Hybrid uncertainties-based analysis and optimization design of powertrain mounting systems

Cai, Bohao; Shangguan, Wen‐Bin; Lü, Hui; Tao, Bo

doi:10.1007/s11431-019-1477-8

Cited by 10 publications

(2 citation statements)

References 34 publications

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“…It is obviously not easy to obtain sufficient statistical data because of the limited samples and high expenses for long-time tests. Occasionally, hybrid uncertainty modeling can be a better choice [13,36,118,[133][134][135][136]. For example, the probability-interval hybrid uncertain model [137] is widely employed for mechanical systems subject to both probabilistic and non-probabilistic uncertain parameters.…”

Section: Classificationsmentioning

confidence: 99%

A state-of-the-art review on uncertainty analysis of rotor systems

Sinou

Zhu

et al. 2023

Mechanical Systems and Signal Processing

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Section: Classificationsmentioning

confidence: 99%

A state-of-the-art review on uncertainty analysis of rotor systems

Sinou

Zhu

et al. 2023

Mechanical Systems and Signal Processing

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“…Vehicle parameters are commonly assumed to be deterministic, but they can undergo variations due to design, manufacturing, assembly, and usage conditions [17,18]. For instance, the difference between the full and unladen mass of a bus can account for 40-50% of the vehicle's total mass.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis and Design of Air Suspension Systems for City Buses Based on Neural Network Model and True Probability Density

Yuan

2023

Machines

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The accuracy of uncertainty analysis in suspension systems is closely tied to the precision of the probability distribution of sprung mass. Consequently, traditional assumptions regarding the probability distribution fail to guarantee the accuracy of uncertainty analyses results. To achieve more precise uncertainty analysis outcomes, this paper proposes a data-driven approach for analyzing the uncertainties in bus air suspension systems. Firstly, a bus vehicle dynamics model is established to investigate the influence of sprung mass on suspension system performance. Subsequently, a deep neural network model is trained using road test data, for the accurate identification of the sprung mass. The historical mass of the bus is then computed using vehicle network data to obtain the true probability density of the sprung mass. Lastly, the real probability distribution of the sprung mass is utilized to perform uncertainty analysis on the bus suspension system, and the results are compared with those obtained by assuming a probability distribution. Comparative analysis reveals substantial disparities in uncertainty response, with a maximum relative error of 9% observed for wheel dynamic loads, thus emphasizing the significance of precise probability distribution information concerning the sprung mass.

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