Software-Based Simulation for Fault Detection and Diagnosis in Electrical Machines Using MATLAB/Simulink

Mohammed Bappamaudo Suleiman; Ezra Husseini; Chibueze Okoro

doi:10.56201/ijemt.vol.11.no8.2025.pg252.259

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Software-Based Simulation for Fault Detection and Diagnosis in Electrical Machines Using MATLAB/Simulink

Mohammed Bappamaudo Suleiman, Ezra Husseini, Chibueze Okoro

Abstract

The reliability of electrical machines is vital in both academic and industrial applications. Frequent faults such as overheating, vibration, insulation breakdown, and electrical imbalances cause downtime, safety hazards, and costly maintenance. This study aims to develop a software- based simulation framework for fault detection and diagnosis (FDD) using MATLAB/Simulink. Specifically, the objectives are to (i) model common electrical and mechanical faults, (ii) simulate fault scenarios to analyze performance, and (iii) evaluate diagnostic strategies. The framework is anchored on the Condition-Based Maintenance Theory, which emphasizes continuous monitoring and predictive diagnostics. Using MATLAB/Simulink, the study incorporated fault injection models and sensor-based monitoring to detect anomalies through signal processing techniques. The findings revealed that the system achieved diagnostic accuracy of over 92% within 30 ms, demonstrating its real-time capability. The conclusion establishes that software-based FDD is cost-effective, improves safety, enhances predictive maintenance, and serves as a valuable tool for engineering laboratories and industrial training.

Keywords

Fault detectionElectrical machinesMATLAB/SimulinkSimulationPredictive

References

Bollen, M. H., & Hassan, F. (2011). Integration of distributed generation in the power system.Wiley-IEEE Press. Chen, Z., & Khorasani, K. (2017). Observer-based fault detection for electrical machines. IEEE Transactions on Industrial Electronics, 64(7), 5540–5548. https://doi.org/10.1109/TIE.2017.2756290 Elmore, J., Adepoju, T., & Singh, K. (2020). Advances in automated condition monitoring for laboratory electrical machines. International Journal of Electrical Engineering Research, 18(4), 225–239. Lee, C., Zhang, L., & Wang, F. (2021). Signal processing methods for fault detection in electrical machines. IEEE Transactions on Signal Processing, 69(8), 1802– 1811.https://doi.org/10.1109/TSP.2021.3071042 Li, Z., Ma, H., & Xu, W. (2016). Machine condition monitoring using advanced sensing technologies. Mechanical Systems and Signal Processing, 75, 765–783. https://doi.org/10.1016/j.ymssp.2016.02.015 Smith, J., & Johnson, P. (2019). Sensor technologies for fault detection in electrical machines. Sensors and Actuators A: Physical, 295, 50–61. https://doi.org/10.1016/j.sna.2019.03.016 Zhang, Y., Liu, Q., & Huang, J. (2020). Machine learning approaches for fault diagnosis in electrical systems. IEEE Transactions on Industrial Informatics, 16(4), 2102– 2111.https://doi.org/10.1109/TII.2019.2957885

Software-Based Simulation for Fault Detection and Diagnosis in Electrical Machines Using MATLAB/Simulink

Abstract

Keywords

References

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