A robust reliability prediction method using Weighted Least Square Support Vector Machine equipped with Chaos Modified Particle Swarm Optimization and Online Correcting Strategy

Accurate reliability prediction in engineering systems has drawn more and more attention over the past decades due to its important role in accessing the security condition of the system and providing safety operation basis, however, which still remains challenging caused by the mismatch of reliability prediction model especially for dynamic uncertainty of future sampled data. Therefore, a novel approach for reliability prediction with an optimal Online Correcting Strategy (OCS) combined with Weighted Least Square Support Vector Machine (WLSSVM) and Chaos Modified Particle Swarm Optimization (CMPSO) algorithm, named OCS–CMPSO–WLSSVM, are proposed, where WLSSVM models the functional relationship between input and output of the system, CMPSO optimizes the parameters of WLSSVM, and OCS modifies the model to reduce its mismatch as the system runs, respectively. The performance of the proposed model is demonstrated with five classic practical engineering examples and compared with the existing methods reported in literature in detail. The experimental results show that the proposed method not only has higher reliability prediction accuracy and robustness, but also has its superiority and applicability in other fields including time-ordered, feature-based regression problem and classification problem.