Fault detection on automotive engine components is an important feature that motivates research from different engineering areas due to the interest of automakers on 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 find 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. Due to production costs, it is not possible to have sensors installed in all engine components, which makes it difficult to apply predictive maintenance for all of them. One way to work around that problem is to use predictors based on machine learning paradigms, which take signals from indirect sensors from other components and predict a fault. To accomplish that, it is necessary to acquire data that contains both normal and faulty behaviors from the target component in order to train a machine learning method to recognize the defective behavior prior to embedding it into the software of the engine electronic control unit. Data acquisition can be an expensive process as well, as it may require several rounds of destructive testing for different driving cycles, which must be performed in real-time on instrumented vehicles in a dynamometer. Since machine learning methods are capable of handling a certain amount of noise, the data to train them can be generated by simulating the model of the respective engine in which the target component is installed. That process does not require real-time executions and vehicle instrumentation in a dynamometer lab, which decreases the cost of the data acquisition as a whole. This work 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 Network (ANN), and Gaussian Process (GP). The data used for training was generated by a simulation testbed of an engine system, 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 U.S. Environmental Protection Agency Federal Test Procedure (FTP-75).
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