A simple yet effective approach for log based critical errors prediction

Abstract

With the advent of Industry 4.0, failure anticipation is becoming one of the key objectives in industrial research. In this context, predictive maintenance is an active research area for various applications. This paper presents an approach to predict high importance errors using log data emitted by machine tools. It uses the concept of bag to summarize events (or errors) provided by remote machines, available within log files. The idea of bag is inspired by the Multiple Instance Learning paradigm introduced by Dietterich et al. However, our proposal follows a different strategy to label bags, that we wanted as simple as possible. Three main setting parameters are defined to build the training set allowing the model to fine-tune the trade-off between early warning, historical data informativeness and time accuracy. The effectiveness of the approach is demonstrated using a real industrial application where failures can be predicted up to seven days in advance thanks to a classification model.