Abstract
Purpose
In this article, a new ANC algorithm is proposed to attenuate the noise of industrial fans. It was named MC-FXLMS/F algorithm. It has showed to have a higher level of attenuation in comparison to other classical algorithms found in the literature. It is seen that the performance of the MC-FXLMS/F is superior when applying specific levels for the reference signal and also considering a substantial number of filter coefficients.
Methods
In the literature, it is possible to find different methods to perform the attenuation of the noise generated by the blades pass frequency, being one of the methods the active noise control. As it is possible to predict the periodicity of noise generated by fans, it is possible to use synthesized signals to be used as a reference signal for an active noise control system. In this work, two classical ANC algorithms, namely the FXLMS and the C-FXLMS/F were also evaluated and implemented. They were compared to the new algorithm. in terms of efficiency (for different types of synthesized and narrowband reference signals). The power spectral density results were presented for variations in the signal levels with and without active noise control.
Results
The results showed the applicability of using pure tones to attenuate specific frequencies of the noise spectrum. The algorithms C-FXLMS/F and MC-FXLMS/F were shown to be more sensitive to high levels of the reference signal in contrast to the FXLMS algorithm that benefits from higher levels.
Similar content being viewed by others
References
Burgess John C (1981) Active adaptive sound control in a duct: a computer simulation. J Acoust Soc Am 70:715
Gérard A, Berry A, Masson P (2005) Control of tonal noise from subsonic axial fan. Part 2: active control simulations and experiments in free field. J Sound Vib 288(4–5):1077–1104
Nelson PA, Elliot S (1992) Active control of sound. Academic Press, London
Yeung YN, Yu P (1996) Chow Active noise control: evaluation in ventilation systems. Build Serv Eng Res Technol 17(4):191–198
Eriksson LJ, Allie MC, Bremigan CD, Gilbert JA (1988) Active noise control and specifications for fan noise problems. In: Proceedings of noise con 88, Purdue University, USA
Kostek TM (1997) Combining adaptive-passive and fully active noise control in ducts. In: Proceedings of the ASME noise control and acoustics division
Chen K, Paurobally R, Pan J, Qiu X (2015) Improving active control of fan noise with automatic spectral reshaping for reference signal. Appl Acoust 87:142–152
Song P, Zhao H (2019) Filtered-x least mean square/fourth (FXLMS/F) algorithm for active noise control. Mech Syst Signal Process 120:69–82
Sun G, Li M, Lim TC (2015) A family of threshold based robust adaptive algorithms for active impulsive noise control. Appl Acoust 97:30–36
Lu L, Zhao H, Chen C (2016) A normalized subband adaptive filter under minimum error entropy criterion. Signal Image Video Process 10(6):1097–1103
Reddy RM, Panahi IMS, Briggs R (2011) Hybrid FXRLS-FXNLMS adaptive algorithm for active noise control in fMRI application. IEEE Trans Control Syst Technol 19(2):474–480
George NV, Panda G (2012) A robust filtered-s LMS algorithm for nonlinear active noise control. Appl Acoust 73(8):836–841
Omour AMA, Zidouri A, Iqbal N et al (2016) Filtered-X least mean fourth (FXLMF) and leaky FXLMF adaptive algorithms. J Adv Signal Process 2016:39
Kuo SM, Morgan DR (1999) Active noise control: a tutorial review. Proc IEEE 87(6):702–943
Chang DC, Chu FT (2014) Feedforward active noise control with a new variable tap-length and step-size filtered-X LMS algorithm. IEEE Trans Audio Speech Lang Process 22(2):542–555
Douglas SC (1995): The fast fine projection algorithm for active noise control. In; Signals, systems and computers: conference record of the twenty-ninth asilomar conference on. IEEE, pp 1245–1249
Bouchard M (2003) Multichannel affine and fast affine projection algorithms for active noise control and acoustic equalization systems. IEEE Trans Speech Audio Process 11(1):54–60
Gonzalez A, Ferrer M, Diego M et al (2005) Fast filtered-x affine projection algorithm for active noise control. In: Applications of signal processing to audio and acoustics—IEEE Workshop, pp 162–165
Carini A, Sicuranza GL (2007) Optimal regularization parameter of the multichannel filtered-x affine projection algorithm. IEEE Trans Signal Process 55(10):4882–4895
Song JM, Park PG (2015) An optimal variable step-size affine projection algorithm for the modified filtered-x active noise control. Signal Process 114:100–111
Zhao H, Zeng X, Zhang XJ (2010) Adaptive reduced feedback FLNN filter for active control of nonlinear noise processes. Signal Process 90(3):834–847
Ferrer M, Diego M, Gonzalez A et al (2013) Convex combination filtered-x algorithms for active noise control systems. IEEE Trans Audio Speech Lang Process 21(1):156–167
Acknowledgements
This work was supported by CNPq (National Council for Scientific and Technological Development) and UFMG (Federal University of Minas Gerais).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Félix, F.B., de Castro Magalhães, M. & de Souza Papini, G. An Improved ANC Algorithm for the Attenuation of Industrial Fan Noise. J. Vib. Eng. Technol. 9, 279–289 (2021). https://doi.org/10.1007/s42417-020-00225-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42417-020-00225-2