Real-Time Predictive Maintenance of Power Electronics Systems using Machine Learning and IoT Integration.

Abstract

Power electronics systems play a crucial role in various industrial applications, ranging from renewable energy generation to electric vehicles and industrial automation. Ensuring the reliability and availability of these systems is essential for uninterrupted operations and optimal performance. Traditional maintenance approaches often rely on scheduled inspections or corrective actions, which may not effectively prevent unexpected failures or downtime. [1] Real-time predictive maintenance (PdM) offers a proactive solution by continuously monitoring system health and predicting potential failures before they occur. This paper proposes a framework for real-time predictive maintenance of power electronics systems by integrating machine learning algorithms with Internet of Things (IoT) technology. The proposed framework offers several benefits, including proactive maintenance, improved reliability, optimized resource allocation, and cost reduction. A case study using real-world data can demonstrate the effectiveness of the framework in predicting failures and optimizing maintenance activities. Overall, real-time predictive maintenance of power electronics systems using machine learning and IoT integration holds promise for enhancing system performance and reducing operational risks in various industrial applications.