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Lahouari Cheded

Researcher at King Fahd University of Petroleum and Minerals

Publications -  78
Citations -  860

Lahouari Cheded is an academic researcher from King Fahd University of Petroleum and Minerals. The author has contributed to research in topics: Kalman filter & Fault detection and isolation. The author has an hindex of 12, co-authored 75 publications receiving 735 citations.

Papers
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Journal ArticleDOI

Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques

TL;DR: In this article, wavelet transform tools are considered as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored.
Journal ArticleDOI

A new modified particle swarm optimization algorithm for adaptive equalization

TL;DR: A novel modification to the standard particle swarm optimization (PSO) technique is presented and the superiority of the proposed modified technique over other PSO-based techniques is illustrated, with an application to the important area of adaptive channel equalization.
Journal ArticleDOI

Kalman filter for parametric fault detection: an internal model principle-based approach

TL;DR: In this article, an internal model principle-based Kalman filter (IMP-KF) structure was proposed for use in the detection of parametric faults, and the proposed scheme was successfully evaluated on both simulated and physical systems.
PatentDOI

Unified approach to detection and isolation of parametric faults using a kalman filter residual-based approach

TL;DR: The unified fault detection and isolation method is successfully evaluated on both simulated data as well as on real data obtained from a benchmarked laboratory-scale coupled-tank system used to exemplify an industrial two-tank process.
Proceedings ArticleDOI

Measurement error sensitivity analysis for detecting and locating leak in pipeline using ANN and SVM

TL;DR: This paper presents an approach for detecting, locating and estimating the size of leak in a pipeline using pressure sensors, differential pressure sensors and flow-rate sensors that detect small change in pressure to overcome the problem with existing approaches.