Abstract
Dempster–Shafer (DS) evidence theory is widely applied in multi-source data fusion technology. However, classical DS combination rule fails to deal with the situation when evidence is highly in conflict. To address this problem, a novel multi-source data fusion method is proposed in this paper. The main steps of the proposed method are presented as follows. Firstly, the credibility weight of each piece of evidence is obtained after transforming the belief Jenson–Shannon divergence into belief similarities. Next, the belief entropy of each piece of evidence is calculated and the information volume weights of evidence are generated. Then, both credibility weights and information volume weights of evidence are unified to generate the final weight of each piece of evidence before the weighted average evidence is calculated. Then, the classical DS combination rule is used multiple times on the modified evidence to generate the fusing results. A numerical example compares the fusing result of the proposed method with that of other existing combination rules. Further, a practical application of fault diagnosis is presented to illustrate the plausibility and efficiency of the proposed method. The experimental result shows that the targeted type of fault is recognized most accurately by the proposed method in comparing with other combination rules.
Highlights
Multi-source data fusion is the process of combining data obtained from different sources to make robust and complete evaluation on the certain system
By taking advantage of the belief entropy to quantify the information volume of the system and belief divergence to measure the difference among multi-source data, the credibility and the information volume, as two important factors of evidence are integrated to allocate the weight on the original evidence
The main contribution of the belief Jenson–Shannon divergence is that it replaces the probabilities distributions in JS divergence with basic probability assignment (BPA), so that Belief Jensen–Shannon (BJS) divergence can be applied in DS evidence theory to measure the difference between BPAs
Summary
Multi-source data fusion is the process of combining data obtained from different sources to make robust and complete evaluation on the certain system. The main shortcoming of these methods is the loss of associative properties, which greatly increases the computational complex degree especially when fusing thousands of pieces of evidence simultaneously Another strategy is to pre-process the original evidence and apply the classical DS evidence theory on the adjusted evidence multiple times. Euclidean distances between pieces of evidence before generating the weighted average evidence These combination methods presented quite reasonable fusing results, there is still some room for further improvement. A novel multi-source data combination method is proposed to handle the problem of highly-conflicted evidence fusion. By taking advantage of the belief entropy to quantify the information volume of the system and belief divergence to measure the difference among multi-source data, the credibility and the information volume, as two important factors of evidence are integrated to allocate the weight on the original evidence.
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