Deep Learning-Based Anomaly Detection for Predictive Maintenance of Cold Isostatic Press

Abstract: Predictive maintenance is an automated technique that analyses sensor data from industrial systems to enable downtime planning. Available for this study is unlabelled data from sensors placed in proximity to hydraulic system outlets of a cold isostatic press. There is limited knowledge about degradation processes because of their rarity, but it is still of high importance to minimise them. One approach to overcome this obstacle is by implementing machine learning to recognise deviations from normal behaviour and potentially learn about them. The state-of-the-art machine learning algorithms for situations with little to no knowledge about anomalies in different machines are deep learning variants using unsupervised learning and transfer learning. With the foundation of such research, this study analyses the available data and proposes three deep learning methods. The testing of these algorithms is made by presenting an equal amount of healthy and simulated unhealthy data as input. The output measurement threshold is adjusted to minimise false negatives because of safety reasons. Consequently, the best method (denoising autoencoder) results in 94% accuracy for separating the data and 74% when also identifying the source of error. However, the results should be taken with caution as the simulated faulty data is not fully representative of a real scenario. These algorithms indicate to what extent they are capable of separating deviations from normal data. This thesis provides knowledge about predictive maintenance and lays a foundation for implementing automatic anomaly detection with deep learning in a high-pressure system.