The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform

Caichun He - One of the best experts on this subject based on the ideXlab platform.

  • application of sound intensity and partial coherence to identify Interior Noise sources on the high speed train
    Mechanical Systems and Signal Processing, 2014
    Co-Authors: Zhongqing Su, Guang Meng, Caichun He
    Abstract:

    Abstract In order to provide a quieter riding environment for passengers, sound quality refinement of rail vehicle is a hot issue. Identification of Interior Noise sources is the prerequisite condition to reduce the Interior Noise on high speed train. By considering contribution of Noise sources such as rolling Noise, mechanical equipment Noise, structure-borne Noise radiated by car body vibration to the Interior Noise, the synthesized measurement of sound intensity, sound pressure levels and vibration have been carried out in four different carriages on high speed train. The sound intensity and partial coherence methods have been used to identify the most significant Interior Noise sources. The statistical analysis results of sound intensity near window and floor on four carriages indicate that sound intensity near floor is higher than that near window at three traveling speeds. Ordinary and partial coherent analysis of vibro-acoustical signals show that the major internal Noise source is structural-borne sound radiated by floor vibration. These findings can be utilized to facilitate the reduction of Interior Noise in the future.

  • Application of sound intensity and partial coherence to identify Interior Noise sources on the high speed train
    Mechanical Systems and Signal Processing, 2014
    Co-Authors: Rongping Fan, Zhongqing Su, Guang Meng, Caichun He
    Abstract:

    In order to provide a quieter riding environment for passengers, sound quality refinement of rail vehicle is a hot issue. Identification of Interior Noise sources is the prerequisite condition to reduce the Interior Noise on high speed train. By considering contribution of Noise sources such as rolling Noise, mechanical equipment Noise, structure-borne Noise radiated by car body vibration to the Interior Noise, the synthesized measurement of sound intensity, sound pressure levels and vibration have been carried out in four different carriages on high speed train. The sound intensity and partial coherence methods have been used to identify the most significant Interior Noise sources. The statistical analysis results of sound intensity near window and floor on four carriages indicate that sound intensity near floor is higher than that near window at three traveling speeds. Ordinary and partial coherent analysis of vibro-acoustical signals show that the major internal Noise source is structural-borne sound radiated by floor vibration. These findings can be utilized to facilitate the reduction of Interior Noise in the future. © 2013 Elsevier Ltd.

Xin Chen - One of the best experts on this subject based on the ideXlab platform.

  • Simulation and Optimization to Vehicle Interior Noise in Mid and High Frequency Using VAone
    Applied Mechanics and Materials, 2012
    Co-Authors: Xin Chen, Xiaohua Geng
    Abstract:

    Finite Element-Statistical Energy Analysis (FE-SEA) hybrid method is better than SEA method for vehicle Interior Noise analysis in mid frequency. The Noise predictions using FE-SEA in mid and SEA in high frequency are good in consistent with the experiments, so the computer-aided simulation using above two methods is a good alternative to experiments. The results shows that the Poly Methyl Meth Acrylate (PMMA) instead of glass as the windshield material can reduce the Interior Noise at the driver’s ear in mid frequency, also lighten the body weight. The results shows the new polymer transparent material can looked as a good new way for vehicle Interior Noise reduction and body lightweighting.

  • Simulation to control the car Interior Noise in high frequency using SEA method
    2010 IEEE 11th International Conference on Computer-Aided Industrial Design & Conceptual Design 1, 2010
    Co-Authors: Xin Chen, Xiaohua Geng, Dengfeng Wang, Zhengdong Ma
    Abstract:

    The SEA simulation method to reduce car Interior Noise in high frequency is introduced by the properties modification of door glass. An SEA (Statistical Energy Analysis) model of a domestic car was built. The inputs of Interior Noise were got by the roads testing and other simulations. The accuracy of this model was validated by the comparison results of testing and software. The following simulation expresses that the car Interior Noise implemented laminated door glass is better than the one using tempered door glass in high frequency, and the thicker glass is not useful obviously for reducing high frequency Noise. If the total thickness and PVB (polyvinyl butyral) film are same and only modify the thickness of the outer glass and inner glass, the sound pressure level of car Interior Noise will be affected not so much. Compared to the application of tempered door glass, the application of laminated door glass can reduce the car Interior Noise efficiently in high frequency. And the research in this paper can be used to guide the car Interior Noise control using nonmetal materials in high frequency.

  • Analysis and control of automotive Interior Noise from powertrain in high frequency
    2009 IEEE Intelligent Vehicles Symposium, 2009
    Co-Authors: Xin Chen, Dengfeng Wang, Xue Yu
    Abstract:

    Application of statistical energy analysis (SEA) method on analysis and control of automotive Interior Noise from powertrain is introduced. An SEA model is presented for Interior Noise reduction. The SEA model is composed of a number of subsystems based on a 3D model with all the parameters for each subsystem. The inputs were measured through road tests in different measurable conditions, including inputs from the engine vibrations and the sound pressure of the engine bay. The accuracy in high frequency of the developed SEA model was validated through comparing the analysis results with the testing pressure level data at driver's ear. Noise contribution and sensitivity analysis of key sussystems were carried out by evaluating the power inputs curve. Then, some effective ways to reduce the car Interior Noise from powertrain were put forward with analyzing and simulation, which can be used to improve the performance of car Interior Noise. And, some conclusions were given.

  • Analysis and control of automotive Interior Noise from powertrain in high frequency
    2009 IEEE Intelligent Vehicles Symposium, 2009
    Co-Authors: Xin Chen, Dengfeng Wang, Xue Yu, Zhengdong Ma
    Abstract:

    Application of Statistical Energy Analysis (SEA) method on analysis and control of automotive Interior Noise from powertrain is introduced. An SEA model is presented for Interior Noise reduction. The SEA model is composed of a number of subsystems based on a 3D model with all the parameters for each subsystem. The inputs were measured through road tests in different measurable conditions, including inputs from the engine vibrations and the sound pressure of the engine bay. The accuracy in high frequency of the developed SEA model was validated through comparing the analysis results with the testing pressure level data at driver's ear. Noise contribution and sensitivity analysis of key sussystems were carried out by evaluating the power inputs curve. Then, some effective ways to reduce the car Interior Noise from powertrain were put forward with analyzing and simulation, which can be used to improve the performance of car Interior Noise. And, some conclusions were given.

  • Simulation of the autobody aerodynamics for car Interior Noise control
    2009 IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design, 2009
    Co-Authors: Xin Chen, Dengfeng Wang, Yunzhu Wu
    Abstract:

    For the analysis of car Interior Noise at high speed, the influence of autobody aerodynamics should be taken into account, besides the vibration and acoustic radiation of powertrain, the random excitation from road and the vibration inputs of tires, and so on. A model of CFD was built. And the most turbulent area of autobody surfaces was located. The spectrum of average aerodynamic pressure of monitoring points on front door window was also obtained by unsteady calculation. So, the more accurate analysis of car Interior Noise can be processed due to loading the power inputs listed above to the simulation model, which is looked as the foundation of further optimization of car Interior Noise control.

Zhongqing Su - One of the best experts on this subject based on the ideXlab platform.

  • application of sound intensity and partial coherence to identify Interior Noise sources on the high speed train
    Mechanical Systems and Signal Processing, 2014
    Co-Authors: Zhongqing Su, Guang Meng, Caichun He
    Abstract:

    Abstract In order to provide a quieter riding environment for passengers, sound quality refinement of rail vehicle is a hot issue. Identification of Interior Noise sources is the prerequisite condition to reduce the Interior Noise on high speed train. By considering contribution of Noise sources such as rolling Noise, mechanical equipment Noise, structure-borne Noise radiated by car body vibration to the Interior Noise, the synthesized measurement of sound intensity, sound pressure levels and vibration have been carried out in four different carriages on high speed train. The sound intensity and partial coherence methods have been used to identify the most significant Interior Noise sources. The statistical analysis results of sound intensity near window and floor on four carriages indicate that sound intensity near floor is higher than that near window at three traveling speeds. Ordinary and partial coherent analysis of vibro-acoustical signals show that the major internal Noise source is structural-borne sound radiated by floor vibration. These findings can be utilized to facilitate the reduction of Interior Noise in the future.

  • Application of sound intensity and partial coherence to identify Interior Noise sources on the high speed train
    Mechanical Systems and Signal Processing, 2014
    Co-Authors: Rongping Fan, Zhongqing Su, Guang Meng, Caichun He
    Abstract:

    In order to provide a quieter riding environment for passengers, sound quality refinement of rail vehicle is a hot issue. Identification of Interior Noise sources is the prerequisite condition to reduce the Interior Noise on high speed train. By considering contribution of Noise sources such as rolling Noise, mechanical equipment Noise, structure-borne Noise radiated by car body vibration to the Interior Noise, the synthesized measurement of sound intensity, sound pressure levels and vibration have been carried out in four different carriages on high speed train. The sound intensity and partial coherence methods have been used to identify the most significant Interior Noise sources. The statistical analysis results of sound intensity near window and floor on four carriages indicate that sound intensity near floor is higher than that near window at three traveling speeds. Ordinary and partial coherent analysis of vibro-acoustical signals show that the major internal Noise source is structural-borne sound radiated by floor vibration. These findings can be utilized to facilitate the reduction of Interior Noise in the future. © 2013 Elsevier Ltd.

Hai B Huang - One of the best experts on this subject based on the ideXlab platform.

  • identification of vehicle Interior Noise sources based on wavelet transform and partial coherence analysis
    Mechanical Systems and Signal Processing, 2018
    Co-Authors: Hai B Huang, Xiao R Huang, Ming L Yang, Wei P Ding
    Abstract:

    Abstract Vehicle Interior Noise has an important influence on the physical and psychological perceptions of passengers and their perception of vehicle quality. The identification of potential Noise sources is necessary to effectively reduce vehicle Interior Noise and/or improve perceived vehicle quality. In this paper, a subjective evaluation of Interior Noise and vibration measurements was conducted. The contributions of different Noise sources, such as the engine, transmission, and structure-borne Noises that radiate from the vibrations of the car body panels, were investigated using continuous wavelet transform and partial coherence analysis methods to identify major sources of vehicle Interior Noise. The continuous wavelet transform results indicated that most Interior Noise was low and middle-frequency Noise. The partial coherence analysis of the vibro-acoustical signals showed that the structural vibrations responsible for structure-borne Noise contribute more to Interior Noise than airborne Noise. This case study shows that one of the main sources of Interior Noise in the sample vehicle is the structure-borne Noise that radiates from the car body panels connected to the front sub-frame. This structure-borne Noise resulted from the low vibrational isolation ratio of the rear engine mount. In addition, to validate the effectiveness of the proposed method, another vehicle was tested to identify Interior Noise sources. For this vehicle, the structure-borne Noise clearly radiated from the panel suspension. These findings can be further applied to facilitate the reduction of vehicle Interior Noise.

  • sound quality prediction of vehicle Interior Noise using deep belief networks
    Applied Acoustics, 2016
    Co-Authors: Hai B Huang, Xiao R Huang, Ren X Li, Wei P Ding
    Abstract:

    Abstract The sound quality of vehicle Interior Noise strongly influences passengers’ psychological and physiological perceptions. To predict the sound quality of Interior Noise, a vehicle road test with four compact cars has been conducted. All recorded Interior Noise signals have been deNoised via a discrete wavelet transform (DWT) denoising procedure and subsequently evaluated subjectively through the anchor semantic differential (ASD) test by a jury. In addition, a novel prediction method, namely, regression-based deep belief networks (DBNs), which substitute the support vector regression (SVR) layer for the linear softmax classification layer at the top of the general DBN’s structure, has been proposed to predict the Interior sound quality. The parameter selection of the DBN model has been compared and studied using a grid search. In addition, four conventional machine-learning-based methods have been introduced to enable a comparison of the performance with the newly developed DBNs. Furthermore, the feature fusion ability of DBNs has been studied by varying the amount of information that the dataset offers. The results show the following: (1) The accuracy and robustness of the proposed DBN-based sound quality prediction approach are better than those of the 4 other referenced methods. (2) The multiple-feature fusing process can strongly affect the prediction performance. (3) Finally, the unsupervised pre-training process of the DBNs can enhance the information fusing ability. Finally, the newly proposed regression-based DBN approach may be extended to address other vehicle Noises in the future.

Dengfeng Wang - One of the best experts on this subject based on the ideXlab platform.

  • Car Interior Noise
    IEEE Vehicular Technology Magazine, 2011
    Co-Authors: Shuming Chen, Dengfeng Wang
    Abstract:

    To analyze the influence of the amplitude of different frequency bands of vehicle Interior Noise on subjective evaluation, an orthogonal array table was designed with 12 factors and three levels using the design of experiment (DOE) method. Furthermore, the range analysis was presented, and the optimal combinations for different categories were found. The sound quality can be improved by increasing, decreasing, or keeping the sound pressure level (SPL) in a certain frequency band.

  • Simulation to control the car Interior Noise in high frequency using SEA method
    2010 IEEE 11th International Conference on Computer-Aided Industrial Design & Conceptual Design 1, 2010
    Co-Authors: Xin Chen, Xiaohua Geng, Dengfeng Wang, Zhengdong Ma
    Abstract:

    The SEA simulation method to reduce car Interior Noise in high frequency is introduced by the properties modification of door glass. An SEA (Statistical Energy Analysis) model of a domestic car was built. The inputs of Interior Noise were got by the roads testing and other simulations. The accuracy of this model was validated by the comparison results of testing and software. The following simulation expresses that the car Interior Noise implemented laminated door glass is better than the one using tempered door glass in high frequency, and the thicker glass is not useful obviously for reducing high frequency Noise. If the total thickness and PVB (polyvinyl butyral) film are same and only modify the thickness of the outer glass and inner glass, the sound pressure level of car Interior Noise will be affected not so much. Compared to the application of tempered door glass, the application of laminated door glass can reduce the car Interior Noise efficiently in high frequency. And the research in this paper can be used to guide the car Interior Noise control using nonmetal materials in high frequency.

  • Analysis and control of automotive Interior Noise from powertrain in high frequency
    2009 IEEE Intelligent Vehicles Symposium, 2009
    Co-Authors: Xin Chen, Dengfeng Wang, Xue Yu
    Abstract:

    Application of statistical energy analysis (SEA) method on analysis and control of automotive Interior Noise from powertrain is introduced. An SEA model is presented for Interior Noise reduction. The SEA model is composed of a number of subsystems based on a 3D model with all the parameters for each subsystem. The inputs were measured through road tests in different measurable conditions, including inputs from the engine vibrations and the sound pressure of the engine bay. The accuracy in high frequency of the developed SEA model was validated through comparing the analysis results with the testing pressure level data at driver's ear. Noise contribution and sensitivity analysis of key sussystems were carried out by evaluating the power inputs curve. Then, some effective ways to reduce the car Interior Noise from powertrain were put forward with analyzing and simulation, which can be used to improve the performance of car Interior Noise. And, some conclusions were given.

  • Analysis and control of automotive Interior Noise from powertrain in high frequency
    2009 IEEE Intelligent Vehicles Symposium, 2009
    Co-Authors: Xin Chen, Dengfeng Wang, Xue Yu, Zhengdong Ma
    Abstract:

    Application of Statistical Energy Analysis (SEA) method on analysis and control of automotive Interior Noise from powertrain is introduced. An SEA model is presented for Interior Noise reduction. The SEA model is composed of a number of subsystems based on a 3D model with all the parameters for each subsystem. The inputs were measured through road tests in different measurable conditions, including inputs from the engine vibrations and the sound pressure of the engine bay. The accuracy in high frequency of the developed SEA model was validated through comparing the analysis results with the testing pressure level data at driver's ear. Noise contribution and sensitivity analysis of key sussystems were carried out by evaluating the power inputs curve. Then, some effective ways to reduce the car Interior Noise from powertrain were put forward with analyzing and simulation, which can be used to improve the performance of car Interior Noise. And, some conclusions were given.

  • Simulation of the autobody aerodynamics for car Interior Noise control
    2009 IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design, 2009
    Co-Authors: Xin Chen, Dengfeng Wang, Yunzhu Wu
    Abstract:

    For the analysis of car Interior Noise at high speed, the influence of autobody aerodynamics should be taken into account, besides the vibration and acoustic radiation of powertrain, the random excitation from road and the vibration inputs of tires, and so on. A model of CFD was built. And the most turbulent area of autobody surfaces was located. The spectrum of average aerodynamic pressure of monitoring points on front door window was also obtained by unsteady calculation. So, the more accurate analysis of car Interior Noise can be processed due to loading the power inputs listed above to the simulation model, which is looked as the foundation of further optimization of car Interior Noise control.