Energy — Predictive Maintenance System for Energy Assets

Overview

The primary purpose of this DAG is to implement a predictive maintenance system for energy assets, leveraging IoT sensor data and historical maintenance records. The ingestion pipeline starts with collecting real-time data from various IoT sensors installed on equipment, alongside historical maintenance logs. These data sources are then normalized to ensure consistency and compatibility for further analysis. The core processing involves applying machine learning models to predict maintenance nee

The primary purpose of this DAG is to implement a predictive maintenance system for energy assets, leveraging IoT sensor data and historical maintenance records. The ingestion pipeline starts with collecting real-time data from various IoT sensors installed on equipment, alongside historical maintenance logs. These data sources are then normalized to ensure consistency and compatibility for further analysis. The core processing involves applying machine learning models to predict maintenance needs based on patterns identified in the data. Quality controls are embedded in the processing logic to validate data integrity and accuracy, ensuring reliable predictions. The outputs of this DAG include a comprehensive maintenance dashboard that displays predictive insights and alerts for necessary interventions. Key performance indicators (KPIs) such as maintenance cost reduction, equipment uptime, and response time to alerts are monitored to assess the effectiveness of the predictive maintenance strategy. The business value derived from this DAG includes significant reductions in operational costs, enhanced asset longevity, improved safety standards, and increased reliability of energy supply, ultimately driving greater customer satisfaction and operational excellence.

Part of the Knowledge Portal & Ontologies solution for the Energy industry.

Use cases

• Reduced unplanned downtime through proactive maintenance.
• Lower operational costs via optimized maintenance schedules.
• Extended asset lifespan through timely interventions.
• Enhanced safety and compliance with maintenance regulations.
• Improved customer satisfaction from reliable energy supply.