Abstract: The core of computer numerical control (CNC) machine tool is the electrical system which controls and coordinates every part of CNC machine tool to complete processing tasks, so it is of great significance to strengthen the reliability of the electrical system. However, the electrical system is very complex due to many uncertain factors and dynamic stochastic characteristics when failure occurs. Therefore, the traditional fault tree analysis (FTA) method is not applicable. Bayesian network (BN) not only has a unique advantage to analyze nodes with multiply states in reliability analysis for complex systems, but also can solve the state explosion problem properly caused by Markov model when dealing with dynamic fault tree (DFT). In addition, the forward causal reasoning of BN can get the conditional probability distribution of the system under considering the uncertainty; the backward diagnosis reasoning of BN can recognize the weak links in system, so it is valuable for improving the system reliability. © 2016, Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg.

Key words: Bayesian network (BN); computer numerical control (CNC) machine tool; dynamic fault tree (DFT); electrical system; reliability