Active Control of Restricted Space Noise in DeepWell Using FXLMS Adaptive Control Algorithm

Authors

  • Qi Yu School of Resource & Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China

Abstract

Restricted spatial noise in deep wells does great harm to the human body, and it needs to be effectively controlled. This study explores the application of the Filtered-XLeast Mean Square (FXLMS) algorithm to deep well active noise control (ANC). Firstly, the FXLMS algorithm is briefly introduced, and then the simulation method of the FXLMS algorithm in Simulink environment was analyzed. In the experimental analysis, the size of the filter and convergence factor of the FXLMS algorithm were determined. Then the algorithm was used to control the simulated restricted space noise of deep wells. The results showed that the noise reduction level was better when the filter order was 30, and the calculation amount was small; when the convergence factor was 0.03, the convergence speed was high, and the stability was good. The energy value of the noise signal after control of the
deep well noise was greatly reduced, which showed that the algorithm had good control effect. The results of this study prove the effectiveness of FXLMS adaptive control algorithm in ANC and makes a contributions to the effective control of restricted space noise in deep wells.

Keywords: filtered-x least mean square, deep well noise, active noise control, simulation experiment

Cite As

Q. Yu, "Active Control of Restricted Space Noise in DeepWell Using FXLMS Adaptive Control Algorithm",
Engineering Intelligent Systems, vol. 27 no. 1, pp. 5-10, 2019.






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Published

2019-03-01

Issue

Vol. 27 No. 1 (2019): International Journal of Engineering Intelligent Systems

Section

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