High-dimensional

non-linear

and non-Gaussian distributions of measured data in batch processes directly influence accuracy of detecting abnormal data. In order to integrate information of multi-source abnormal detection and increase detection accuracy

a method was proposed on the basis of multi-evidence fusion decision. With introduction of the Dempster-Shafer evidence theory

the main focal element was used to identify fake evidence and to recompute weight of evidences. The re-calculation on weight of evidences improved handling conflict evidences

reduced influence of conflict evidences on multi-evidence fusion decision

and enhanced detection accuracy of abnormal measured data. Furthermore

an abnormal detection model was constructed from multi-evidence fusion decision and was applied to decision-making of abnormal data detection in batch processes. The experimental results show that the proposed method can combine multi-evidence information and analyze conflict evidence effectively. Thus abnormal data detection for batch processes is achieved with low false and missing detection rates.