Pattern recognition for HEV engine diagnostic using an improved statistical analysis

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

Development of Acetone Liquid Concentration Detection Sensor by Using Fiber Optic for Diabetic Level Detection

The design of current fiber optic sensor technology especially when related to the chemical still requires a certain degree of the quantitative reliability. The acetone is one of chemical found in the bodily fluid for diabetic person and it would be obvious during fasting. The acetone level could be monitored which is indicate the severity of the diabetes. In this experiment, 4 samples of acetone are used with different concentrations between 20% until 80%. It is represented four different level of diabetic. Fiber optic would be dipped in every concentration before measuring process. Each concentration presented in different line graph and analyze for sensitivity value using statistical method. By using 1550nm wavelength of light source, the maximum sensitivity of 1.064 is obtained at second slope for 40% of concentration, respectively. This is showed that fiber optic sensor could be use as diabetic level sensor. However this sensor would be in high performance at 40% concentration of acetone

Development of Acetone Liquid Concentration Detection Sensor by Using Fiber Optic for Diabetic Level Detection

The design of current fiber optic sensor technology especially when related to the chemical still requires a certain degree of the quantitative reliability. The acetone is one of chemical found in the bodily fluid for diabetic person and it would be obvious during fasting. The acetone level could be monitored which is indicate the severity of the diabetes. In this experiment, 4 samples of acetone are used with different concentrations between 20% until 80%. It is represented four different level of diabetic. Fiber optic would be dipped in every concentration before measuring process. Each concentration presented in different line graph and analyze for sensitivity value using statistical method. By using 1550nm wavelength of light source, the maximum sensitivity of 1.064 is obtained at second slope for 40% of concentration, respectively. This is showed that fiber optic sensor could be use as diabetic level sensor. However this sensor would be in high performance at 40% concentration of acetone