Pattern recognition for HEV engine diagnostic using an improved statistical analysis - Nor Azizi Ngatiman; Mohd. Zaki Nuawi; Mohd Irman Ramli (Penerbit Universiti Kebangsaan Malaysia, 2019)
Detecting early symptoms of engine failure is a crucial phase in an engine management system to prevent poor driving performance and experience. This paper proposes a Hybrid Electric Vehicle (HEV) engine diagnostics using a low-cost piezo-film sensor, an analysis with improved statistical method and verification by a Support Vector Machine (SVM). The current engine management system is unable to evaluate the performance of each cylinder operation. Eventually, it affects the whole hybrid vehicle system, particularly in the mode of charging and accelerating. This research aims to classify the combustion to monitor the condition of sparking activity of the engine by using the Z-freq statistical method. Piezo-film sensors were mounted on the Internal Combustion Engine (ICE) wall of each hybrid vehicle for vibration signal measurements. The engine runs at different speeds, the vibration signals were then recorded and analysed using the Z-freq technique. A machine learning tool referred to as Support Vector Machine was used to verify the classifications made by the Z-freq technique. A significant correlation was found between the voltage signal and calculated Z-freq coefficient value. Moreover, a good pattern was produced within a consistent value of the engine speed. This technique is useful for the hybrid engine to identify different stages of combustion and enable pattern categorisation of the measured parameters. These improved techniques provide strong evidence based on pattern representation and facilitate the investigator to categorise the measured parameters.