Fault diagnosis for mechanical system using dynamic Bayesian network

The present study focuses on the fault diagnosis of mechanical systems. Mechanical systems are considered with interconnected components that work together to achieve a common function or purpose. On the one hand, the fault diagnosis result is affected by strong dependence between each component. One the other hand, diagnostic results may be different at different time slices because of the performance degradation of components when the same fault symptoms are given. To deal with these problems in diagnosis, a dynamic Bayesian network (DBN) model is proposed. First, series and parallel systems are converted to a Bayesian network. And the relationship between components and reliability of the system is expressed by the Bayesian network. Then, the dynamic Bayesian network is established to model the dynamic degradation of components in a system under additional information by using the wear data. The parameters of the model are estimated by historical data. Finally, a case is investigated to verify the proposed model in this study. Fault diagnosis is conducted through a backward analysis of the DBN model proposed, and the weakest component is identified. The dynamic probabilities of the mechanical system are obtained through forwarding analysis of the DBN model.