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Mondi Implements Statistics-Based Health Monitoring and Predictive Maintenance
Technology Category
- 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
Applicable Industries
- Packaging
Applicable Functions
- Maintenance
Use Cases
- Predictive Maintenance
The Challenge
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.
The Customer
Mondi Grona
About The Customer
Mondi Grona is a leading international manufacturer of packaging and paper products. The company’s plastic production plant delivers about 18 million tons of plastic and thin film products annually. The plant’s 900 workers operate approximatel
The Solution
Use MATLAB to develop and deploy monitoring and predictive maintenance software that uses machine learning algorithms to predict machine failures
Data Collected
Machine Performance
Quantitative Benefit
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