Self-diagnosis for in-vehicle networks

A system for fault diagnosis in a vehicular communication network comprising:

• a memory of a local computer associated with the vehicular communication network of a first vehicle, the memory configured for storing a neural network trained model:
• the neural network trained model having been trained based on:
• 1) collected diagnostic information from components of the vehicular communication network in a plurality of second vehicles over a period of time;
• and 2) collected failure information of the components of the vehicular communication network in the plurality of second vehicles over the period of time;
• and
• the neural network trained model having generated, based on the collected diagnostic information, a set of metrics indicating a likelihood of failure of the components to perform in a vehicular communication network in a specified manner;
• and
• a processor configured to:
• deploy the trained neural network model stored in the memory;
• obtain and input diagnostic information from the components of the vehicular communication network of the first vehicle into the deployed trained neural network model;
• and
• generate maintenance recommendations for the input diagnostic information from the components of the vehicular communication network of the first vehicle, based on the set of metrics generated by the trained neural network model.

A computer usable non-transitory storage medium having a computer program embodied thereon for causing a suitably programmed system to diagnose faults in a vehicular communication network, by performing the following steps when such program is executed on the system, the steps comprising:

• downloading a neural network trained model to a local computer associated with the vehicular communication network of a first vehicle, the trained neural network model having been trained based on:
• 1) collected diagnostic information from components of the vehicular communication network in a plurality of second vehicles over a period of time;
• and 2) collected information of failures of the components of the vehicular communication network in the plurality of second vehicles over the period of time;
• and the trained neural network model having generated, based on the collected diagnostic information, a set of metrics indicating a likelihood of failure of the components to perform in a vehicular communication network in a specified manner;
• causing deploying of the trained neural network model by the local computer;
• inputting diagnostic information from the components of the vehicular communication network of the first vehicle into the trained neural network model;
• and
• outputting maintenance recommendations based on the set of metrics generated by the trained neural network model.