L
Lotfi Saidi
Researcher at Tunis University
Publications - 47
Citations - 2106
Lotfi Saidi is an academic researcher from Tunis University. The author has contributed to research in topics: Bearing (mechanical) & Prognostics. The author has an hindex of 14, co-authored 42 publications receiving 1532 citations. Previous affiliations of Lotfi Saidi include University of Sousse & Centre national de la recherche scientifique.
Papers
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Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
TL;DR: In this article, a mathematical analysis to select the most significant intrinsic mode functions (IMFs) is presented, and the chosen features are used to train an artificial neural network (ANN) to classify bearing defects.
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Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network
Jaouher Ben Ali,Jaouher Ben Ali,Brigitte Chebel-Morello,Lotfi Saidi,Simon Malinowski,Farhat Fnaiech +5 more
TL;DR: In this article, a combination of simplified fuzzy adaptive Resonance theory map (SFAM) neural network and Weibull distribution (WD) is explored to predict the remaining useful life (RUL) of rolling element bearings.
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Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR
TL;DR: In this paper, a vibration-based prognostic and health monitoring methodology for wind turbine high-speed shaft bearing (HSSB) is proposed using a spectral kurtosis (SK) data-driven approach.
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Application of higher order spectral features and support vector machines for bearing faults classification
TL;DR: A novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier, which indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.
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Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis
TL;DR: First, original vibration signals collected from accelerometers are decomposed by EMD and a set of intrinsic mode functions (IMFs) is produced and the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects.