Abstract: The present disclosure concerns a method to train, on a computing device, a machine-learning model adapted to determine a dysfunction of a monitored vehicle electronic control unit (ECU) or vehicle embedded computer. In aspects, the computing device stores, in a memory, historical data from a plurality of ECUs having a dysfunction. The historical data may include usage values over a period of time of at least one ECU resource by applications running on the ECUs. Further, the computing device may process the historical data to obtain two-dimensional training files. In implementations, each usage value may be linked with a specific application in a first dimension and a specific time in a second dimension. Still further, the computing device may train a machine-learning model with the training files.

Abstract: The present disclosure concerns a method to train, on a computing device, a machine-learning model adapted to determine a dysfunction of a monitored vehicle electronic control unit (ECU) or vehicle embedded computer. In aspects, the computing device stores, in a memory, historical data from a plurality of ECUs having a dysfunction. The historical data may include usage values over a period of time of at least one ECU resource by applications running on the ECUs. Further, the computing device may process the historical data to obtain two-dimensional training files. In implementations, each usage value may be linked with a specific application in a first dimension and a specific time in a second dimension. Still further, the computing device may train a machine-learning model with the training files.