Fault diagnosis is an important way to improve the reliability of complex systems.
Machine learning algorithm is an effective means to improve the efficiency of
fault diagnosis and Bayesian networks is widely used in the fault diagnosis
due to its advantages in uncertainty reasoning. Being unable to select the fault
paths effectively, the existing fault diagnosis algorithm based on Bayesian
network cannot detect faulty nodes accurately and has high computational complexity.
In this study Bayesian networks sensitivity analysis is introduced into fault
diagnosis and a kind of Bayesian network fault diagnosis algorithm, SA_FD, is
presented in complicated system. First, the formal model of Bayesian fault diagnosis
networks is given. Second, the model of how parent nodes influence their child
nodes is built based on sensitivity analysis. Last, sensitivity analysis of
the nodes are used to detect the faulty nodes based on heuristic path search
method, to overcome the blindness of existing algorithm in searching important
parent nodes and selecting the fault paths so as to improve performance of fault
diagnosis effectively. Experimental results show that SA_FD is more efficient
is than DFS and DFC obviously, although its complexity increases with the scale
of the network.