Bayesian formula is used to determine diagnosis sequence when several fault trees meet requirements. Bayesian prior probability is usually determined through expert or the user's subjective judgment and historical experience. If there is lack of expert experience, the determination of priori probability is very difficult. A real-time priori probability calculation method is proposed, which needn’t any priori-knowledge and can regulate automatic on the monitoring parameters. It takes into account the multiple diagnosis impact and more flexible than fixed priori probability according application.

In view of the disadvantage of current FAT-based fault diagnosis method in large-scale complicated system, fault diagnosis method of heavy NC machine based on FTA and Bayesian is discussed. Firstly, building fault trees with the help of reachability matrix, and to set the determinate conditions at every node of fault tree combining FTA with rule reasoning, the minimum cut set of fault reasons are determined as a result of step by step screening fault tree from top-down; Secondly, Bayesian method is integrated into the fault tree diagnostic method to calculate the posterior probability triggered by each fault tree in order to locate the fault tree where the fault had occurred and ensure high efficiency of fault diagnosis; Finally, B/S based intelligent fault diagnosis system for large-scale CNC equipments is developed, and the feasibility and efficiency of this method are proved in an example of fault diagnosis of Φ 160 NC boring and milling machine.

Three main technical problems on heavy equipments fault diagnosis are all solved in the following three aspects. Firstly, build fault trees with the help of reachability matrix. This fault tree building method is helpful to short the labor and time for building fault tree; secondly, design a rule-based reasoning algorithm on the fault tree to avoid the shortcomings of traditional minimal cut sets (MCS) solution; thirdly, design out a new knowledge database structure for fault trees and rules storage. This structure could meet the need of the algorithm; so that the algorithm could be run well with this knowledge database .At the end demonstrate advantages of the fault tree based diagnostic intelligent system with an example.

Because of their complex structures, diverse functions, and cross-correlation among subsystems, the fault of large-scale equipments occurs easily, but its trouble shooting is difficult. Firstly, a hybrid reasoning method is proposed, and the framework of fault diagnosis system is constructed according to characteristics of case based reasoning (CBR) and rule based reasoning (RBR). Secondly, CBR and RBR applied to fault diagnosis for large-scale NC equipments are analyzed. In RBR process, the fault tree was obtained by reachability matrix, and the rules knowledge is automatically generated by fault tree, so the bottleneck of acquiring rules knowledge is solved. Lastly, this method is used in the fault diagnosis of certain large-scale NC equipment, which verifies the validity of the method.