Reducing cyclic testing time for components of automotive suspension system utilising the wavelet transform and the Fuzzy C-Means

Putra, T. E.; Abdullah, Shahrum; Schramm, Dieter; Nuawi, Mohd. Zaki; Brückmann, Tobias

doi:10.1016/j.ymssp.2016.12.001

Cited by 52 publications

(29 citation statements)

References 45 publications

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“…Power spectral density is a common approach in frequency domain analysis to identify the dominant frequency obtained. Although the frequency domain approach can reveal the amplitude of the signal frequency, the disadvantage is that it cannot pinpoint information about when that particular dominant frequency occurred [4]. The time domain and frequency domain approaches adopt linear analysis, but the actual random loading conditions on structures are by nature non-linear, with a varying interaction between loadings, changes in road roughness and turbulence loads.…”

Section: Introductionmentioning

confidence: 99%

Strain Signal Characterisation Using the 4th Order of Daubechies Wavelet Transform for Fatigue Life Determination

Rahim

Singh

Abdullah

et al. 2018

IJIE

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This paper presents the significance of Discrete Wavelet Transform to provide more accuracy by using the Wavelet (Db4) Daubechies approach to analyse original signals obtained from the actual responses of an automotive suspension system. The time-frequency domain considers both time and frequency parameters, making this approach more efficient compared to the time domain and frequency domain approaches. An original signal was obtained from three road types: highway road, rural road and residential road. These signals were classified into 12 levels of decomposition where each level contained its own frequency range. The decomposed signals were then analysed using fatigue analysis to obtain the fatigue damage at each interval, which was then compared to the original signal. Results show that the decomposition signals from levels 1 to level 2 for highway and residential roads and level 1 to level 3 for rural roads gave a significant value of fatigue life located in the range of 2:1 and 1:2 in the fatigue life prediction graph. In summary, the Daubechies (Db4) Wavelet approach is capable of correlating the fatigue life of those components that contribute to the failure of a suspension system.

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Section: Introductionmentioning

confidence: 99%

Strain Signal Characterisation Using the 4th Order of Daubechies Wavelet Transform for Fatigue Life Determination

Rahim

Singh

Abdullah

et al. 2018

IJIE

Self Cite

View full text Add to dashboard Cite

show abstract

“…To obtain a good suspension design, repeated processes of analysis were required because it promise a good ride quality of the vehicle and able to withstand required repeated cyclic loadings. Hence, many researches have been focused on automotive ride [2,3] and suspension coil spring fatigue [4,5]. For objective ride analysis, the vehicle was usually modelled with road excitation input.…”

Section: Introductionmentioning

confidence: 99%

“…They used road characteristics in cases of bump, pothole and bump for full vehicle and root mean square (RMS) acceleration as indicator for vehicle ride comfort. Putra et al [5] performed a fatigue analysis on coil spring using the collected strain signals on a spring with different road conditions. Based on these works, it has shown that the fatigue life and ride quality assessments of a vehicle pos-sessed a similarity which was the interaction of movement of the spring and vehicle body to the road excitations.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Spring Durability for Automotive Ride Using Artificial Neural Network Analysis

Kong

Abdullah²,

Schramm

et al. 2018

IJET

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This paper presents the establishment of a relationship between coil spring fatigue life and automotive vertical vibration using neural network. During an automotive suspension design process, the suspension components are designed with the consideration of structure strength and fatigue life as well as the effects toward automotive ride. Hence, it is important to have a functional mathematical model to predict the fatigue life and automotive life simultaneously. To build the mathematical model, a multibody kinematic quarter model of suspension system was constructed to simulate force and acceleration time histories from the suspension system and the sprung mass of the vehicle model. The force time histories were used to predict the fatigue life of the coil spring while the acceleration time histories were converted into ISO vertical vibration index. A neural network model was created and used to fit the spring fatigue life and vehicle vertical vibration into a mathematical function. The neural network with 1 hidden layer and 2 neurons has shown a good fitting of the data with coefficient of determination as high as 0.88, 0.98, 0.96 for training, validation and testing, respectively. This constructed neural network serves to predict the vehicle vertical vibration using the spring fatigue life and suspension natural frequencies as input, and hence reduce the automotive suspension design process.

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“…Based on the results, they found that the amplitude of the strain signal affected the value of RMS and Kurtosis. Ananthakrishnan et al [6], conducted a study on vehicle vibration using Power Spectral Density (PSD) and in another study, Putra et al [7] extracted the features from the strain signal and predicted the fatigue damage using wavelet transform. Accordingly, wavelet transform can detect the areas that contain high fatigue damage in the strain signal and extract the features.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Classification for Fatigue Feature Extraction Parameters Based on Road Surface Response

Yunoh

Abdullah²,

Singh

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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The aim of this paper is for classification for fatigue feature extraction parameters based on road surface response using the artificial neural network (ANN) technique. It is important for classification of the fatigue damage of automotive suspension as it is considers the random strain loading from the road surface contributed from complex variable amplitude loadings. In this study, the proposed method captured that strain signal collected from the car coil spring during the road test. Hence, the prediction of fatigue life need to assess based on actual loading to ensure the prediction results are accurate. The high amplitude segments were extracted from the strain signals using the discrete wavelet transform. This approach provides an advantage in assessing the signals containing random loads for both discontinuities and smooth components for the areas that contain high fatigue damage in the strain signal. From this, three significant fatigue features extraction parameters such as kurtosis, wavelet energy coefficients and fatigue damage were classified based on the similarities using ANN classification technique. This is important in analysing the clustering and classification were used in detection of fatigue damage according to the response of the various road surface condition. The results show the classifications using ANN give the accuracy based on the coefficient of determinant, R= 0.85 for all data. From on the accuracy of the ANN, it can be concluded that the discrete wavelet transform as a pre-processing method to extract the features from the signal for classification level of fatigue damage for the coil spring according to the response of loading based on road surface.

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Reducing cyclic testing time for components of automotive suspension system utilising the wavelet transform and the Fuzzy C-Means

Cited by 52 publications

References 45 publications

Strain Signal Characterisation Using the 4th Order of Daubechies Wavelet Transform for Fatigue Life Determination

Strain Signal Characterisation Using the 4th Order of Daubechies Wavelet Transform for Fatigue Life Determination

Characterizing Spring Durability for Automotive Ride Using Artificial Neural Network Analysis

Artificial Neural Network Classification for Fatigue Feature Extraction Parameters Based on Road Surface Response

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