El Houssin El Bouchikhi

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Development of large scale offshore wind and marine current turbine farms implies to minimize and predict maintenance operations. In director indirect-drive, fixedor variable-speed turbine generators, advanced signal processing tools are required to detect and diagnose the generator faults from vibration, acoustic, or generator current signals. The(More)
Fault detection in squirrel cage induction machines based on stator current spectrum has been widely investi­ gated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. In this paper, a modified version of MUSIC algorithm has been developed based on the faults(More)
Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are(More)
To reduce the cost of wind energy, minimization and prediction of maintenance operations in wind turbine is of key importance. In variable speed turbine generator, advanced signal processing tools are required to detect and diagnose the generator faults from the stator current. To detect a fault in non-stationary conditions, previous studies have(More)
Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are(More)
This paper focuses on the estimation of the instantaneous amplitude, phase, and unbalance parameters in three-phase power systems. Due to the particular structure of three-phase systems, we demonstrate that the maximum-likelihood estimates (MLEs) of the unknown parameters have simple closed-form expressions and can be easily implemented without matrix(More)
This 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(More)
Several 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(More)
Induction machines are widely used in industrial applications. Safety, reliability, efficiency and performance are major concerns that direct the research activity in the field of electrical machines. Though the induction machine is very reliable, many failures can occur such as bearing faults, air-gap eccentricity and broken rotor bars. Therefore, the(More)
Over 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(More)
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