Integration of heterogeneous information in SHM models

Probabilistic models are desirable for diagnosis and prognosis to account for many sources of uncertainty. However, the probability distributions of system variables and the conditional probability relationships between components or subsystems may be unknown or incomplete. Useful information for such systems may be available from multiple sources and in varying formats such as operational and laboratory data, mathematical models, reliability data, or expert opinion. Conventional modeling techniques do not generally include all such information. This paper presents a methodology for constructing a system model for use in diagnosis and prognosis in the presence of such heterogeneous information. An attractive modeling paradigm for this methodology is the dynamic Bayesian network (DBN). A systematic approach is developed to incorporate various types of data in the DBN and to learn the probability distributions of the system variables as well as the conditional probability relationships between them. The structure and distribution parameters of the DBN are obtained via established learning methods. The proposed methodology is demonstrated for a hydraulic actuator system.