Case Study: Predictive Maintenance for Equipment

Manufacturing

Predictive Maintenance for Equipment

The Challenge: Unplanned Downtime Crippling Production

A large manufacturing company operating complex production lines faced significant operational disruptions and financial losses due to unexpected equipment failures. Their existing maintenance strategy was primarily reactive (fixing equipment after it broke down) or based on fixed time schedules, which often resulted in either premature replacement of functional parts or catastrophic failures before scheduled maintenance. Unplanned downtime led to costly production halts, missed delivery targets, increased scrap rates, and expensive emergency repairs, including overtime labor and expedited shipping of spare parts. The lack of insight into the actual condition of critical machinery made it impossible to optimize maintenance schedules or prevent failures proactively.

Our Solution: AI-Driven Insights from Sensor Data on Azure

Lydatum implemented an end-to-end predictive maintenance (PdM) solution built on the Microsoft Azure platform, designed to anticipate equipment failures and enable proactive maintenance interventions. The solution involved several integrated components:

• Sensor Data Integration & Storage (Azure IoT Hub & Azure SQL): We worked with the client to identify critical equipment and instrument it with relevant sensors (e.g., vibration, temperature, pressure, acoustic, power consumption). Sensor data was securely ingested in real-time using Azure IoT Hub and stored efficiently in Azure SQL Database, alongside historical maintenance logs and operational data.
• Machine Learning for Failure Prediction (Azure ML): Using Azure Machine Learning, we developed and deployed machine learning models specifically trained to detect anomalies and predict potential failures. These models analyzed patterns in the high-frequency sensor data, identifying subtle deviations from normal operating conditions that often precede a breakdown. Techniques included anomaly detection algorithms and supervised learning models trained to predict Remaining Useful Life (RUL) for specific components.
• Data Visualization and Alerting (Power BI): Interactive Power BI dashboards were created to provide maintenance teams and plant managers with a clear view of equipment health. The dashboards visualized real-time sensor readings, historical trends, anomaly alerts generated by the ML models, and predicted failure timelines. Automated alerts were configured to notify relevant personnel via email or SMS when a potential issue was detected or when predicted RUL dropped below a critical threshold.
• Integration with Maintenance Systems (CMMS): The alerts and insights from the PdM solution were integrated with the company's existing Computerized Maintenance Management System (CMMS). This allowed for the automatic generation of work orders for proactive maintenance based on the AI predictions, streamlining the scheduling and execution of maintenance tasks.

The Impact: Maximized Uptime, Optimized Costs, Extended Lifespan

The predictive maintenance solution delivered substantial improvements to the manufacturing operation's efficiency and bottom line:

By predicting failures before they occurred, the company significantly reduced costly unplanned downtime, leading to smoother production schedules and improved output. Maintenance activities shifted from reactive fixes to proactive, planned interventions, which were less disruptive and less expensive. This optimization reduced overall maintenance costs, including labor and spare parts inventory. Furthermore, by addressing potential issues early and performing maintenance based on actual equipment condition rather than fixed schedules, the solution helped to extend the operational lifespan of critical machinery, maximizing the return on capital investments.

Technologies Used: Azure Machine Learning (Azure ML), Azure SQL Database, Microsoft Power BI, Azure IoT Hub, Azure Stream Analytics, Azure Functions, CMMS Integration