The Cyber-Physical System of Machine Tool Monitoring: A Model-Driven Approach With Extended Kalman Filter Implementation

The condition monitoring is essential to the advanced manufacturing process in the era of the fourth industrial revolution because it ensures the prediction and optimization of machine tool conditions via data analytics or physical modeling methods. The cyber-physical system (CPS) has the property of intellectuality, scalability, adaptability, and openness, making it suitable for machine tool monitoring. The current data-driven CPS method is prone to interpretability and generalization limitations due to the empirical selection of hyper-parameters in the model and the need for heterogeneous data. On the other hand, traditional model-driven systems are difficult to adjust models to practical working conditions data due to empirical equations constructed by offline data. This article proposes a novel model-driven cyber-physical system (MDCPS) to overcome these weaknesses. First, the physical model generates a counterpart of the machining process to form a cyber world, and sensors depict the real-time state of the machining process to form a physical world. Second, for deep fusion between the cyber and physical worlds, the extended Kalman filter (EKF) approach is applied to calibrate the empirical model with online measured data. Third, the model-based diagnosis and prediction methods are used for online monitoring and control. Case studies of MDCPS for machining monitoring are presented to prove the feasibility of this model-driven system.