MathWorks Case Studies Mondi Implements Statistics-Based Health Monitoring and Predictive Maintenance

	Mondi Implements Statistics-Based Health Monitoring and Predictive Maintenance MathWorks

Mondi Implements Statistics-Based Health Monitoring and Predictive Maintenance

MathWorks

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
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. Read More
About 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 Read More
Customer Name	Mondi Grona Read More
Solution	Use MATLAB to develop and deploy monitoring and predictive maintenance software that uses machine learning algorithms to predict machine failures Read More Log in to view content
Data Collected	Machine Performance Read More Log in to view content
Contents

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

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.

About 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

Customer Name

Mondi Grona

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

Benefit #1	More than 50,000 euros saved per year.
Benefit #2	Predictive maintenance prototype completed in 6 months.
Benefit #3	Production software runs 24/7.

Benefit #1

More than 50,000 euros saved per year.

Benefit #2

Predictive maintenance prototype completed in 6 months.

Benefit #3

Production software runs 24/7.

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Overview

Mondi Implements Statistics-Based Health Monitoring and Predictive Maintenance

Quantitative Benefit