Our client is a large-scale manufacturing company operating multiple production lines across facilities in Europe. Their operations involve heavy machinery and precision equipment used in continuous cycles, where unplanned downtime results in significant financial loss and production bottlenecks.
The client faced several challenges with their existing maintenance process. Reliance on fixed-interval maintenance which was either too late or unnecessarily early. Frequent unplanned breakdowns that caused production stoppages.
We developed a scalable predictive maintenance platform that continuously monitored machinery health, forecasted potential failures, and recommended preemptive actions. The system was built around a combination of sensor data ingestion and historical failure analysis.
Software Deployment
Equipment Mapping and Failure Profiling
We began by identifying all critical assets across the facility and cataloging known failure types, historical breakdowns, and available telemetry. This phase included workshops to define operational thresholds, failure consequences, and acceptable times for proactive intervention.
Sensor Integration and Historical Cleaning
Our team connected to PLCs, SCADA systems, and IoT sensors to collect real-time and historical data on parameters like temperature, vibration, RPM, and load. We cleaned, normalized, and aligned this data with past maintenance logs to establish ground truth labels.
Predictive Modeling and Anomaly Detection
We built machine learning models tailored to different machine types and usage patterns. These models identified patterns of degradation and predicted failure windows. We also incorporated confidence intervals and business-impact scoring to prioritize attention.
Testbed Rollout and Field Validation
The system was deployed on a single production line for real-world testing. Operators and planners received early alerts through dashboards and validated the relevance of predictions. Feedback helped refine model sensitivity, alert thresholds, and UI/UX for the final rollout.
Multi-Line Integration and Process Automation
After the successful pilot, we scaled the solution across all applicable production lines and equipment categories. The system was connected to the client’s CMMS, enabling automatic work order generation. Performance metrics were tracked and visualized through custom dashboards.
The platform continuously ingested sensor data such as vibration, temperature, and energy usage to monitor machine health in real time.
Advanced machine learning models provided accurate predictions of equipment failure windows with lead time estimates.
When failure probabilities crossed thresholds, the system automatically generated maintenance tasks inside the client’s CMMS.
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