Xiaolong Li, Chihua Lu, Wan Chen, Yawei Zhu, Can Cheng
{"title":"道路噪声主动控制中参考传感器快速优化配置研究","authors":"Xiaolong Li, Chihua Lu, Wan Chen, Yawei Zhu, Can Cheng","doi":"10.3397/1/377126","DOIUrl":null,"url":null,"abstract":"The noise reduction performance of the active road noise control (ARNC) system highly depends on the location of the reference sensors. Generally, the optimal sensor locations are evaluated by calculating the multiple coherence function (MCF) between all possible reference signal combinations with road noise. However, this trial-and- error method becomes time-consuming when the number of candidate locations is large. The transfer path analysis method can select the optimal sensor locations quickly while with low accuracy. Therefore, this article proposes two fast optimal sensor placement (FOSP) methods, namely, Wiener filter (WF)-FOSPand the MCF-FOSP, respectively. In both methods, the sensors are iteratively extended to the desired number, and each added sensor maximizes the predicted noise reduction of this iteration loop. Numerous ARNC simulations based on measured signals are conducted to illustrate the performance of the proposed two methods in terms of efficiency and accuracy. The results demonstrate that the WF-FOSP method provides the best comprehensive performance. The data analysis for one operating condition takes three minutes, and the absolute error is within 5% with respect to the benchmark. In addition, two schemes are discussed to obtain a set of sensor locations compatible with the noise reduction requirement of different operating conditions. The sensor locations can achieve a maximum average noise reduction of 7.29 dB(A) under four operating conditions.","PeriodicalId":49748,"journal":{"name":"Noise Control Engineering Journal","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on fast optimal reference sensor placement in active road noise control\",\"authors\":\"Xiaolong Li, Chihua Lu, Wan Chen, Yawei Zhu, Can Cheng\",\"doi\":\"10.3397/1/377126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The noise reduction performance of the active road noise control (ARNC) system highly depends on the location of the reference sensors. Generally, the optimal sensor locations are evaluated by calculating the multiple coherence function (MCF) between all possible reference signal combinations with road noise. However, this trial-and- error method becomes time-consuming when the number of candidate locations is large. The transfer path analysis method can select the optimal sensor locations quickly while with low accuracy. Therefore, this article proposes two fast optimal sensor placement (FOSP) methods, namely, Wiener filter (WF)-FOSPand the MCF-FOSP, respectively. In both methods, the sensors are iteratively extended to the desired number, and each added sensor maximizes the predicted noise reduction of this iteration loop. Numerous ARNC simulations based on measured signals are conducted to illustrate the performance of the proposed two methods in terms of efficiency and accuracy. The results demonstrate that the WF-FOSP method provides the best comprehensive performance. The data analysis for one operating condition takes three minutes, and the absolute error is within 5% with respect to the benchmark. In addition, two schemes are discussed to obtain a set of sensor locations compatible with the noise reduction requirement of different operating conditions. The sensor locations can achieve a maximum average noise reduction of 7.29 dB(A) under four operating conditions.\",\"PeriodicalId\":49748,\"journal\":{\"name\":\"Noise Control Engineering Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Noise Control Engineering Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3397/1/377126\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ACOUSTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Noise Control Engineering Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3397/1/377126","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ACOUSTICS","Score":null,"Total":0}
Research on fast optimal reference sensor placement in active road noise control
The noise reduction performance of the active road noise control (ARNC) system highly depends on the location of the reference sensors. Generally, the optimal sensor locations are evaluated by calculating the multiple coherence function (MCF) between all possible reference signal combinations with road noise. However, this trial-and- error method becomes time-consuming when the number of candidate locations is large. The transfer path analysis method can select the optimal sensor locations quickly while with low accuracy. Therefore, this article proposes two fast optimal sensor placement (FOSP) methods, namely, Wiener filter (WF)-FOSPand the MCF-FOSP, respectively. In both methods, the sensors are iteratively extended to the desired number, and each added sensor maximizes the predicted noise reduction of this iteration loop. Numerous ARNC simulations based on measured signals are conducted to illustrate the performance of the proposed two methods in terms of efficiency and accuracy. The results demonstrate that the WF-FOSP method provides the best comprehensive performance. The data analysis for one operating condition takes three minutes, and the absolute error is within 5% with respect to the benchmark. In addition, two schemes are discussed to obtain a set of sensor locations compatible with the noise reduction requirement of different operating conditions. The sensor locations can achieve a maximum average noise reduction of 7.29 dB(A) under four operating conditions.
期刊介绍:
NCEJ is the pre-eminent academic journal of noise control. It is the International Journal of the Institute of Noise Control Engineering of the USA. It is also produced with the participation and assistance of the Korean Society of Noise and Vibration Engineering (KSNVE).
NCEJ reaches noise control professionals around the world, covering over 50 national noise control societies and institutes.
INCE encourages you to submit your next paper to NCEJ. Choosing NCEJ:
Provides the opportunity to reach a global audience of NCE professionals, academics, and students;
Enhances the prestige of your work;
Validates your work by formal peer review.