Predictive Maintenance

Based on sensor data and the use of machine learning algorithms, an accurate predictive model for predicting failures of industrial plants has been developed. This can lower the effort for maintenance, cut downtime and reduce production costs.

The Hackathon: In January 2017, ANDRITZ and PIONEERS DISCOVER invited seven select software startups from Europe, including 7LYTIX, to a Big Data Analytics Hackathon in Graz / Austria. In these three days, five core issues in predictive maintenance had to be solved in the best feasible way. Andritz's industrial plants are equipped with many sensors that permanently measure plant performance.

Accurate lifetime predictions: By identifying and using the best machine learning algorithm on a test dataset, 7LYTIX was able to identify patterns in the sensor data and develop a predictive model that accurately predicts the timing of upcoming machine failures over the following 20 days. The prediction accuracy achieved was outstanding with an F1 value of 0.96 (0 = bad, 1 = perfect prediction).

Decisive operating parameters: In addition, 7LYTIX was able to determine the operating parameters that have the greatest impact on the failures, maintenance and operating costs of Andritz's industrial plants. As part of this, it was immediately possible to determine the production phases in which the plants work most cost-effective and which variables play a decisive role.

Benefits: The benefits for manufacturing companies are obvious: cutting down on expensive downtime. Reduce maintenance. Reduce production costs. However, suppliers of industrial equipment and machines such as ANDRITZ also can make precise product improvements based on usage data.

Summary

Based on sensor data and the use of machine learning algorithms, an accurate predictive model for predicting failures of industrial plants has been developed. This can reduce maintenance, cut downtime, and reduce production costs.