Dynamic Fault Detection in Automotive Engines using Principal Component Analysis based Hybrid Radial Basis Function and Input Training Neural Network Models

Abstract

For automobile engines, a novel fault detection (FD) system is created using PCA in this paper. To identify errors, two distinct neural networks are used. Radial basis function (RBF) is the first neural network, while input training neural network (ITNN) is the second. The mean value engine model (MVEM) with Matlab/Simulink is used to build the approach and evaluate its performance. The MVEM has been used to simulate three faults. The outputs of the MVEM are used as input data for the RBF. ITNN received the RBF output as an input and the output were the estimation of speed, pressure and temperature. According to the simulation results, faults with an amplitude variation of 10 – 20% were successfully identified under dynamic conditions across the whole working range. The corresponding detection thresholds are 0.36, 0.68, and 0.284 for speed, pressure and temperature respectively and any error exceeding the allowable threshold will be easily detected. Therefore, the simulation demonstrates that all three flaws may be easily identified and yields satisfactory results.