Assessing HMM and SVM for Condition-Based Monitoring and Fault Detection in HEV Electrical Machines

Riham Ginzarly (), Nazih Moubayed, Ghaleb Hoblos, Hassan Kanj, Mouhammad Alakkoumi and Alaa Mawas
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Riham Ginzarly: Department of Electrical and Electronics Engineering, Lebanese International University LIU, Bekaa 1801, Lebanon
Nazih Moubayed: LaRGES, CRSI, Faculty of Engineering 1, Lebanese University, Tripoli 1300, Lebanon
Ghaleb Hoblos: UNIROUEN/ESIGELEC/IRSEEM, 76000 Rouen, France
Hassan Kanj: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Mouhammad Alakkoumi: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Alaa Mawas: LaRGES, CRSI, Faculty of Engineering 1, Lebanese University, Tripoli 1300, Lebanon

Energies, 2025, vol. 18, issue 13, 1-16

Abstract: The rise of hybrid electric vehicles (HEVs) marks a shift away from traditional engines driven by environmental and economic concerns. With the rapid growth of HEVs worldwide, their reliability becomes of utmost concern; thus, guaranteeing the proper operation of HEVs is a crucial quest. Condition-based monitoring (CBM), which intends to observe different kinds of parameters in the system to detect defects and reduce any unwanted breakdowns and equipment failure, plays an efficient role in enhancing HEVs’ reliability and ensuring their healthy operation. The permanent magnet machine (PMM) is the most used electric machine in the electric propulsion system of HEVs, as well as the most expensive. Hence, the condition monitoring of this machine is of great importance. The magnet crack is one of the most severe faults that may arise in this machine. Artificial intelligence (AI) is showing high capability in the field of CBM, fault detection, and fault identification and prevention. Hence, the aim of this paper is to present two data-based fault detection approaches, which are the support vector machine (SVM) and the Hidden Markov Model (HMM). Their capability to detect primitive faults like tiny cracks in the machine’s magnet will be shown. Applying and evaluating various CBM methods is essential to identifying the most effective approach to maximizing reliability, minimizing downtime, and optimizing maintenance strategies. A strategy to specify the remaining useful life (RUL) of the defected element is proposed.

Keywords: hybrid electric vehicle; electric machine; fault detection; condition-based monitoring; hidden Markov model; support vector machine; RUL (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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