Mondi Implements Statistics-Based Health Monitoring and Predictive Maintenance
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
- Automation & Control - Programmable Logic Controllers (PLC)
- Functional Applications - Remote Monitoring & Control Systems
- Sensors - Accelerometers
- Sensors - Pressure Sensors
- Sensors - Temperature Sensors
- Predictive Maintenance
The extrusion and other machines at Mondi’s plant are large and complex, measuring up to 50 meters long and 15 meters high. Each machine is controlled by up to five programmable logic controllers (PLCs), which log temperature, pressure, velocity, and other performance parameters from the machine’s sensors. Each machine records 300–400 parameter values every minute, generating 7 gigabytes of data daily.
Mondi faced several challenges in using this data for predictive maintenance. First, the plant personnel had limited experience with statistical analysis and machine learning. They needed to evaluate a variety of machine learning approaches to identify which produced the most accurate results for their data. They also needed to develop an application that presented the results clearly and immediately to machine operators. Lastly, they needed to package this application for continuous use in a production environment.
Use MATLAB to develop and deploy monitoring and predictive maintenance software that uses machine learning algorithms to predict machine failures