Machine learning approaches to estimate suspension parameters for performance degradation assessment using accurate dynamic simulations

Yongjun Pan, Yu Sun, Zhixiong Li and Paolo Gardoni

Reliability Engineering and System Safety, 2023, vol. 230, issue C

Abstract: The suspension is one of the most vital systems in a vehicle. Its performance degrades over time due to road conditions. The suspension parameters of a moving vehicle are difficult or sometimes impossible to measure within the desired level of accuracy due to high costs and other associated impracticalities. In this work, we comprehensively investigate various machine learning (ML) methods to estimate the suspension parameters for assessing performance degradation. These methods include particle swarm optimization backward propagation, radial basis function neural network, generalized regression neural network, deep belief network, wavelet neural network, Elman neural network, extreme learning machine, and fuzzy neural network. During the training process, the vehicle states, calculated using a semi-recursive multibody model, are used as the inputs to predict the stiffness and damping coefficients of the suspensions. The semi-recursive multibody model considers the dynamic properties of all the components, which enables accurate vehicle states and characteristics. In addition, we compare the performance of the ML methods by using the reference data (multibody model data). The results show that the ML approaches can estimate accurate stiffness and damping coefficients in real-time.

Keywords: Parameter estimation; Machine learning; Vehicle suspension; Stiffness and damping coefficients; Dynamic simulation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832022005658
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005658

DOI: 10.1016/j.ress.2022.108950

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().