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Abstract

In this paper, a new dynamic model was proposed for identifying the rock hardness during the process of roadway tunnelling, thereby regulating the speed of the driving motor and the torque of the cutting head. The presented identification model establishes a multi-information feature database containing vibration signals in the y-axis, acoustic emission signals, cutting current signals, and temperature signals. Subsequently, we obtain the membership functions (MFs) of the given multiple signals with the amount of feature samples according to the principle of minimum fuzzy entropy. Furthermore, a rock hardness identification model was established based on multi-sensor information fusion and Dempster-Shafer (D-S) evidence theory. To prove the accuracy of the proposed model, an identification experiment was carried out through the cutting of a poured mixed rock specimen with five grades of hardness. As a result, the proposed identification model recognizes the rock hardness accurately for fifteen sampling points, which indicates the significance of the method with regard to the dynamic identification of rock hardness during the process of roadway tunnelling, and further provides data support for adjusting the speed of the cutting head adaptively, thereby achieving high efficiency tunnelling.

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Authors and Affiliations

H. Wang
ORCID: ORCID
S. Lu
M. Huang
X. Zhao
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Abstract

The quick leakage alarm and the accurate concentration prediction are two important aspects of natural gas safety monitoring. In this paper, a rapid monitoring method of sensor data sharing, rapid leakage alarm and simultaneous output of concentrations prediction is proposed to accelerate the alarm speed and predict the possible impact of leakage. In this method, the Dempster-Shafer evidence theory is used to fuse the trend judgment and the CUSUM (cumulative sum) and the Gauss-Newton iteration is used to predict the concentration. The experiment system based on the TGS2611 natural gas sensor was built. The results show that the fusion method is significantly better than the single monitoring method. The alarm time of fusion method was more advanced than that of the CUSUM method and the trend method (being averagely, 10.4% and 7.6% in advance in the CUSUM method and the trend method respectively). The relative deviations of the predicted concentration were the maximum (13.3%) at 2000 ppm (parts per million) and the minimum (0.8%) at 6000 ppm, respectively.
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Authors and Affiliations

Rongli Li
1
Yuexin Fan
2

  1. Faculty of Sanjiang University, Nanjing, China
  2. Faculty of Fujian Normal University, Fuzhou, China

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