IoT-Enabled Predictive Maintenance in Electrical Systems using Databricks and Synapse

Abstract

This study explores the integration of Internet of Things (IoT) technology with predictive maintenance strategies for electrical systems, utilizing Databricks and Azure Synapse Analytics to enhance low-latency data processing capabilities. Predictive maintenance has emerged as a pivotal approach to minimizing downtime and operational costs in electrical systems, allowing for timely interventions based on real-time data. By leveraging IoT devices, we can collect vast amounts of operational data, including temperature, vibration, and electrical consumption metrics, which are essential for predictive modeling.

The research focuses on the development of a robust IoT framework capable of real-time data collection and analysis. We implemented machine learning algorithms within Databricks to process and analyze this data, identifying patterns that indicate potential failures. Azure Synapse Analytics was employed to provide a seamless environment for big data analytics, enabling fast querying and insights generation.

The findings demonstrate that implementing an IoT-enabled predictive maintenance system significantly enhances decision-making speed in financial operations tied to electrical systems. Specifically, low-latency data handling allows organizations to respond quickly to potential equipment failures, ultimately reducing costs and improving efficiency. This paper also discusses the challenges faced in achieving optimal data processing speeds and provides recommendations for organizations aiming to adopt similar systems. The insights gained from this study can guide future implementations of IoT and predictive maintenance in various industrial sectors, emphasizing the critical role of advanced analytics in modern operational strategies.