Research on rotor condition monitoring based on D-S evidence theory

Chen, Yuanchao; Wen, Guangrui; Dong, Xiaomin; Zhang, Zhifen

doi:10.1109/urai.2016.7733993

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…To solve these problems, we propose an improved Dempster-Shafer evidence theory (D-S theory) for tunnel water inrush risk analysis based on the fuzzy characteristics of multi-source geophysical data. The D-S theory is an effective means to deal with uncertain information, which can realize multi-source information fusion without prior information, and is widely used in expert systems, pattern recognition, intelligence analysis, and other fields [21][22][23][24]. In the face of evidence conflict, the results obtained using classical D-S theory are often ambiguous [25].…”

Section: Introductionmentioning

confidence: 99%

Improved Dempster–Shafer Evidence Theory for Tunnel Water Inrush Risk Analysis Based on Fuzzy Identification Factors of Multi-Source Geophysical Data

Ding

Yang

et al. 2022

Remote Sensing

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Water inrush is one of the most important risk factors in tunnel construction because of its abruptness and timeliness. Various geophysical data used in actual construction contain useful information related to groundwater development. However, the existing approaches with such data from multiple sources and sensors are generally independent and cannot integrate this information, leading to inaccurate projections. In addition, existing tunnel advanced geological forecast reports for risk projections interpreted by human operators generally contain no quantitative observations or measurements, but only consist of ambiguous and uncertain qualitative descriptions. To surmount the problems above, this paper proposes a tunnel water inrush risk analysis method by fusing multi-source geophysical observations with fuzzy identification factors. Specifically, the membership function of the fuzzy set is used to solve the difficulty in determining the basic probability assignment function in the improved Dempster–Shafer evidence theory. The prediction model of effluent conditions fuses seismic wave reflection data, ground penetrating radar data, and transient electromagnetic data. Therefore, quantitative evaluations of the effluent conditions are achieved, including the strand water, linear water, seepage and dripping water, and anhydrous. Experimental evaluations with a typical tunnel section were conducted, in which the state of the groundwater from a series of geological sketch reports in this sectionpaper were used as ground truth for verification. The experimental results revealed that the proposed method not only has high accuracy and robustness but also aligns well with different evidence effectively that generally contradicts manual interpretation reports. The results from 12 randomly selected tunnel sections also demonstrate the generalization abilities of the proposed method.

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Section: Introductionmentioning

confidence: 99%