Optimization and prediction of engine block vibration using micro-electro-mechanical systems capacitive accelerometer, fueled with diesel-bioethanol (water-hyacinth) blends by response surface methodology and artificial neural network

The current investigation is focused on the vibration signals analysis for health status diagnosis of the single-cylinder diesel engine fueled with bioethanol diesel mixture. The water hyacinth (WH) plants (Eichhornia crassipes) are used as raw materials for bioethanol production. The bio-ethanol obtained from WH has been mixed with diesel fuel (WBED) to various extent. Systematically designed experiments were conducted with different working parameters like load, fuel injection pressure (FIP), and compression ratio (CR) in a diesel engine. The Micro-Electro-Mechanical Systems (MEMS) capacitive accelerometer was used to get vibration signals from the engine while operating with blended fuels. The obtained experimental vibrations data have been used to predict the engine vibration by using Response Surface Methodology (RSM) technique and Artificial Neural Network (ANN). The experimental results have been compared with RSM and ANN prediction results. From results, it is elicited that the acceleration declines with the increase in load and CR. At all tested blends, FIP produces a significant effect on the engine block vibration. Among all blends, WBED 5 and WBED 10 produce less vibration as compared to other diesel bioethanol blends. At optimized operating condition the engine block vibration for WBED 5; the experimental acceleration is 0.016962 m/s2 and the predicted acceleration by RSM and ANN is 0.016182 m/s2 and 0.0166 m/s2, respectively. For WBED 10, the acceleration is 0.0172604 m/s2 and the predicted acceleration by RSM and ANN 0.016207 m/s2 0.017 m/s2, respectively, has been found.