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In this paper we develop a fault detection and isolation method based on data-driven approach. Data-driven methods are effective for feature extraction and feature analysis using statistical techniques. In the proposal, the Cumulated Sum (CUSUM) efficiency is explored for incipient fault detection. The fault is assumed to be a Gain variation, an Offset evolution, a Phase shifting or one of the multiple...
This paper proposes a method for voltage dip fault voltage detection and diagnosis in a grid connected Wind Turbine Generator. The method is data-driven. From the measurements of the currents flowing into the grid, three features related to the trajectory of the current vector in the Concordia stationary reference frame are extracted. The evaluation of the features for fault diagnosis is done through...
This research deals with the discrimination between conditions of faults in rolling element bearings based on a global spectral analysis. This global spectral analysis allows to obtain spectral features with significant discriminatory power. These features are extracted from the envelope spectra of vibration signals without prior knowledge of the bearings specific parameters and the characteristic...
Informative features having important discriminatory power, also called high-level features, for classification of bearings faults can be automatically extracted from the spectrum of the vibration signal envelope without the a prior knowledge of the characteristic bearing frequencies. It was shown in a previous work that the Principal Component Analysis (PCA), when applied on a specific spectral matrix...
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