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• W3025744230 endingPage "104573" @default.
• W3025744230 startingPage "104573" @default.
• W3025744230 abstract "This study aims to assess the risk-based fatigue life from each decomposition signals for various roads through the probabilistic analysis on the suspension system. Cyclic loading in fatigue failure and fatigue life-reliability was the main concern in the design and durability analysis. Different types of road surface signals, namely, rural, urban and highway roads were measured by a road test. These signals were decomposed using the Hilbert-Huang transform that contains the simpler component for describing the information from the original signal based on each decomposition mode. The degree of decomposition can be determined from the IMF level. The decomposition signals were then used in predicting the fatigue life of the given component. Akaike’s information criterion was used to determine suitable distribution of the experimental data. Fatigue life was calculated using the mean-cycle-to-failure (McTF) and the result was used to evaluate risk areas for each road surface. The McTF of rural, urban and highway road surfaces for the Morrow model were obtained at 2.4768 × 104, 1.5585 × 108 and 1.5491 × 106 cycles per block, respectively. The McTF of the Morrow model is the safe region that predicts the longer fatigue life compared to in the Coffin-Manson and SWT model. The risk was predicted to be 1.456 × 10–3 (rural), 1.221 × 10–3 (urban) and 1.890 × 10–3 (highway) failures per cycles per block based on the hazard rate function. Hence, high-risk and low-risk were found to occur at above and below of each risk prediction value of the various roads. Risk-based life cycle assessment from decomposition signals has the capability in assessing the fatigue life and risk area of the component under strain loading." @default.
• W3025744230 created "2020-05-21" @default.
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• W3025744230 date "2020-08-01" @default.
• W3025744230 modified "2023-10-02" @default.
• W3025744230 title "Risk-based life assessment of prediction models on suspension system for various road profiles" @default.
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• W3025744230 doi "https://doi.org/10.1016/j.engfailanal.2020.104573" @default.
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