Automotive physical layer (PHY) cable fault diagnosis

1. A method for fault diagnosis in one or more cables of a vehicular communication network, the method comprising:

obtaining a return echo of a data-carrying communication signal transmitted over a cable in a vehicle;

applying, to the return echo of the data-carrying communication signal, a computer trained model that distinguishes among a plurality of predetermined fault types in cables by analyzing a signal shape of the return echo against a plurality of signal shapes of echoes associated with the plurality of predetermined fault types, so as to identify a predetermined fault type in the cable; and

initiating an action in response to the identified predetermined fault type.

2. The method for fault diagnosis of claim 1 , wherein applying the computer trained model comprises applying an Artificial Intelligence (AI) model that is pre-trained to distinguish the predetermined fault type in the cable from amongst the plurality of predetermined fault types.

3. The method for fault diagnosis of claim 2 , wherein the AI model comprises multiple feature-extraction layers configured to determine that the cable has a fault, and to identify the predetermined fault type.

4. The method for fault diagnosis of claim 2 , wherein the AI model includes one or more of: a trained neural network and/or a machine learning model.

5. The method for fault diagnosis of claim 1 , wherein obtaining the return echo comprises receiving the return echo by an Ethernet physical layer (PHY) device that applies echo cancellation in response to the receiving the return echo.

6. The method for fault diagnosis of claim 1 , wherein applying the computer trained model to the return echo includes determining a probability of the cable exhibiting the identified predetermined cable fault type selected from amongst the plurality of predetermined cable fault types.

7. The method for fault diagnosis of claim 1 , wherein the plurality of predetermined fault types in cables includes two or more of:

Short circuit (short);

Open circuit (Open);

Open and ground;

Normal and ground;

one wire shorted to ground;

one wire shorted to power;

one wire open;

two wires shorted to ground;

two wires shorted to power; and

two wires open.

8. The method for fault diagnosis of claim 1 , wherein obtaining the return echo includes obtaining a plurality of return echoes from a corresponding plurality of cables; and applying the computer trained model to analyze each of the return echoes to identify a respective predetermined fault type in each of the cables, from amongst the plurality of predetermined fault types.

9. The method for fault diagnosis of claim 1 , wherein the initiating an action comprises one or more of: logging the fault, issuing an alert, notifying the driver or other user, and switching to a backup cable.

10. A system for diagnosing faults in one or more cables of a vehicular communication network, the fault diagnosis system comprising:

one or more processors; and

a program memory storing executable instructions that, when executed by the one or more processors, cause the system to:

obtain a return echo of a data-carrying communication signal transmitted over a cable in a vehicle;

apply, to the return echo of the data-carrying communication, a computer trained model that distinguishes among a plurality of predetermined fault types in cables by analyzing a signal shape of the return echo against a plurality of signal shapes of echoes associated with the plurality of predetermined fault types, so as to identify a predetermined fault type in the cable; and

initiate an action in response to the identified fault type.

11. The system for diagnosing faults of claim 10 , wherein the computer trained model comprises an Artificial Intelligence (AI) model to determine a probability of the identified predetermined type of fault from amongst the plurality of predetermined fault types.

12. The system for diagnosing faults of claim 11 , wherein the AI model includes one or more of: a trained neural network and/or a machine learning model.

13. A system for diagnosing faults in a cable in an automotive or industrial network, the system comprising:

an Ethernet physical layer (PHY) device, comprising:

a transmitter for transmitting an outbound data-carrying Ethernet communication signal to the cable;

a receiver for receiving an inbound Ethernet communication signal from the cable, the received inbound Ethernet communication signal comprising a return echo caused by the transmitted outbound data-carrying Ethernet communication signal;

an echo canceller for estimating the return echo and canceling the return echo in the received inbound Ethernet communication signal; and

storage media in communication with the echo canceller for storing the estimated return echo, the stored estimated return echo being available for readout externally to the Ethernet PHY device; and

a processor or computer external to the Ethernet PHY device, the processor or computer configured to:

read the estimated return echo from the storage media of the Ethernet PHY device;

apply, to the return echo of the outbound data-carrying Ethernet communication signal, a computer trained model that distinguishes among a plurality of predetermined fault types in cables by analyzing a signal shape of the return echo against a plurality of signal shapes of echoes associated with the plurality of predetermined fault types, so as to identify a predetermined fault type in the cable; and

initiate an action in response to the identified fault type.

14. The system for diagnosing faults of claim 13 , wherein the return echo is of a predetermined size.