Abstract
In this study, we employ the active noise control (ANC) method to eliminate the low-frequency part of the noise generated by the rotation of the axial fan in heating, ventilation, and air-conditioning (HVAC) pipelines. Because the traditional variable step size least mean square (VSS-LMS) algorithm has poor tracking performance, we propose a variable step size filtered-X least mean square (FXLMS) algorithm based on the arctangent function to improve the adaptive filtering method of the convergence speed and noise cancellation effect. The step size of the proposed algorithm can be adjusted according to the error. When the error signal is significant, a larger step can be obtained, and when the error is small, the step size smoothness of the algorithm can be optimized. Compared with the traditional VSS-LMS algorithm, the convergence speed of the proposed algorithm is increased by 29%, the noise reduction effect is enhanced by 19%, and the mean square error (MSE) is reduced by 23% (0.0084). In addition, we developed a hardware experimental platform based on noise characteristics. In the noise reduction test using a GB/T 5836.2-06 standard PVC pipeline, the system reduced the noise by 12–17 dB.
Highlights
Noise has become one of the three significant pollutants that cannot be ignored globally due to the development of modern industry and the constant improvement of people’s desires for a higher quality of life
We propose a variable step size filtered-X least mean square (FXLMS) algorithm based on the arctangent function. e improved algorithm uses an inverse tangent function to link the error to the step size for the complex transformation rule of the step size in the VSS algorithm, and the transformed step size will find the optimum within a stable convergence range and does not require any new information for prediction. is optimization process is fully autonomous and does not require any perceived intervention, and the noise in the pipeline can be judged adaptively and effectively eliminated. e optimized convergence speed allows the system to adapt faster to changes in the external environment
Experimental Results and Discussion e proposed algorithm is compared with the fixed step size FXLMS, piecewise least mean square (LMS), AVSS-LMS, and Salman algorithms in the simulation
Summary
Noise has become one of the three significant pollutants that cannot be ignored globally due to the development of modern industry and the constant improvement of people’s desires for a higher quality of life. Salman used the arctangent and sign functions to constrain weights in the iterative process of the filter weights to increase the sparsity of the LMS algorithm and further improve its convergence in impulse noise environments. Simulations and experimental investigations show that using the proposed algorithm for the low-frequency part of the noise generated by the axial fan in the pipeline has a better effect. E rest of this study is organized as follows: Section 2 introduces the HVAC-ANC system and experimental setup employed; Section 3 presents the traditional FXLMS algorithm and analyzes the step change strategy of the VSS-LMS algorithm; Section 4 introduces the improved algorithm, and Section 5 examines the axial fan noise characteristics in HVAC pipeline; Section 6 presents the algorithm simulation and experimental results; Section 7 presents the conclusion of the study
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