Reliability Analysis of Electrical System of Computer Numerical Control Machine Tool Based on Bayesian Networks

• Published in: Shanghai jiao tong da xue xue bao Vol. 21; no. 5; pp. 635 - 640
• Main Author: 黄土地 晏晶 姜梅 彭卫文 黄洪钟
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 01.10.2016; Springer Nature B.V
• Subjects: Architecture; Bayesian analysis; Complex systems; Computer numerical control; Computer Science; Conditional probability; Control systems; Dynamical systems; Dynamics; Electrical Engineering; Engineering; Fault tree analysis; Life Sciences; Machine tools; Markov chains; Materials Science; Mathematical analysis; Mathematical models; Network reliability; Nodes; Numerical controls; Probability distribution; Reasoning; Reliability analysis; System reliability; Task complexity; A; dynamic fault tree (DFT); electrical system; reliability; computer numerical control (CNC) machine tool; TG 531; Bayesian network (BN)
• ISSN: 1007-1172; 1995-8188
• DOI: 10.1007/s12204-016-1775-3

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.