Data Driven Fault Detection and Predictive Maintenance in Industrial Electrical Systems Using Python Based Signal Analysis and Power Quality Indicators
DOI:
https://doi.org/10.32628/IJSRSET261359Keywords:
Predictive Maintenance, Industrial Electrical Systems, Power Quality Monitoring, Machine Learning for Fault Detection, Python-Based Signal ProcessingAbstract
Industrial electrical systems are critical components of modern manufacturing and energy infrastructures, where unexpected equipment failures can lead to costly operational disruptions and safety risks. This study presents a data-driven framework for fault detection and predictive maintenance in industrial electrical systems using Python-based signal analysis and power quality indicators. The proposed approach integrates electrical signal monitoring, advanced signal processing techniques, and machine learning algorithms to detect early signs of system degradation. Electrical parameters including voltage, current, frequency, and power factor are captured using high-resolution data acquisition systems and processed through a Python-based analytical pipeline. Signal processing methods such as Fast Fourier Transform are applied to identify harmonic distortions and transient anomalies, while power quality indicators such as Total Harmonic Distortion, voltage imbalance, and frequency deviation are extracted as predictive features. Machine learning models including Random Forest, Support Vector Machines, Artificial Neural Networks, and Gradient Boosting are trained to classify operational states and forecast potential equipment failures. Experimental results demonstrate that the proposed framework significantly improves fault detection accuracy and enables earlier identification of abnormal electrical behavior compared with conventional monitoring systems. The integration of real-time signal analytics with predictive modeling provides a scalable solution for intelligent maintenance planning, reduced equipment downtime, and improved operational efficiency in industrial environments. The study highlights the potential of combining power quality monitoring with machine learning–based predictive analytics to enhance the reliability and sustainability of industrial electrical infrastructures.
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Adewale, L. D. (2025). Applying Supply Chain 4.0 to vertical supply chain integration: A key to revitalizing U.S. automotive manufacturing sector. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.6.0225.0940
Adewale, L. D. (2025). Lifecycle assessment and circular economy strategies for sustainable automotive materials: Optimizing recycling, waste reduction, and cost efficiency. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.6.0225.0953
Adewale, L. D. (2025). Sustainable and high-performance materials in automotive manufacturing: Enhancing durability, lightweighting, and lifecycle optimization through data-driven material science. International Research Journal of Modernization in Engineering Technology and Science, 7(2). https://www.doi.org/10.56726/IRJMETS67497
Adewale, L. D. (2026). Digital evidence chains for PPAP assurance: AR-guided data capture, AI-verified documentation, and continuous audit automation for secure multi-tier supplier traceability in Industry 4.0 manufacturing. International Journal of Multidisciplinary Futuristic Development, 7(1), 43–55. https://doi.org/10.54660/IJMER.2026.7.1.43-55
Adewale, L. D. (2026). Machine learning surrogate models replacing physics simulations. International Journal of Computer Applications Technology and Research, 12(12), 341–352. https://doi.org/10.7753/IJCATR1212.1030
Adewale, L. D. (2026). Smart factories, smarter evidence: Reinventing quality assurance for U.S. manufacturing competitiveness. International Journal of Multidisciplinary Futuristic Development, 7(1), 9–18. https://doi.org/10.54660/IJMFD.2026.7.1.09-18
Adewale, L. D. (2026). Smart factories, smarter evidence: Reinventing quality assurance for U.S. manufacturing competitiveness. International Journal of Multidisciplinary Futuristic Development, 7(1), 9–18. https://doi.org/10.54660/IJMFD.2026.7.1.09-18
Animasaun, J. B., Ijiga, O. M., Ayoola, V. B., & Enyejo, L. A. (2026). Development of a rapid GC-MS workflow for simultaneous quantification of volatile terpenes and cannabinoids in industrial hemp extracts. International Journal of Innovative Science and Research Technology.
Animasaun, J. B., Ijiga, O. M., Ayoola, V. B., & Enyejo, L. A. (2024). Impact of solvent polarity on volatile and non-volatile cannabinoid recovery: A multivariate GC-MS/LC-MS extraction optimization study. International Journal of Scientific Research and Modern Technology.
Animasaun, J. B., Ijiga, O. M., Ayoola, V. B., & Enyejo, L. A. (2024). Evaluating the stability of cannabinoid extracts following different solvent evaporation conditions: A GC-MS/LC-MS degradation profiling study. International Journal of Scientific Research and Modern Technology.
Anyebe, A. P., Yeboah, O. K. K., Bakinson, O. I., Adeyinka, T. Y., & Okafor, F. C. (2024). Optimizing carbon capture efficiency through AI-driven process automation for enhancing predictive maintenance and CO2 sequestration in oil and gas facilities. American Journal of Environment and Climate, 3(3), 44–58. https://doi.org/10.54536/ajec.v3i3.3766
Avevor, J., Aikins, S. A., & Enyejo, L. A. (2025). Optimizing gas and steam turbine performance through predictive maintenance and thermal optimization for sustainable and cost-effective power generation. International Journal of Innovative Science and Research Technology, 10(3). https://doi.org/10.38124/ijisrt/25mar1336
Avevor, J., Aikins, S. A., & Enyejo, L. A. (2025). Optimizing gas and steam turbine performance through predictive maintenance and thermal optimization for sustainable and cost-effective power generation. International Journal of Innovative Science and Research Technology, 10(3). https://doi.org/10.38124/ijisrt/25mar1336
Avevor, J., Aikins, S. A., Peter-Anyebe, A. C., Enyejo, L. A., Eze, F. C., Adaudu, I. I. (2025). A Digital Twin-Based Predictive Maintenance Framework for Combined-Cycle Turbines in Load-Bearing Smart Structures Supporting Energy Diplomacy. Acta Mechanica Malaysia (AMM) 8(2) (2025) 77-85. http://doi.org/10.26480/amm.02.2025.77.85
Bollen, M. H. J., & Gu, I. Y. H. (2017). Signal processing of power quality disturbances. Wiley-IEEE Press.
Bollen, M. H. J., & Hassan, F. (2011). Integration of distributed generation in the power system. IEEE Press.
Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. P., Basto, J., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.
Ebika, I. M., Idoko, D. O., Efe, F., Enyejo, L. A., Otakwu, A., & Odeh, I. I. (2024). Utilizing machine learning for predictive maintenance of climate-resilient highways through integration of advanced asphalt binders and permeable pavement systems with IoT technology. International Journal of Innovative Science and Research Technology, 9(11).
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
Idoko, I. P., Ijiga, O. M., Akoh, O., Agbo, D. O., Ugbane, S. I., & Umama, E. E. (2024). Empowering sustainable power generation: The vital role of power electronics in California's renewable energy transformation. *World Journal of Advanced Engineering Technology and Sciences*, 11(1), 274-293.
IEEE. (2019). IEEE standard 1159-2019: Recommended practice for monitoring electric power quality. IEEE.
Isibor, I., Bamigwojo, O. V., Enyejo, L.,, & Olola, G. I. (2025). Automated FEMA-Compliant Floodplain Encroachment Assessment Using Python-Based Geospatial Workflows. International Journal of Scientific Research and Modern Technology, 4(10), 225–244. https://doi.org/10.38124/ijsrmt.v4i10.1285
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.
Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.
Mallat, S. (2009). A wavelet tour of signal processing: The sparse way (3rd ed.). Academic Press.
McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference (SciPy), 51–56.
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. Wiley.
Okeke, R. O., Ibokette, A. I., Ijiga, O. M., Enyejo, L. A., Ebiega, G. I., & Olumubo, O. M. (2024). The reliability assessment of power transformers. *Engineering Science & Technology Journal*, 5(4), 1149-1172
Oladoye, S. O., Bamigwojo, O. V., James, A. O., & Ijiga, O. M. (2021). AI-driven predictive maintenance modeling for high-voltage distribution assets using sensor fusion and time-series degradation analysis. International Journal of Scientific Research in Science, Engineering and Technology, 11(2), 387–411. https://doi.org/10.32628/IJSRSET2291524
Oloba, B. L., Olola, T. M., & Ijiga, A. C. (2024). Powering reputation: Employee communication as the key to boosting resilience and growth in the U.S. service industry. World Journal of Advanced Research and Reviews, 23(3), 2020–2040. https://doi.org/10.30574/wjarr.2024.23.3.2689
Onuh, J. E., Idoko, I. P., Igbede, M. A., Olajide, F. I., Ukaegbu, C., & Olatunde, T. I. (2024). Harnessing synergy between biomedical and electrical engineering: A comparative analysis of healthcare advancement in Nigeria and the USA. World Journal of Advanced Engineering Technology and Sciences, 11(2), 628–649.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Kusuma, Kranthi Kiran. (2025). Cross-Carrier Certification Challenges and Solutions for Multi-National Device Deployments. International Journal of Research and Analytical Reviews. 12. 10.56975/ijrar.v12i3.319333.
Singfield, A. (January 27, 2026). Power Quality Monitoring for Early Fault Detection: The Engineering Guide to Predictive Electrical Maintenance. Retrieved from: https://www.vistaprojects.com/power-quality-monitoring-early-fault-detection/
Sutherland, P. E. (2014, October). Harmonics in electrical power systems: Effects of new technologies. In 2014 IEEE Industry Application Society Annual Meeting (pp. 1-13). IEEE.
Tom-Ayegunle, K., Jamil, Y., & Echouffo-Tcheugui, J. (2025). Cumulative burden of geriatric conditions and cardiovascular outcomes in older adults: Analysis from ARIC. JACC: Advances, 4(12 Part 1). https://doi.org/10.1016/j.jacadv.2025.102308
Zonta, T., Da Costa, C. A., Da Rosa Righi, R., De Lima, M. J., Da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 106889.
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