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.

[1]  Zhou Dong,et al.  A Survey on Anomaly Detection, Life Prediction and Maintenance Decision for Industrial Processes , 2013 .

[2]  Rui Peng,et al.  A preventive maintenance model with a two-level inspection policy based on a three-stage failure process , 2014, Reliab. Eng. Syst. Saf..

[3]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .

[4]  Min Xie,et al.  A value-based preventive maintenance policy for multi-component system with continuously degrading components , 2014, Reliab. Eng. Syst. Saf..

[5]  Jan L. Fan,et al.  Constrained coding and soft iterative decoding , 2001, Proceedings 2001 IEEE Information Theory Workshop (Cat. No.01EX494).

[6]  Hugo Hammer,et al.  Approximate forward-backward algorithm for a switching linear Gaussian model , 2011, Comput. Stat. Data Anal..

[7]  Wenbin Wang,et al.  A model to predict the residual life of rolling element bearings given monitored condition information to date , 2002 .

[8]  J. Movellan.  Tutorial on Hidden Markov Models , 2006 .

[9]  Jun Ni,et al.  Two-Zone Proportional Hazard Model for Equipment Remaining Useful Life Prediction , 2010 .

[10]  Keh-Shew Lu.  Failure Prediction for an On-Line Maintenance System in a Poisson Shock Environment. , 1977 .

[11]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[12]  Stefan Katzenbeisser,et al.  Computation of mutual information from Hidden Markov Models , 2010, Comput. Biol. Chem..