Integration of process parameters and condition monitoring data through Deep Learning models for predictive maintenance

Manufacturers, particularly machine tool builders, are increasingly adopting servitization, transitioning from selling products to offering integrated product-service systems (PSS). Machine tool companies aim to create value added processes by providing predictive maintenance services and ensuring machine users can minimize downtime through up-to-date machines. However, the health condition of machines is significantly influenced by process parameters and operating conditions, often overlooked during machine operation. This results in the accumulation of unlabeled condition monitoring data, posing challenges in constructing predictive models for health assessment and prediction. Although some of this data resides in Programmable Logic Controllers (PLC), obtaining information directly from users is challenging due to privacy concerns, as users perceive PLC data as sensitive and are hesitant to share it with manufacturers. Consequently, there is a need to develop a data collection platform capable of remotely gathering both condition monitoring and sensitive data. This study addresses the integration of process parameters and condition monitoring data to facilitate predictive maintenance servitization in the machine tool industry. To this aim, sequence classification, sequence-to-sequence classification and sequence regression approaches based on Convolutional Neural Network and Long Short-Term Memory are adopted. These lightweight algorithms efficiently predict the machining processes, the tool, and the depth of cut, automatically storing contextual information for each manufacturing process sequence. This model contributes to creating a comprehensive database that producers can utilize to develop maintenance plans for users. The proposed approach is validated through a case study involving a five-axes CNC machine, underscoring the importance of automatically collecting contextual information for real-time monitoring and enhancing PHM techniques. The findings contribute to the realization of predictive health monitoring methods, fostering large-scale interoperability and servitization in maintenance practices.