Operation and maintenance (O&M) costs account for a large proportion of the total costs for offshore wind energy. Performing a reasonable maintenance strategy is an effective approach to reduce O&M costs and gain more profits. In this paper, an opportunistic maintenance strategy for offshore wind turbine systems considering maintenance intervals of each subsystem is proposed to minimize the total maintenance cost. First, a Non-homogeneous Continuous-Time Markov Process based state transition model is established to study degradation process of subsystems. The influence of maintenance time schedule on the maintenance cost is studied to obtain the optimal maintenance intervals of each subsystems. Then, an opportunistic maintenance model considering economic dependencies between multiple subsystems is proposed to optimize the maintenance strategy by combining maintenance activities of individual subsystems to a grouping maintenance activity. A numerical example is used to indicate the significant effectiveness of the maintenance model. The result shows that the total maintenance cost of an offshore wind turbine system will be reduced by adopting the opportunistic maintenance strategy when compared with conventional preventive maintenance strategy.

Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods.