Energy — Predictive Maintenance for Industrial Assets

Overview

The purpose of this DAG is to enhance the maintenance of industrial assets by leveraging data from IoT sensors and ERP systems to predict failures and minimize unplanned downtime. The ingestion pipeline begins with the collection of real-time data from IoT sensors, which monitor asset health, and ERP transaction logs that provide operational context. This data is normalized to ensure consistency and accuracy before being processed through predictive modeling algorithms that identify potential fa

The purpose of this DAG is to enhance the maintenance of industrial assets by leveraging data from IoT sensors and ERP systems to predict failures and minimize unplanned downtime. The ingestion pipeline begins with the collection of real-time data from IoT sensors, which monitor asset health, and ERP transaction logs that provide operational context. This data is normalized to ensure consistency and accuracy before being processed through predictive modeling algorithms that identify potential failure patterns. Quality control measures are implemented, including data validation tests and access checks, to maintain the integrity of the data being analyzed. The outputs of this DAG include alerts for maintenance interventions, which are integrated into a Computerized Maintenance Management System (CMMS) to track actions taken and monitor key performance indicators (KPIs) such as mean time to repair (MTTR) and asset uptime. By effectively predicting maintenance needs, this solution offers significant business value, including reduced operational costs, increased asset reliability, and enhanced decision-making capabilities for maintenance teams.

Part of the Predictive Maintenance solution for the Energy industry.

Use cases

• Minimizes unplanned downtime through proactive maintenance
• Reduces operational costs associated with emergency repairs
• Enhances asset reliability and lifespan with timely interventions
• Improves decision-making with data-driven insights
• Increases overall operational efficiency and productivity