Fault Detection and Isolation in Sensors of an Internal Combustion Engine

Resumen: This work presents the fault detection and isolation (FDI) system design for the Throttle Position Sensor (TPS), Mass Air Flow (MAF), and Manifold Absolute Pressure (MAP) sensor of an internal combustion engine. The FDI system utilizes five multilayer perceptron artificial neural network (ANN), which were trained to estimate the value of each sensor using the crankshaft position (CKP) and the Air-Fuel ratio (AFR) sensors to generate analytical redundancy. When a fault is induced in one sensor, the FDI system replaces the faulty signal for an adequate estimation of the signal given by one ANN allowing uninterrupted operation of the internal combustion engine.

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Guzmán Zaragoza Miguel Maximiliano, Jarniel García Morales, Carlos Daniel Garcia Beltran, Manuel Adam-Medina, Ricardo Fabricio Escobar-Jiménez & Cervantes-Bobadilla Marisol. Fault Detection and Isolation in Sensors of an Internal Combustion Engine. Memorias del Congreso Nacional de Control Automático, pp. 1-6, 2020.

Palabras clave

Fault Detection and Isolation, Internal Combustion Engine, Artificial Neural Networks

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