The problem of fault detection and isolation in complex plants can be effectively addressed by a hierarchical strategy involving successive narrowing of the search space of potential faults. A bond graph network is one means of achieving a hierarchical strategy based on the physical domains present in the plant. First, the multivariate statistical method of principal component analysis is used to reduce the data dimension. Second, a discrete wavelet transform is applied to abstract the dynamics at different scales. Thirdly, the Mahalanobis distance is applied to calculate the confidence level. Following a conclusion of the existence of a fault, isolation is achieved by comparing the time scale at which the violation occurred to the time scale associated with a physical domain. In the final step, a Bayesian network is employed to describe the conditional dependence between faulty domains and fault signatures. Two examples are presented to demonstrate these concepts. (C) 2010 Elsevier Ltd. All rights reserved.