Abstract
Uncertainty in data fusion applications has received great attention. Due to the effectiveness and flexibility in handling uncertainty, Dempster–Shafer evidence theory is widely used in numerous fields of data fusion. However, Dempster–Shafer evidence theory cannot be used directly for conflicting sensor data fusion since counterintuitive results may be attained. In order to handle this issue, a new method for data fusion based on weighted belief entropy and the negation of basic probability assignment (BPA) is proposed. First, the negation of BPA is applied to represent the information in a novel view. Then, by measuring the uncertainty of the evidence, the weighted belief entropy is adopted to indicate the relative importance of evidence. Finally, the ultimate weight of each body of evidence is applied to adjust the mass function before fusing by the Dempster combination rule. The validity of the proposed method is demonstrated in accordance with an experiment on artificial data and an application on fault diagnosis.
Highlights
In recent years, considerable attention has been paid to multisensor data fusion technology or information acquisition and environment sensing, such as the wireless network [1], fault detection [2], condition monitoring [3], and image processing [4,5,6]
Numerous methods have been proposed for multisensor modeling and data fusion, including rough sets theory [11], belief function theory [12], Dempster–Shafer evidence theory [13], fuzzy set theory [14, 15], Z-numbers [16], and D-numbers [17,18,19]
This paper proposes an improved data fusion method by integrating the negation of basic probability assignment (BPA) with the weighted belief entropy. e proposed method considers both the uncertainty measure of the negation of BPA and the uncertainty measure on the weight so that it can acquire more suitably weighted average evidence before applying the Dempster combination rule
Summary
Considerable attention has been paid to multisensor data fusion technology or information acquisition and environment sensing, such as the wireless network [1], fault detection [2], condition monitoring [3], and image processing [4,5,6]. En, the Dempster combination rule will be used to fuse the weighted average evidence This new data fusion method is applied on fault diagnosis of a motor rotor to validate its capacity in real applications. E rest of this paper is organized as follows: in Section 2, the preliminaries on Dempster–Shafer evidence theory, Shannon entropy, weighted belief entropy, negation of BPA, and some uncertainty measures in the Dempster–Shafer framework are briefly introduced; an improved data fusion method which is based on the weighted belief entropy and the negation of BPA is proposed in Section 3; Section 4 illustrates a numerical example to show the effectiveness of the proposed method; in Section 5, the proposed sensor data fusion method is adopted to an application in fault diagnosis; Section 6 gives a conclusion
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