Abstract
Digitisation offers manufacturing companies new opportunities to improve their operations and competitiveness in the market by unleashing potentialities related to real-time monitoring and control of operating machines. Through condition-based and predictive maintenance, the knowledge about the health state and probability of failure of the machines is improved for better decision-making. Amongst them, CNC machine tools do represent a complex case from a maintenance viewpoint as their operations are ever-changing and their high reliability brings to a lack, or limited set, of run-to-failure data. To address the problem, the research work proposes an operations-aware novelty detection framework for CNC machine tools based on already-in-place controllers. The framework is based on statistical modelling of the behaviour of the machine tools, namely through gradient boosting regression and Gaussian mixture models, to identify the health state considering varying operations through time. The proposed solution is verified on sixteen multi-axis CNC machine tools in a large manufacturing company. The results show that the proposed solution can effectively support maintenance decisions by informing on the health states while discerning between varying operations and abnormal/faulty states of interest. This solution represents a brick in a cloud-edge-based industrial information system stack that can be further developed for shop floor-integrated decision-making.
