Bayesian networks and decision theory-based forward multi-step troubleshooting strategy

A forward multi-step troubleshooting strategy generation algorithm based on Bayesian networks and decision-theory was proposed for sequential diagnosis and maintenance problems. Troubleshooting knowledge under uncertainty was compactly represented by Bayesian network model and inference algorithm was independent on practical application. The correlation-ship among observations described in influence diagrams was explored to select reasonable forward multi-step observations and make troubleshooting decision in order to reduce blindness of repair. To verify the proposed method, the random troubleshooting strategy, decision theory strategy and ideal strategy were selected as comparison. Simulation results indicate that the proposed algorithm can significantly decrease the total troubleshooting costs by increasing the number of reasonable observer operation and reducing the numbers of maintenance focus and actual repair operation.