Multi-source monitoring information fusion method for dam health diagnosis based on Wasserstein distance

Abstract

The fusion of multi-source monitoring information has become the main trend in the field of dam health diagnosis because of the increasing amount of monitoring data that can be obtained from different sensors. However, the Dempster–Shafer (D–S) evidence theory, an important method in multi-source information fusion, may produce counter-intuitive results when fusing conflicting pieces of evidence. To some extent, existing distance measures can deal with highly conflicting evidence, however, the fusion of completely conflicting evidence (indicated by the conflict coefficient K, K = 1) is typically ignored. This study mainly focuses on the fusion of pieces of completely conflicting evidence considering K = 1. The Wasserstein distance in the field of deep learning is introduced to the D–S evidence theory. Based on the foregoing, the belief Wasserstein–1 distance (BWD) method combined with basic probability assignment is proposed to measure evidence distance. The application of the BWD method in dam health diagnosis is presented to demonstrate the validity and effectiveness of this method in multi-source fusion with completely conflicting evidence.