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Vibro-Impact Analysis of Manual Transmission Gear Rattle and Its Sound Quality Evaluation

Journal Article
2017-01-0403
ISSN: 1946-391X, e-ISSN: 1946-3928
Published March 28, 2017 by SAE International in United States
Vibro-Impact Analysis of Manual Transmission Gear Rattle and Its Sound Quality Evaluation
Sector:
Citation: Wu, G. and Wu, H., "Vibro-Impact Analysis of Manual Transmission Gear Rattle and Its Sound Quality Evaluation," SAE Int. J. Commer. Veh. 10(1):184-192, 2017, https://doi.org/10.4271/2017-01-0403.
Language: English

Abstract:

Experimental schemes, frequency characteristics, subjective and objective sound quality evaluation and sound quality prediction model establishment of a certain mass-production SUV (Sport Utility Vehicle, SUV) manual transmission gear rattle phenomenon were analyzed in this paper. Firstly, vehicle experiments, including experiment conditions, vibration acceleration sensor and microphone arrangements and especial considerations in experiments, were described in detail. Secondly, through time-frequency analysis, broadband characteristics of manual transmission gear rattle noise were identified and vibro-impact of gear rattle occurs in the frequency range of 450~4000Hz on the vehicle idle condition and the creeping condition. Thirdly, based on bandwidth filtering processing of gear rattle noise, subjective assessment experiments by a paired comparison method were carried out. Evaluation results passed triangular loop verification and Spearman correlation coefficient examination, and then subjective annoyance results of each noise sample were calculated. Further, objective evaluation results, based on two physical acoustics parameters and six psychological acoustics parameters, were obtained respectively. Finally, comprehensive evaluation of subjective and objective results was analyzed by the MLR (Multiple Linear Regression, MLR) method. It’s concluded that AI (Articulation Index) was the appropriate parameter that’s closely related to subjective annoyance results, and correlation coefficient of AI and subjective annoyance results was up to 0.948. Sound quality prediction model of gear rattle was then established on the vehicle idle condition and the creeping condition. Overall in this paper, research achievements could be adopted to solve practical engineering problems (especially gear rattle problem), and furthermore it could reduce R&D (Research and Design, R&D) cycle, labor costs and material costs dramatically.