Intraday forecasting of OEE through sensor data and machine learning

Abstract

Key performance indicators are crucial for measuring and managing the performance of industrial processes. They are employed to detect deviations in production, enabling opportunities to improve manufacturing processes within the three dimensions of time, quality, and cost. In this context, the timeliness of information plays a decisive role in the success of measures since delayed information availability can leave decision-makers with no time to react. Usually, key performance indicators are updated on a shift or daily basis and thus only allow a delayed reaction to deviations that have occurred. To tackle this problem, different mixed data sampling and machine learning methods for the intraday monitoring of key performance indicators based on high-frequency sensor and machine data are investigated. By benchmarking the approaches on real-world data from the process industry, the authors show how to obtain models for real-time detection of performance deviations. To enable application for practitioners and academia in the future, a method for implementation and deployment in real-world scenarios is given. The developed methodology offers the construction of high-frequency forecasts for key performance metrics, and therefore accelerates managers’ reactions to associated problems.