Multiple fault detection and isolation using artificial neural networks in sensors of an internal combustion engine

Technology advances rapidly in vehicles with internal combustion engines; therefore, detecting vehicles' faults becomes more complex, primarily due to the increased usage of sensors and actuators to improve engine performance. Specialized instruments for detecting vehicle faults can sometimes show errors in multiple sensors when there is only one issue in the system, and they are not responsible for isolating faults. Then, it must add superior algorithms for fault detection and isolation (FDI) in the Internal Combustion Engine to these diagnostic instruments. For this reason, this research focuses on designing an individual and multiple fault detection and isolation system based on artificial neural networks for an internal combustion engine. The proposed scheme detects individual and multiple faults in the throttle position sensors (TPS), mass air-flow (MAF), and manifold absolute pressure (MAP). The FDI system uses five artificial neural networks (ANN). Each ANN estimates the value of two sensors using the engine speed and the air-fuel ratio (AFR) signal to generate analytical redundancy. When a sensor fault occurs, the FDI system substitutes the faulty signal with a healthy signal estimate provided by an ANN. Suppose a second fault arises in another sensor. The FDI system can replace the faulty signal with the ANN's estimated signal, allowing the internal combustion engine to run even with multiple faults.