FAULT DETECTION AND SEPARATION OF HYBRID ELECTRIC VEHICLES BASED ON KERNEL ORTHOGONAL SUBSPACE ANALYSIS

Yonghui Wang; Syamsunur Deprizon; Cong Peng; Zhiming  Zhang

doi:10.5937/jaes0-45837

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

DOI: https://doi.org/10.5937/jaes0-45837

Keywords: fault detection, fault separation, hybrid electric vehicles, kernel function, nonlinear problem, orthonormal subspace analysis

Abstract

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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