Health status assessment for the feed system of CNC machine tool based on simulation

This paper proposes an approach of health assessment based on the ADAMS simulation for the feed system of the CNC machine tool. Firstly, the dynamics simulation model of the feed system has been built to collect the large number of the motion parameters, and to calculate the related performance parameters according to these motion parameters. Secondly, the BP neural network model is built for the performance mapping model between the structural parameters and the related performance parameters, which can provide a large number of reference data for health assessment of the CNC machine tool. Then the Hidden Markov Chain model is used to establish a health status assessment model, which has the observation sequence of multi-state and multi-performance. Based on the HMC model, the algorithms are used to solve the health status probability for the feed system of the CNC machine tool. Finally, a case study is discussed to verify the method of health status assessment.