Fault tree analysis of feeding control system for computer numerical control heavy-duty horizontal lathes with multiple common cause failure groups

The lathes are basic machine tools for manufacturing cylindrical parts. In recent years, the DLseries computer numerical control(CNC) heavy-duty horizontal lathes(HDHLs) have been widely used in the transportation, energy and aviation industries. High availability of the CNC heavy-duty lathes is demanded to guarantee the efficiency and benefit of these manufacturing industries. As one of the key subsystems of the HDHLs, the feeding control system is studied in this paper on reliability modeling and reliability analysis. The fault tree analysis(FTA) method is used for reliability modelling of the feeding control system. Considering the multiple common cause failure groups(CCFGs) existing in the system, a modified beta factor parametric model is introduced to model the common cause failure(CCF) in system. The reliability of feeding control system is then obtained and the effect of CCF on the reliability of the whole system is studied as well.     

Reliability analysis of electrical system of computer numerical control machine tool based on bayesian networks

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.