clustering normal historical data of normal cars into a plurality of groups based on a car subsystem to which they belong;

extracting (i) features based on membership to the plurality of groups and (ii) feature correlations based on correlation graphs formed from the plurality of groups to retrieve relevant signals and filter signal noise;

training, by a hardware processor, an Auto-Encoder and Auto Decoder (AE&AD) model based on the features and the feature correlations to reconstruct the normal historical data by minimizing reconstruction errors;

reconstructing, using the trained AE&AD model, historical data of specific car fault types with reconstruction errors, normalizing the reconstruction errors, and selecting features of the car faults with a top k large errors as fault signatures; and

reconstructing streaming data of monitored cars using the trained AE&AD model, installed in an edge module of the monitored car that is connected to an online car monitoring system, to determine streaming reconstruction errors, comparing the streaming reconstruction errors with the fault signatures to predict impending known faults and provide alerts for the impending known faults.