction motor fault. In this thesis MCSA has been improved to accurately detect electrical and mechanical faults in the induction motor namely broken rotor bars, stator faults and motor bearing faults. Motor current signals corresponding to a healthy (baseline) and faulty condition on induction motor at different loads (zero, 25%, 50% and 75% of full load) were rearranged and the baseline current data were examined using conventional methods in frequency domain and referenced for comparison with new modulation signal bispectrum. Based on the fundamental modulation effect of the weak fault signatures, a new method based on modulation signal bispectrum (MSB) analysis is introduced to characterise the modulation and hence for accurate quantification of the signatures. This method is named as (MSB-SE). For broken rotor bar(BRB), the results show that MSB-SE suggested in this research outperforms conventional bispectrum CB significantly for all cases due its high performance of nonlinear modulation detection and random noise suppression, which demonstrates that MSB-SE is an outstanding technique whereas (CB) is inefficient for motor current signal analysis [1] . Moreover the new estimators produce more accurate results at zero, 25%, 50%, 75% of full load and under broken rotor bar, compared with power spectrum analysis. Especially it can easily separate the half BRB at a load as low as 25% from baseline where PS would not produce a correct separation. In case of stator faults, a MSB-SE is investigated to detect different severities of stator faults for both open and short circuit. It shows that MSB-SE has the capability to accurately estimate modulation degrees and suppress the random and non-modulation components. Test results show that MSB-SE has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of power spectrum (PS). For motor bearing faults, tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high noise levels, MSB-SE is used to detect and diagnose different motor bearing defects. The results show that bearing faults can induce detectable amplitude increases at its characteristic frequencies. MSB-SE peaks show a clear difference at these frequencies whereas the conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in detecting small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also shows that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component. This research also applies a mathematical model for the simulation of current signals under healthy and broken bars condition in order to further understand the characteristics of fault signature to ensure the methodologies used and accuracy achieved in the detection and diagnosis results. The results show that the frequency spectrum of current signal outputs from the model take the expected form with peaks at the sideband frequency and associated harmonics

FAULT DIAGNOSIS OF MECHANICAL SYSTEMS BASED ON ELECTRICAL SUPPLY CHARACTERISTICS

Induction motors are the main workhorses of industry. Condition monitoring (CM) of motor based systems plays an important role in the early detection of possible defects, averting adverse operational and financial effects caused by unexpected breakdowns. Limited information has been found which explores the diagnostic abilities of voltage and motor current signals from motors with variable speed drives (VSDs), which are increasingly used in industry to obtain better dynamic response, higher efficiency and lower energy consumption. This study addresses the gap identified by carrying out a systematic review of the monitoring of mechanical systems using induction motors with sensorless VSDs. Specifically, existing techniques often prove ineffective for common internal and external faults that develop in Induction motors. The primary aim is to extract accurate diagnostic information from the power supply parameters of a VSD to monitor IM driven systems for early diagnosis of both mechanical and electrical faults. This thesis examines the effectiveness of both motor current and voltage signals using spectrum analysis for detecting broken rotor bar(s) and/or shaft misalignment and gear oil viscosity changes with different degrees of severities under sensorless control (close) modes. The results are obtained from common spectrum analysis applied to signals from a laboratory experimental setup operating under different speeds and loads. Evaluation of the results shows that the faults cause an increase in sideband amplitudes, which can be observed in both the current and voltage signals under the sensorless control mode. In addition, combined faults cause an additional increase in the sideband amplitudes and this increase can be observed in both the current and voltage signals. The voltage signals show greater change compared with the current signals because the VSD adapts the voltage supply source to compensate for changes in the system dynamics. Furthermore, this study also presents a model of an induction motorfed by a variable speed drive (VSD), as an approach to simulate broken rotor bars and shaft misalignments to give an in-depth understanding of fault signatures. The model was validated with experimental results in both current and voltage signals, with good agreement. The model confirmed that BRB causes a shift and increase in the amplitudes of the sidebands with the amplitudes of the rotor frequency components increased due to shaft misalignment