Predictive Maintenance Using Deep Learning: Enhancing Reliability and Reducing Electrical System Downtime

Jamshaid Basit ()
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Jamshaid Basit: Department of Computer Science and Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 3, 1120-1136

Abstract: Predictive Maintenance (PM) is crucial for enhancing the reliability of electrical systems and minimizing unscheduled outages. However, the conventional methodology lacks the ability to address the escalating and diverse problems of the modern complex environment. The current study provides an alternative approach to carrying out predictive maintenance activity based on the use of deep learning models that enhance conventional procedures. For the given analysis, we used an artificial data set consisting of 10,000 samples and 14 variables, such as air temperature, process temperature, flipping rate, and tool wear level. We conducted a self-assessment using the specified models to confirm their effectiveness in predicting various failure modes and forms, such as tool wear and heat dissipation conking. This research demonstrated that deep learning models, specifically LSTMs, outperform the established statistical methods in predicting equipment failures. LSTMs provided high accuracy and predicted the failing system before it happened. Furthermore, integrating deep learning with the statistical method that is normally used for anomaly detection improves the model's stability and reliability. The evaluation's findings emphasize the potential of deep learning algorithms for expanding the range of PM applications to achieve better and faster failure predictions. The beneficial thing about this approach is that it presents a means for addressing the inherent problems of large electrical systems’ predictive maintenance that are beyond the scope of traditional practice.

Keywords: Predictive Maintenance; Deep Learning; Downtime Minimization; Neural Networks; Failure Prediction (search for similar items in EconPapers)
Date: 2024
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