Detecting Seasonal Dependencies in Production Lines for Forecast Optimization

Abstract

Huge amounts of data are produced inside an industrial production plant every minute. This data is getting more accessible by higher network and computing capabilities. This poses an opportunity to apply methods in real time to support the reliability of production machines. In theory every time series, that is currently monitored by for a breach of thresholds, can be extended with a forecast method. Classical approaches, such as ARIMA and Exponential Smoothing can be used for forecasting. To describe the signal and boost the forecast results we use a clustering method to group each unknown data stream in a seasonality class. This seasonality classes can be used for insight into intra and inter group behaviour between machines and add causality to factory wide correlations. We collected 10000 multiple day segments of multiple identical and different machines. We manually hand labelled the data segments for their seasonality pattern to compare and explain the clustering results. Classes, obtained through clustering, are used to adapt each single forecast model for every machine. For the forecast method we could show improved results by selecting the correct seasonality for each data stream.