Western Canadian Energy Firm Deploys AI-Powered Predictive Maintenance

A major Western Canadian energy company was experiencing significant unplanned downtime across its pipeline and processing infrastructure, costing an estimated $12 million annually. The company had thousands of IoT sensors generating terabytes of data daily, but lacked the analytics capability to derive actionable maintenance insights. Their reactive maintenance approach was inefficient and posed safety risks.

Zaha designed and deployed an AI-powered predictive maintenance platform that ingests data from 4,800+ IoT sensors in real time. Using Apache Kafka for stream processing and Databricks for machine learning model training, we built anomaly detection models that predict equipment failures 72 hours in advance with 94% accuracy. The platform includes a real-time monitoring dashboard built with Grafana, automated alerting through PagerDuty, and integration with the client's SAP maintenance management system. Models were trained on three years of historical failure data and are continuously retrained as new data flows in.