Nowadays, cars are instrumented with thousands of sensors continuously collecting data about its components. Thanks to the concept of connected cars, this data can be now transferred to the cloud for advanced analytics functionalities, such as prognostic or predictive maintenance. In this paper, we dissect a data-driven prognostic pipeline and apply it in the automotive scenario. Our pipeline is composed of three main steps: (i) selection of most important signals and features describing the scenario for the target problem, (ii) creation of machine learning models based on different classification algorithms, and (iii) selection of the model that works better for a deployment scenario. For the development of the pipeline, we exploit an extensive experimental campaign where an actual engine runs in a controlled test bench under different working conditions. We aim to predict failures of the High-Pressure Fuel System, a key part of the diesel engine responsible for delivering high-pressure fuel to the cylinders for combustion. Our results show the advantage of data-driven solutions to automatically discover the most important signals to predict failures of the High-Pressure Fuel System. We also highlight how an accurate model selection step is fundamental to identify a robust model suitable for deployment.
Dissecting a data-driven prognostic pipeline: A powertrain use case
Cerquitelli T.;Tricarico D.
2021-01-01
Abstract
Nowadays, cars are instrumented with thousands of sensors continuously collecting data about its components. Thanks to the concept of connected cars, this data can be now transferred to the cloud for advanced analytics functionalities, such as prognostic or predictive maintenance. In this paper, we dissect a data-driven prognostic pipeline and apply it in the automotive scenario. Our pipeline is composed of three main steps: (i) selection of most important signals and features describing the scenario for the target problem, (ii) creation of machine learning models based on different classification algorithms, and (iii) selection of the model that works better for a deployment scenario. For the development of the pipeline, we exploit an extensive experimental campaign where an actual engine runs in a controlled test bench under different working conditions. We aim to predict failures of the High-Pressure Fuel System, a key part of the diesel engine responsible for delivering high-pressure fuel to the cylinders for combustion. Our results show the advantage of data-driven solutions to automatically discover the most important signals to predict failures of the High-Pressure Fuel System. We also highlight how an accurate model selection step is fundamental to identify a robust model suitable for deployment.
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