Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-45837 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 21 article 1159 pages: 1192 -1202

Yonghui Wang
Department of Civil Engineering, Faculty of Engineering, Technology and Built Environment, UCSI University,Kuala Lumpur, Malaysia; College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China

Syamsunur Deprizon*
Department of Civil Engineering, Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia; Postgraduate Department, Universitas Bina Darma Palembang, Indonesia

Cong Peng
Department of Civil Engineering, Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia

Zhiming Zhang
Department of Civil Engineering, Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia

Driving quality and vehicles safety of hybrid electric vehicles (HEVs) are two hot-topic issues in automobile technology. Nowadays, research focuses to more intelligent and convenient HEVs fault detection methods. This paper will focus on the fault detection of HEV powertrain system with a data-driven algorithm. Orthonormal subspace analysis (OSA) is a newly proposed data-driven method which adds the ability of fault separation. Nonetheless, the linear OSA algorithm cannot effectively detect powertrain system faults, since these faults present complex nonlinear characteristics. A new kernel OSA (KOSA) method is proposed to transform the nonlinear problem into a linear problem through the mapping of kernel function and the dimensionality reduction technique of OSA. Testing results on a nonlinear model and real samples of XMQ6127AGCHEVN61 HEV show that KOSA address the nonlinear problems and it performs better than OSA and kernel principal component analysis (KPCA).

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