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Fault detection on automotive engine components is an important feature that motivates research from different engineering areas due to the interest of automakers in its potential to increase safety, reliability, and lifespan and to reduce pollutant emissions, fuel consumption, and maintenance costs. The fault detection can be applied to several types of maintenance strategies, ranging from finding the faults that generated a component failure to finding them before the failure occurs. This work is focused on predictive maintenance, which aims to constantly monitor the target component to detect a fault at the beginning, thus facilitating the prevention of target component failures. It presents the results of different machine learning methods implemented as classification predictors for fault detection tasks, including Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Networks (ANN) variants, and Gaussian Processes (GP). The data used for training were generated by a simulation testbed for fault diagnosis in turbocharged petrol engine systems, whereby its operation was modeled using industrial-standard driving cycles, such as the Worldwide Harmonized Light Vehicle Test Procedure (WLTP), New European Driving Cycle (NEDC), Extra-Urban Driving Cycle (EUDC), and the United States Environmental Protection Agency Federal Test Procedure (FTP-75).
Tessaro et al. (Sat,) studied this question.