Identification of vehicle suspension shock absorber squeak and rattle noise based on wavelet packet transforms and a genetic algorithm-support vector machine. (1st December 2016)

• Record Type: Journal Article
• Authors: Huang, Hai B.; Li, Ren X.; Huang, Xiao R.; Lim, Teik C.; Ding, Wei P.
• Is Part Of: Applied acoustics. Volume 113(2016)
• Page Start: 137
• Page End: 148
• Publication Date: 2016-12-01
• Subjects: 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); Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2016.06.016
• Languages: English
• ISSNs: 0003-682X

Abstract

Highlights: The interior S&R noise is proportional to the vibration of shock absorber. The WPSE criterion can extract more effective S&R features than the WPE criterion. A GA-SVM model has been proposed. The GA-SVM model outperforms the GA-BPNN model in effectiveness and efficiency. Abstract: 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.