Data Mining and Machine Learning for Condition-based Maintenance

Complex production systems may count thousands of parts and components, subjected to multiple physical and logical connections and interdependencies. This level of complexity inhibits the traditional and statistically-based approach to reliability engineering, failure prediction and maintenance planning. The existing ICT solutions simplify the on-field collection of large amount of data, but require models and tools able to create knowledge from these data. Key questions on how to predict in advance the performance of the production system and the associated failure events could be finally addressed. This paper introduces a set of data analytics models and methods that can be profitably used for decision making in general, and, specifically, in maintenance engineering. These classification models, specifically decision trees, random forests, and neural networks, are applied to a real-world case study, and the resulting accuracy on predicting faults is quantified and compared. We used the historical profiles of the energy variables of an high-speed packaging machine to find out some strategies for the prediction of a given failure. The conducted experiments demonstrate that the accuracy of the random forest is slightly better than the other methods, but even increases the probability of false alarm, which would result in unwanted production break-down. Even though the obtained results are promising, they leave room for further experiments based on the application of other classifiers, rather than the definition of customized methods able to embrace such complexity.