The International Journal of Acoustics and Vibration最新文献_第3页

Investigation of the Difference Between Ordinary and FSI Numerical Solution for Flutter of Tandem Compressor 串联式压缩机飘动的普通数值解法与 FSI 数值解法差异调查

The International Journal of Acoustics and Vibration Pub Date : 2023-12-28 DOI: 10.20855/ijav.2023.28.41972

Alireza Sekhavat Benis, R. A. Togh

引用次数: 0

Acoustic Modeling and Analysis of Automotive Air-Filters 汽车空气滤清器的声学建模与分析

The International Journal of Acoustics and Vibration Pub Date : 2023-12-28 DOI: 10.20855/ijav.2023.28.41959

D. Veerababu, C. Sachin, P.V.S. Subhashini, B. Venkatesham

引用次数: 0

International Congresses on Sound and Vibration 国际声学与振动大会

The International Journal of Acoustics and Vibration Pub Date : 2023-12-28 DOI: 10.20855/ijav.2023.28.4e110

Adrian Brown

引用次数: 0

The ICSV 29 in Prague—city of a hundred spires 布拉格的ICSV 29——这座城市有上百个尖塔

The International Journal of Acoustics and Vibration Pub Date : 2023-06-16 DOI: 10.20855/ijav.2023.28.2e108

Malcolm J. Crocker

引用次数: 0

High-Precision Modeling and Prediction of Acoustic Comfort for Electric Bus Based on BPNN and XGBoost 基于BPNN和XGBoost的电动客车声舒适性高精度建模与预测

The International Journal of Acoustics and Vibration Pub Date : 2023-06-16 DOI: 10.20855/ijav.2023.28.21922

E. Zhang, Yi Chen, Xianyi Chen, Junbo Zhang, Pengwu Xu, Jianming Zhuo

{"title":"High-Precision Modeling and Prediction of Acoustic Comfort for Electric Bus Based on BPNN and XGBoost","authors":"E. Zhang, Yi Chen, Xianyi Chen, Junbo Zhang, Pengwu Xu, Jianming Zhuo","doi":"10.20855/ijav.2023.28.21922","DOIUrl":"https://doi.org/10.20855/ijav.2023.28.21922","url":null,"abstract":"At present, the A-weighted sound pressure level inside electric buses has generally reached the industry decibel limit, and sound quality research is a considerable way to improve future vehicle performance. In this paper, 64 noise samples from eight electric buses are collected, with acoustic comfort as the evaluation index, the subjective evaluation tests are carried out by rank score comparison (RSC), and nine objective psycho-acoustic parameters of all the samples are calculated to form a basic database. Aiming at the high-precision modeling requirement of electric bus sound quality and taking objective parameters and acoustic comfort as input and output variables, two machine learning algorithms, back propagation neural network (BPNN) and extreme gradient boosting (XGBoost), are respectively performed to establish nonlinear comfort evaluation models through data training, and ultimately, based on sample data test and relative error comparison, the acoustic comfort evaluation model with prediction accuracy of 95.65% and its mathematical formula are determined. This lays a key technical foundation for the future evaluation and optimization of electric bus sound quality.","PeriodicalId":131358,"journal":{"name":"The International Journal of Acoustics and Vibration","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128580139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Dynamic Responses of a Tower Trane Tower and Payload Subjected to Elastic Jib in Radial Motion 弹性臂作用下塔架和载荷径向运动的动力响应

The International Journal of Acoustics and Vibration Pub Date : 2023-06-16 DOI: 10.20855/ijav.2023.28.21935

Ruiqi Gao, M. Dong, Dalong He, Runhui Feng

引用次数: 0

A Novel Robotic GWO LDI Modeling and Control for Nonlinear Systems 一种新型机器人非线性系统GWO LDI建模与控制方法

The International Journal of Acoustics and Vibration Pub Date : 2023-06-16 DOI: 10.20855/ijav.2023.28.21897

E. Zhang, Yi Chen, Xianyi Chen, Junbo Zhang, Pengwu Xu, Jianming Zhuo

{"title":"A Novel Robotic GWO LDI Modeling and Control for Nonlinear Systems","authors":"E. Zhang, Yi Chen, Xianyi Chen, Junbo Zhang, Pengwu Xu, Jianming Zhuo","doi":"10.20855/ijav.2023.28.21897","DOIUrl":"https://doi.org/10.20855/ijav.2023.28.21897","url":null,"abstract":"This works aims to develop a new and improved GWO (Grey Wolf Optimizer), the so-called Robotic GWO (RGWO). First, to improve GWO's update formula position with an optimal learning strategy, we adapt the algorithm to real mobile environments, including robots, so that tracking robots can move prey toward targets. Then, the nonlinear active suspension (AS) control system is linearized by a neural network (NN) based linear differential inclusion (LDI) using feedback and feedforward linearization. In theory, it is found that the general SM (Sliding Mode) optimal control cannot provide sudden optimal results for the active linearized suspension system, so a method is proposed to improve the shortcomings of the active linearized suspension system. By constructing an extended SM-optimal manifold function, an improved SM-optimal controller is designed, which incorporates information on the entire structure and the expected performance of the suspension. For comparison purposes, the performance of three kinds of controls: SM optimal refinement control, logic-fuzzy SM control, and PS (passive suspension), shows the proposed controller's advantages . Finally, our improved SM optimal control for nonlinear AS systems, in general, can achieve the actual nominal optimal suspension performance, as confirmed by the simulation results. The results also show that the improved SM optimal control method provides better robustness even when the operating conditions or parameters of the structure vary.","PeriodicalId":131358,"journal":{"name":"The International Journal of Acoustics and Vibration","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122694881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural Design and Optimization of Nonuniform Chiral Phononic Crystals for Vehicle Interior Noise Reduction 非均匀手性声子晶体的结构设计与优化

The International Journal of Acoustics and Vibration Pub Date : 2023-06-16 DOI: 10.20855/ijav.2023.28.21932

T. Yuan, Hui Guo, Yansong Wang, Pei Sun, Yi Wang

引用次数: 0

Research on Abnormal Feature Extraction and Early Fault Alarm Method of Rolling Bearing's Based on CDAE and KLD 基于CDAE和KLD的滚动轴承异常特征提取及早期故障报警方法研究

The International Journal of Acoustics and Vibration Pub Date : 2023-06-16 DOI: 10.20855/ijav.2023.28.21929

Zheng Qin, Qin Chang, Qiang Li, Yao Wang, Jie Wang, Weiwei Xu

引用次数: 0

Research on Rolling Bearing Fault Diagnosis Based on DRS Frequency Spectrum Image and Deep Learning 基于DRS频谱图像和深度学习的滚动轴承故障诊断研究

The International Journal of Acoustics and Vibration Pub Date : 2023-06-16 DOI: 10.20855/ijav.2023.28.21942

Zhuoxian Li, Hao Wang, Jiatai Chen, Zhexin Zhou, Wei Chen

{"title":"Research on Rolling Bearing Fault Diagnosis Based on DRS Frequency Spectrum Image and Deep Learning","authors":"Zhuoxian Li, Hao Wang, Jiatai Chen, Zhexin Zhou, Wei Chen","doi":"10.20855/ijav.2023.28.21942","DOIUrl":"https://doi.org/10.20855/ijav.2023.28.21942","url":null,"abstract":"Deep learning is gradually being widely used in fault diagnosis now, because deep learning networks are more advantageous in processing data, especially image data. However, research using frequency spectra image of fault signals as inputs to deep learning networks are extremely rare in the field of fault diagnosis. Therefore, a brand-new intelligent fault diagnosis method is proposed in this paper which combines discrete random separate (DRS) frequency spectrum images with deep learning networks (DRSFSI-DL). To investigate the fault diagnosis effects of the method mentioned above, several deep learning networks are utilized for comparisons, such as GoogLeNet, residual network, and Inception_ResNet_v2. The vibration fault frequency spectrum images processed by the DRS method are input to train several deep learning networks. Under the same circumstance of deep learning networks, the fault diagnosis using the DRS frequency spectrum image (DRSFSI), is also compared to the fault diagnosis using traditional frequency spectrum, including the power spectrum density (PSD) and cepstrum. The fault diagnosis results show that the proposed method has a better classification accuracy than the PSD image and cepstrum image, with the same deep learning networks. The fault diagnosis accuracy can reach up to 100.00% for some deep learning networks with better generalization performance than the PSD image and cepstrum image. Lastly, the proposed method is further verified using the brand-new bearing fault dataset, and excellent accuracy and generalization ability are achieved.","PeriodicalId":131358,"journal":{"name":"The International Journal of Acoustics and Vibration","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126953305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0