Sound quality evaluation of vehicle suspension shock absorber rattling noise based on the Wigner–Ville distribution

Huang, Hai; Li, Ren X.; Huang, Xiao; Yang, Ming; Ding, Wei P.

doi:10.1016/j.apacoust.2015.06.018

Cited by 46 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In one study, a quarter-car suspension system was used to investigate the S&R noise of a shock absorber and found that powerful impact acceleration along a shock absorber resulted in an intensive vibration of the car body, which can create a high S&R noise level [11]. Meanwhile, reports in the literature [8,12] have shown similar results. The methods used to extract the S&R characteristics and establish the relationships between the obtained features and human hearing perception are key factors in the identification of S&R noise.…”

Section: Introductionmentioning

confidence: 81%

Identification of vehicle suspension shock absorber squeak and rattle noise based on wavelet packet transforms and a genetic algorithm-support vector machine

Huang

et al. 2016

Applied Acoustics

Self Cite

View full text Add to dashboard Cite

Squeak and rattle (S&R) Wavelet packet transform (WPT) Wavelet packet energy (WPE) Wavelet packet sample entropy (WPSE) Genetic algorithm (GA) Support vector machine (SVM) a b s t r a c tThe 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.

show abstract

Section: Introductionmentioning

confidence: 81%

Identification of vehicle suspension shock absorber squeak and rattle noise based on wavelet packet transforms and a genetic algorithm-support vector machine

Huang

et al. 2016

Applied Acoustics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The time-frequency analysis algorithms, such as the short-time Fourier transform (STFT), discrete wavelet transform (DWT), wavelet packet analysis (WPA) and the Wigner-Ville distributions (WVD), were introduced into SQE engineering for sound feature extraction (virtual "ear") of the impact and nonstationary vehicle noises. [20][21][22] Originally used for signal processing, wavelet techniques have been developed and successfully applied in structure analysis, diagnosis of crack faults and wave propagation in structures. [39][40][41][42][43][44] Due to good time-frequency characteristics, the wavelet-based algorithms are usually considered for SQE of both stationary and nonstationary noises.…”

Section: Introductionmentioning

confidence: 99%

Roughness Evaluation Approach for Nonstationary Vehicle Noise Based on Wavelet Packet and Neural Network Techniques

Guo¹,

Wang²,

Wang³

et al. 2018

IJAV

View full text Add to dashboard Cite

Based on the wavelet packet decomposition (WPD) and artificial neural network (ANN) methods, this paper presents a new technique for auditory roughness evaluation (ARE) of nonstationary vehicle noise, named WPD-ANN-ARE model. According to sound transfer and perception by the human auditory system, the noise roughness of a sample vehicle under different conditions of constant speed, acceleration, and braking are calculated. After comparisons by the time-frequency analysis techniques in common use, a WPD model with approximately twentyone critical bands, which is specially designed by considering the auditory perception characteristics of human, is proposed for envelope feature extraction of vehicle noise. Taking the WPD extracted features as inputs and the calculated roughnesses as outputs, a back-propagation ANN with one hidden layer is trained and established for ARE of nonstationary vehicle noises. The verification results show that errors of the time-varying roughness calculated from the WPD-ANN-ARE are below 8.56 percent, which suggest a very good accuracy of the newly proposed ARE model. In applications, the WPD-ANN-ARE can be directly used in ARE of vehicle noises. And the modelling approach presented in this paper may be extended to other sound related fields for sound quality evaluation (SQE) in engineering.

show abstract

“…However, the noise was measured in the car interior, not directly near the shock absorber itself. Huang (2015) investigated the differences between a properly functioning shock absorber and a shock absorber with "rattling noise". Measured sound energy of both shocks in the time-frequency domain was mainly below 1 kHz; lower frequencies were represented more often.…”

Section: Introductionmentioning

confidence: 99%

Experimental Methodology for Acoustic Diagnostics of Shock Absorbers

Halama

Klapka

Mazůrek

2018

Acta Univ. Agric. Silvic. Mendelianae Brun.

View full text Add to dashboard Cite

The application of acoustic measurements brings about a new diagnostic method for evaluating the conditions of shock absorbers. In shock absorber diagnostics, it is advantageous to observe and understand what phenomena occur during the working cycle in the inner tube. Using a non‑destructive and non‑contact method can avoid dismantling whole device. For the research of this new acoustic method, a classic sound meter, an automotive and a train shock absorber were used. FFT analysis and concurrent filtration were applied for the measurement evaluation of obtained data. It has been proven that applying acoustic methods can lead to diagnostics of aeration in the shock absorbers. A defective shock absorber changes its damping characteristics as well as noise radiation compared with the properly functioning one; these differences in noise are measurable and quantifiable. The results show that characteristics of acoustic radiation of the aerated shock absorbers relate to the shock construction type.

show abstract

Sound quality evaluation of vehicle suspension shock absorber rattling noise based on the Wigner–Ville distribution

Cited by 46 publications

References 12 publications

Identification of vehicle suspension shock absorber squeak and rattle noise based on wavelet packet transforms and a genetic algorithm-support vector machine

Identification of vehicle suspension shock absorber squeak and rattle noise based on wavelet packet transforms and a genetic algorithm-support vector machine

Roughness Evaluation Approach for Nonstationary Vehicle Noise Based on Wavelet Packet and Neural Network Techniques

Experimental Methodology for Acoustic Diagnostics of Shock Absorbers

Contact Info

Product

Resources

About