Browsing by Author "ESSID Abderrahmane"

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    Analysis and Diagnostic Methods for Fault Detection in Industrial Three-Phase Asynchronous Machines
    (ECOLE NATIONALE SUPERIEURE DE TECHNOLOGIE ET D’INGENIERIE - ANNABA, 2025) GUERMOUZ Mohamed Amine; ESSID Abderrahmane; DEKHANE Azzeddine (Encadrant)
    This project recognizes a promising alternative diagnostic approach to fault detection in industrial three-phase asynchronous motors. These motors are significant for industrial operations in power plants and manufacturing businesses. As part of the work undertaken with SKD Power Plant and SIDER El Hadjar, more reliability and predictive maintenance have been achieved through the application of vibration analysis and Motor Current Signature Analysis (MCSA). The limitations of physical, visual inspections and reactive maintenance are acknowledged within the project. More importantly, real industrial measurements were performed as well as a modeling/simulation effort using MATLAB/Simulink. The presence of mechanical (bearing faults) and electrical (voltage unbalance) anomalies were both diagnosed. The combination of time-domain and frequency-domain analysis has provided evidence for early and accurate fault detection. The project has highlighted the benefits of using new, non-invasive diagnostic techniques in the industrial landscape of Algeria to support improved operational efficiencies and reduce the risk of unplanned outages.
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    Simulation of Various Faults in Electric Machines
    (National Higher School of Technology and Engineering - Annaba, 2025) GUERMOUZ Mohamed Amine; ESSID Abderrahmane; DEKHANE Azzeddine
    The performance and dependability of three-phase induction motors are integral to the continuing operations of many industrial processes. However, all machines are susceptible to electrical or mechanical faults such as a broken rotor bar, short circuit in the stator, and voltage unbalance, which can rapidly lead to damaging consequences and expensive downtime. The SKD factory, which is an integral portion of this study had not adopted an advanced motor testing or fault diagnostic approach, but a means of detecting and classifying faults would be a significant improvement when working with three-phase induction motors. This work provides results from a MATLAB/Simulink environment simulation approach of typical motor faults which analyses how the faults behaved dynamically when processed through current signals. A Fast Fourier Transform (FFT) was applied to extract key frequency signatures that represented the faults. This work serves as an example to convey potential opportunities for detecting faults. Unlike in-line motor tests, this study demonstrated how spectral analysis could be applied in a non-invasive nature, to provide motor fault diagnostics with the aim of developing predictive maintenance and improved reliability for the industry.