# US 2022/0044495 A1 · Self-diagnosis for in-vehicle networks > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0044495 A1 - Document ID: US20220044495A1 - Status: Published application - Kind: A1 - Application number: 17/396,710 - Filed: 2021-08-08 - Published: 2022-02-10 - Earliest claimed: 2020-08-07 - Assignees: Marvell Asia Pte Ltd - Applicants: Marvell Asia Pte Ltd - Inventors: Ramin Farjadrad; Dance Wu; Xing Wu; Shaoan Dai; Wensheng Sun - Web page: https://patentgrep.com/patent/US20220044495A1 ## CPC - G06F 11/004: {Error avoidance (G06F11/07 and subgroups take precedence)} - G07C 5/006: {Indicating maintenance} - G05B 19/042: using digital processors (G05B19/05 takes precedence) - G06F 11/0709: {in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems} - G06F 11/0739: {in a data processing system embedded in automotive or aircraft systems} - G06F 11/0793: {Remedial or corrective actions (recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40)} - G06F 11/3006: {where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems (multiprogramming arrangements G06F9/46; allocation of resources G06F9/50)} - G06F 11/3013: {where the computing system is an embedded system, i.e. a combination of hardware and software dedicated to perform a certain function in mobile devices, printers, automotive or aircraft systems (testing or monitoring of control systems or parts thereof G05B23/02)} - G06F 11/3055: {Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available (error or fault processing without redundancy G06F11/0703; error detection or correction by redundancy in data representation G06F11/08; error detection or correction of the data by redundancy in operations G06F11/14; error detection or correction by redundancy in hardware G06F11/16)} - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/0499: Feedforward networks - G06N 3/09: Supervised learning - G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)} - G05B 23/0254: {based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks} - G05B 2219/2637: Vehicle, car, auto, wheelchair - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Methods and systems provide for fault diagnosis in a vehicular communication network. The methods and systems utilize a trained neural network model which is downloaded to a local computer associated with the vehicular communication network of a given vehicle and which applies inputs from the given vehicle to output maintenance recommendations for the given vehicle. ## Claims ### Claim 1 A method for fault diagnosis in a vehicular communication network, the method 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 failure information 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; deploying 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. ### Claim 2 (depends on 1) The method of claim 1, wherein the outputting the maintenance recommendations includes responding to output from the trained neural network model corresponding to the diagnostic information inputted into the trained neural network model. ### Claim 3 (depends on 1) The method of claim 1, wherein the inputting the diagnostic information comprises collecting information from sensors associated with components of the vehicular communication network in the first vehicle. ### Claim 4 (depends on 1) The method of claim 1, wherein the collected diagnostic information and the output from the first vehicle are periodically provided to the trained neural network model to further train the trained neural network model. ### Claim 5 (depends on 1) The method of claim 1, wherein the failure information from one or more of the first vehicle or the plurality of second vehicles is periodically provided to the trained neural network model to further train the trained neural network model. ### Claim 6 (depends on 1) The method of claim 1, wherein the diagnostic information is selected from a group of inputs including a channel quality index, a link quality metric, a temperature, a cable length, an accumulated time, a communication speed, a measure of interference, cable type, and chip voltage. ### Claim 7 (depends on 6) The method of claim 6, wherein inputting the diagnostic information comprises measuring and inputting one or more parameters associated with the channel quality index from one or more cables in the vehicular communication network of the first vehicle, including an overall insertion loss (IL), an overall return loss (RL), a near end RL and a far end RL, into the trained neural network model. ### Claim 8 (depends on 6) The method of claim 6, wherein the inputting the diagnostic information comprises measuring and inputting one or more parameters associated with the link quality metric from a communication link in the network, including a signal to noise ratio (SNR) of the link and an error rate of the link, into the trained neural network model. ### Claim 9 (depends on 6) The method of claim 6, wherein the inputting the diagnostic information for the temperature includes measuring and inputting one or more parameters including core temperature and junction temperature of an integrated circuit (IC) chip, at one or more locations on the IC chip, and, ambient temperature, into the trained neural network model. ### Claim 10 (depends on 6) The method of claim 6, wherein inputting the diagnostic information for the accumulated time includes measuring and inputting accumulated power up time and system time into the trained neural network model. ### Claim 11 (depends on 2) The method of claim 2, wherein the trained neural network model outputs one or more of: a link health indication, a system reliability indication, one or more warnings, a cable fault indication, a cable fault location, and an IC chip fault indication, which are members of the set of metrics generated by the trained neural network model. ### Claim 12 (depends on 11) The method of claim 11, wherein the one or more warnings include channel warnings, communication quality warnings, and chip warnings. ### Claim 13 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. ### Claim 14 (depends on 13) The system of claim 13, wherein the processor is additionally configured to: generate the maintenance recommendations in response to output from the trained neural network model corresponding to the diagnostic information input into the trained neural network model. ### Claim 15 (depends on 13) The system of claim 13, wherein the components of the vehicular communication network in the first vehicle comprise one or more sensors from which the diagnostic information is obtained. ### Claim 16 (depends on 13) The system of claim 13, wherein the components of the vehicular communication network of the first vehicle comprise integrated circuit (IC) chips, from which the diagnostic information is obtained. ### Claim 17 (depends on 13) The system according to claim 13, wherein the components of the vehicular communication network include one or more cables, from which the diagnostic information is obtained. ### Claim 18 (depends on 17) The system according to claim 17, wherein the one or more cables include: shielded cables, unshielded cables, single stranded cables and multi stranded cables. ### Claim 19 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.