Title: A novel ensemble multiple kernel relevance vector regression modelling for reliability analysis

Authors: Manman Dong; Yongbo Cheng; Liangqi Wan

Addresses: School of Economy and Management, Zhejiang University of Water Resources and Electric Power, Hangzhou, 310018, China ' College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing, 210046, China ' School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing, 211106, China

Abstract: Reliability analysis is a crucial aspect of evaluating and enhancing product quality. A novel ensemble multiple kernel relevance vector regression (EMKRVR) modelling approach is introduced in this context. This approach incorporates adaptive learning strategies, effectively combining kernel functions to reduce the number of calls made to the performance function, thus improving efficiency and accuracy in reliability analysis. The EMKRVR model is further strengthened by the integration of Monte Carlo simulation (MCS), further enhancing its precision. Notably, an active learning function that focuses on areas with significant prediction errors is adopted. This allows the model to refine its predictions continuously. A hybrid efficient stopping criteria is employed for automatic termination. The results from three illustrative examples validate that our approach provides accurate failure probability estimates with fewer performance function evaluations compared to traditional methods. This method shows great effectiveness in the realm of quality improvement and product reliability analysis.

Keywords: reliability analysis; adaptive learning; ensemble multiple kernel; relevance vector regression; quality improvement.

DOI: 10.1504/IJPQM.2026.154370

International Journal of Productivity and Quality Management, 2026 Vol.48 No.2, pp.239 - 256

Received: 06 Nov 2023
Accepted: 29 Nov 2023
Published online: 25 Jun 2026 *

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