Energy — Sensor Data Time Series Analysis for Predictive Maintenance

Overview

The primary purpose of this DAG is to facilitate predictive maintenance in the energy sector by analyzing time series data derived from real-time sensor inputs. It begins by ingesting data from various sensor sources, including temperature readings, vibration metrics, and operational logs. This data is then transformed into time series formats, allowing for detailed trend analysis over time. The processing logic employs advanced anomaly detection techniques, utilizing statistical models and mach

The primary purpose of this DAG is to facilitate predictive maintenance in the energy sector by analyzing time series data derived from real-time sensor inputs. It begins by ingesting data from various sensor sources, including temperature readings, vibration metrics, and operational logs. This data is then transformed into time series formats, allowing for detailed trend analysis over time. The processing logic employs advanced anomaly detection techniques, utilizing statistical models and machine learning algorithms to identify potential equipment failures before they occur. Quality controls are implemented to ensure data integrity, including checks for missing values and outlier detection. The processed data is stored for historical analysis, ensuring traceability and compliance with industry standards. Alerts are generated when anomalies are detected, providing immediate notifications to maintenance teams. The final outputs are visualized through a comprehensive dashboard, which displays key performance indicators (KPIs) such as anomaly frequency, maintenance predictions, and overall equipment effectiveness (OEE). This DAG not only enhances operational efficiency but also reduces downtime and maintenance costs, ultimately leading to improved asset reliability and performance in the energy sector.

Part of the Predictive Maintenance solution for the Energy industry.

Use cases

• Reduces unplanned downtime through predictive insights
• Enhances operational efficiency and asset reliability
• Lowers maintenance costs with proactive strategies
• Improves compliance with industry regulations
• Increases overall equipment effectiveness and performance