Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis

Wen Rong Jiang · Chunhe Xie · Miaoyan Zhuang · Yehang Shou · Yongchuan Tang

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Multi-Criteria Decision Making Machine Fault Diagnosis Techniques Fault Detection and Control Systems

Abstract

Sensor data fusion technology is widely employed in fault diagnosis. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-number, Z = (A, B), can represent the fuzziness and the reliability of information simultaneously, where the first component A represents a fuzzy restriction on the values of uncertain variables and the second component B is a measure of the reliability of A. In order to model and process the uncertainties in a sensor data fusion system reasonably, in this paper, a novel method combining the Z-number and Dempster-Shafer (D-S) evidence theory is proposed, where the Z-number is used to model the fuzziness and reliability of the sensor data and the D-S evidence theory is used to fuse the uncertain information of Z-numbers. The main advantages of the proposed method are that it provides a more robust measure of reliability to the sensor data, and the complementary information of multi-sensors reduces the uncertainty of the fault recognition, thus enhancing the reliability of fault detection.

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References

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2019Environmental assessment under uncertainty using Dempster–Shafer theory and Z-numbers 2018Sensor dynamic reliability evaluation based on evidence theory and intuitionistic fuzzy sets 2017Intuitionistic Fuzzy Power Aggregation Operator Based on Entropy and Its Application in Decision Making 2017An Evidential Axiomatic Design Approach for Decision Making Using the Evaluation of Belief Structure Satisfaction to Uncertain Target Values 2019Derive knowledge of Z-number from the perspective of Dempster–Shafer evidence theory

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