Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization

For the sound field reconstruction of large conical surfaces, current statistical optimal near-field acoustic holography (SONAH) methods have relatively poor applicability and low accuracy. To overcome this problem, conical SONAH based on cylindrical SONAH is proposed in this paper. Firstly, element...

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Autores principales:	Wei Cheng, Jinglei Ni, Chao Song, Muhammad Mubashir Ahsan, Xuefeng Chen, Zelin Nie, Yilong Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sound field reconstruction conical statistical optimal near-field acoustic holography (SONAH) truncated singular value decomposition combined regularization method noise monitoring and control Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/55c7ee2c2de745d4bd9984575a54f266
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spelling	oai:doaj.org-article:55c7ee2c2de745d4bd9984575a54f2662021-11-11T19:08:47ZConical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization10.3390/s212171501424-8220https://doaj.org/article/55c7ee2c2de745d4bd9984575a54f2662021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7150https://doaj.org/toc/1424-8220For the sound field reconstruction of large conical surfaces, current statistical optimal near-field acoustic holography (SONAH) methods have relatively poor applicability and low accuracy. To overcome this problem, conical SONAH based on cylindrical SONAH is proposed in this paper. Firstly, elementary cylindrical waves are transformed into those suitable for the radiated sound field of the conical surface through cylinder-cone coordinates transformation, which forms the matrix of characteristic elementary waves in the conical spatial domain. Secondly, the sound pressure is expressed as the superposition of those characteristic elementary waves, and the superposition coefficients are solved according to the principle of superposition of wave field. Finally, the reconstructed conical pressure is expressed as a linear superposition of the holographic conical pressure. Furthermore, to overcome ill-posed problems, a regularization method combining truncated singular value decomposition (TSVD) and Tikhonov regularization is proposed. Large singular values before the truncation point of TSVD are not processed and remaining small singular values representing high-frequency noise are modified by Tikhonov regularization. Numerical and experimental case studies are carried out to validate the effectiveness of the proposed conical SONAH and the combined regularization method, which can provide reliable evidence for noise monitoring and control of mechanical systems.Wei ChengJinglei NiChao SongMuhammad Mubashir AhsanXuefeng ChenZelin NieYilong LiuMDPI AGarticlesound field reconstructionconical statistical optimal near-field acoustic holography (SONAH)truncated singular value decompositioncombined regularization methodnoise monitoring and controlChemical technologyTP1-1185ENSensors, Vol 21, Iss 7150, p 7150 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sound field reconstruction conical statistical optimal near-field acoustic holography (SONAH) truncated singular value decomposition combined regularization method noise monitoring and control Chemical technology TP1-1185
spellingShingle	sound field reconstruction conical statistical optimal near-field acoustic holography (SONAH) truncated singular value decomposition combined regularization method noise monitoring and control Chemical technology TP1-1185 Wei Cheng Jinglei Ni Chao Song Muhammad Mubashir Ahsan Xuefeng Chen Zelin Nie Yilong Liu Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization
description	For the sound field reconstruction of large conical surfaces, current statistical optimal near-field acoustic holography (SONAH) methods have relatively poor applicability and low accuracy. To overcome this problem, conical SONAH based on cylindrical SONAH is proposed in this paper. Firstly, elementary cylindrical waves are transformed into those suitable for the radiated sound field of the conical surface through cylinder-cone coordinates transformation, which forms the matrix of characteristic elementary waves in the conical spatial domain. Secondly, the sound pressure is expressed as the superposition of those characteristic elementary waves, and the superposition coefficients are solved according to the principle of superposition of wave field. Finally, the reconstructed conical pressure is expressed as a linear superposition of the holographic conical pressure. Furthermore, to overcome ill-posed problems, a regularization method combining truncated singular value decomposition (TSVD) and Tikhonov regularization is proposed. Large singular values before the truncation point of TSVD are not processed and remaining small singular values representing high-frequency noise are modified by Tikhonov regularization. Numerical and experimental case studies are carried out to validate the effectiveness of the proposed conical SONAH and the combined regularization method, which can provide reliable evidence for noise monitoring and control of mechanical systems.
format	article
author	Wei Cheng Jinglei Ni Chao Song Muhammad Mubashir Ahsan Xuefeng Chen Zelin Nie Yilong Liu
author_facet	Wei Cheng Jinglei Ni Chao Song Muhammad Mubashir Ahsan Xuefeng Chen Zelin Nie Yilong Liu
author_sort	Wei Cheng
title	Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization
title_short	Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization
title_full	Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization
title_fullStr	Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization
title_full_unstemmed	Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization
title_sort	conical statistical optimal near-field acoustic holography with combined regularization
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/55c7ee2c2de745d4bd9984575a54f266
work_keys_str_mv	AT weicheng conicalstatisticaloptimalnearfieldacousticholographywithcombinedregularization AT jingleini conicalstatisticaloptimalnearfieldacousticholographywithcombinedregularization AT chaosong conicalstatisticaloptimalnearfieldacousticholographywithcombinedregularization AT muhammadmubashirahsan conicalstatisticaloptimalnearfieldacousticholographywithcombinedregularization AT xuefengchen conicalstatisticaloptimalnearfieldacousticholographywithcombinedregularization AT zelinnie conicalstatisticaloptimalnearfieldacousticholographywithcombinedregularization AT yilongliu conicalstatisticaloptimalnearfieldacousticholographywithcombinedregularization
_version_	1718431584715014144

Conical Statistical Optimal Near-Field Acoustic Holography with Combined Regularization

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