Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle.

In this research, fault detection and diagnosis (FDD) scheme for isolating the damaged injector of an internal combustion engine is formulated and experimentally applied. The FDD scheme is based on a temporal analysis (statistical methods), as well as in a frequency analysis (fast Fourier transform) of the fuel rail pressure. The arrangement of the scheme consists of three coupled artificial neural networks (ANNs) to classify the faulty injector correctly. The ANNs were trained considering five different scenarios, one scenario without fault in the injection system, and the other four scenarios represent a fault per injector (1 to 4). The Levenberg-Marquardt (LM), BFGS quasi-Newton, gradient descent (GD), and extreme learning machine (ELM) algorithms were tested to select the best training algorithm to classify the faults. Experimental results obtained from the implementation in a VW four-cylinder CBU 2.5L vehicle in idle operating conditions (800 rpm) show the effectiveness of the proposed FDD scheme.

Sensor fault detection and isolation via high-gain observers: application to a double-pipe heat exchanger.

This paper deals with fault detection and isolation (FDI) in sensors applied to a concentric-pipe counter-flow heat exchanger. The proposed FDI is based on the analytical redundancy implementing nonlinear high-gain observers which are used to generate residuals when a sensor fault is presented (as software sensors). By evaluating the generated residual, it is possible to switch between the sensor and the observer when a failure is detected. Experiments in a heat exchanger pilot validate the effectiveness of the approach. The FDI technique is easy to implement allowing the industries to have an excellent alternative tool to keep their heat transfer process under supervision. The main contribution of this work is based on a dynamic model with heat transfer coefficients which depend on temperature and flow used to estimate the output temperatures of a heat exchanger. This model provides a satisfactory approximation of the states of the heat exchanger in order to allow its implementation in a FDI system used to perform supervision tasks.