@Article{cmc.2026.084652,
AUTHOR = {Ying Yan, Yongqiang Yang, Cong Jiang, Bin Suo, Kai Sun},
TITLE = {Fusing Multi-Source Information for Reliability Assessment under Uncertainty: An Approach Integrating D-S Evidence Theory with Wiener Process Degradation Modeling},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27493},
ISSN = {1546-2226},
ABSTRACT = {Degradation data in practical reliability engineering are often scarce and heterogeneous, originating from multiple sources with varying degrees of uncertainty and conflict. Accordingly, this study proposes a hybrid framework that integrates Dempster–Shafer (D-S) evidence theory with the Wiener process for small-sample reliability assessment using multi-source heterogeneous data. First, a probabilistic non-uniform sampling method regularizes varied data sources and computes basic probability assignments (BPA). Second, a weight synthesis mechanism is constructed, where prior weights derived from prior knowledge are updated by evidence similarity quantified through the Expectation–Width (EW) distance, yielding posterior weights. Quantile sequences from each source are then fused via weighted aggregation to generate a time-series probability box. A Wiener process is subsequently employed to model the probability box (P-box) sequence, enabling interval reliability evaluation. Numerical simulations and a satellite gyroscope case study show that the method effectively fuses multi-source data, provides more comprehensive reliability intervals than single-source approaches, and significantly enhances evaluation robustness under small samples. The framework offers a systematic solution for multi-source data fusion and establishes a novel reliability assessment pathway under uncertainty, with broad applicability to aerospace and precision instrumentation systems.},
DOI = {10.32604/cmc.2026.084652}
}