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The squeak and rattle (S&R) noise of a vehicle’s suspension shock absorber substantially influences the psychological and physiological perception of passengers. In this paper, a state-of-the-art method, specifically, a genetic algorithm-optimized support vector machine (GA-SVM), which can select the most effective feature subsets and optimize the model’s free parameters, is proposed to identify this specific noise. A vehicular road test and a shock absorber rig test are conducted to investigate the relationship between these features, and then an approach for quantifying the shock absorber S&R noise is given. Pre-processed signals are decomposed through a wavelet packet transform (WPT), and two criteria, namely, the wavelet packet energy (WPE) and wavelet packet sample entropy (WPSE), are introduced as the feature extraction methods. Then, the two extracted feature sets are compared based on this genetic algorithm. Another advanced method, known as the genetic algorithm-optimized back propagation neural network (GA-BPNN), is introduced for comparison to illustrate the superiority of the newly developed GA-SVM model. The result shows that the WPSE can extract more useful features than the WPE and that the GA-SVM is more effective and efficient than the GA-BPNN. The proposed approach could be retrained and extended to address other fault identification problems.  相似文献
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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.  相似文献
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In this paper, a new sound metric–Sound Metric based on the Wigner–Ville distribution (SMWVD) – was developed to investigate the relationship between subjective evaluations and vehicle suspension shock absorber rattling noise, which substantially affects passengers’ psychological and physiological perceptions. A complete vehicle road test was conducted to measure the interior shock absorber noises, which were subjectively evaluated by 20 jurors. Conventional psychoacoustic indices, i.e. loudness, sharpness, roughness and fluctuation strength, were used to calculate the correlation coefficients between the objective and subjective evaluations, and then, the results were compared with the performance of the SMWVD. The results show that conventional sound metrics have poor relationships with the subjective ratings, while the SMWVD displayed a high correlation of >0.9 between the objective evaluation and the subjective evaluation. These results indicate that the SMWVD can be used to estimate the rattling noise index of a suspension shock absorber without jury evaluation.  相似文献
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