LAUSR

Multimodule and multisensor data with different characteristics can reflect the overall operating status of the equipment from different angles. It is far from enough to monitor the operating status of equipment from a single perspective for this fails to take all valid information into account. However, data integration may face problems such as the curse of dimensionality and scale mismatches. Therefore, decision fusion, which needs to measure and manage evidence conflicts, has attracted extensive attention from scholars. However, most of the state-of-art methods focus on conflict management based on evidence itself and ignore the irrationality of conflict factor $K$ in measuring conflicts and the reliability of evidence sources in conflict management. In order to solve the above problems, a novel hybrid decision fusion approach is proposed in this article. First, divide data into modules and use the models in the model library to conduct cross-validation, thus obtaining the performance ranking. Then, select the optimal classifier of each module to obtain the evidence for decision fusion. Given the conflict of evidences, the Jensen–Shannon (JS) divergence is used to measure the conflict, and those high-conflict evidences will be revised through sensitivity and support analyses. Finally, the Dempster–Shafer (DS) evidence theory is used to integrate multimodule evidences to assess the status. To prove the feasibility and effectiveness of this approach, a realistic operational shield case in China is used.