The Active Noise Control System For Pipeline Noise Based On FxLMS Algorithm

With the continuous progress and development of industrial technology,the affect of domestic noise and industrial noise on people is gradually increasing.Being in a noisy environment for a long time will have a huge negative impact on people's physical and mental health.How to effectively reduce noise pollution has become an urgent problem that people need to solve.Traditional passive noise reduction methods have little effect on the elimination of mid-and low-frequency noise.To obtain a better noise reduction effect,it is necessary to increase the volume of the noise absorption system.As a new noise reduction method,the active noise control method has a very good noise reduction effect against the mid-and low-frequency noise,and has the advantages of small size and wide application range,which has aroused the research enthusiasm of researchers in the field of active noise control.This article is dedicated to reducing the mid-and low-frequency noise generated by the fan in the Heating,Ventilation and Air Conditioning(HVAC)system,so the pipeline sound field is the main research object,and the traditional active noise control method is sTab.due to the fixed step length.Performance and convergence speed of the ANC algorithm are always in contradiction.A variable step size minimum mean square error algorithm based on arctangent function is proposed,and an active noise controller based on ARM platform is designed and completed.Through simulation and experiment methods Verify the feasibility,practicability and advancement of the noise control system.Firstly,the paper introduces the basic principles of active noise control methods.By studying the Wiener filter and the search method of the error plane,this paper introduces the least mean square error(LMS)algorithm based on the finite-length unit impulse response filter(FIR)structure.The principle of the algorithm,and the update process of the adaptive parameters in the LMS algorithm is deduced.Secondly,based on the above foundation,the adaptive iterative process of Filtered-x Least Mean Square(FxLMS)algorithm and the Second-Path modeling method are introduced.This paper studies the reasons for the deterioration of the stability of the adaptive filtering algorithm and the slower convergence speed.Through the formula derivation of the classic Variable Step-Size Least Mean Square(VSS-LMS)algorithm,the convergence speed and reduction of the algorithm are analyzed.Causes of noise performance.Therefore,an improved Least Mean Square algorithm by using the arctangent function is proposed.And then,this paper studies the noise source in the pipeline noise,and analyses the frequency characteristics of the noise generated by the axial flow fan and models by Matlab.The transmission characteristics of sound waves in the pipeline are also studied,and Comsol Multiphysics software is used for finite element analysis.Next,this paper designs an adaptive active noise control hardware platform with STM32F746 processor as the core to realize the real-time communication and data analysis with PC.The design includes the type selection and circuit design of the reference microphone and error microphone,the type selection and circuit design of the main control board,and the type selection and design of the power amplifier.And finally realize the operation of the active noise control algorithm on the hardware platform.Finally,the paper combines the hardware and software of the active noise control system with the improved algorithm to verify the function and algorithm.In this paper,the experimental data is recorded and analyzed using Matlab on the computer side,and the noise reduction performance and convergence speed of other variable step-size FxLMS algorithms are compared.By comparing the improved method proposed in the paper with the other four active noise control algorithms,it is shown that the improved algorithm has obvious improvements in the convergence speed and noise reduction effect,and based on the current results and the problems in the debugging process carried out analysis and summary.