Architecture and methodology for vehicle motion control health monitoring and mitigation

Patent metadata

Field	Value
Publication number	US-2023242131-A1
Application number	US-202217592024-A
Country	US
Kind code	A1
Filing date	Feb 3, 2022
Priority date	Feb 3, 2022
Publication date	Aug 3, 2023
Grant date	—

Abstract

A motor vehicle motion control health monitoring system includes sensors and actuators disposed on the motor vehicle. The sensors measure real-time static and dynamic telemetry data about the motor vehicle, and the actuators alter static and dynamic behavior of the motor vehicle. A control module has a processor, a memory, and input/output (I/O) ports. The processor executes program code portions stored in the memory, the program code portions include: an offline portion that collects telemetry data from the motor vehicle, performs failure analysis on the telemetry data and allocates tasks based on the failure analysis; and an online portion that analyzes the telemetry data for failures within specific sensors, actuators, or functions that utilize systems of sensors and/or actuators. The online portion mitigates deviations in the telemetry data by sending a correction to the one or more sensors, actuators, and/or functions of a motor vehicle motion control system.

First claim

What is claimed is:

1 . A motor vehicle motion control health monitoring system, comprising:
one or more sensors disposed on the motor vehicle, the one or more sensors measuring real-time static and dynamic telemetry data about the motor vehicle; one or more actuators disposed on the motor vehicle, the one or more actuators altering static and dynamic behavior of the motor vehicle; a control module having a processor, a memory, and input/output (I/O) ports in communication with the one or more sensors and the one or more actuators, the control module executing program code portions stored in the memory, the program code portions comprising;
an offline program code portion that collects telemetry data from the motor vehicle, performs failure analysis on the telemetry data and allocates tasks based on the failure analysis; and
an online program code portion that analyzes the telemetry data for failures within specific sensors, actuators, or functions that utilize systems of sensors and/or actuators and mitigates deviations in the telemetry data by sending a correction to the one or more sensors, actuators, and/or functions of a motor vehicle motion control system.

2 . The system of claim 1 wherein the offline program code portion further comprises:
a first program code portion that collects, via the I/O ports, the real-time static and dynamic data from the one or more sensors, from the one or more actuators, and from one or more functions of a motor vehicle motion control system.

3 . The system of claim 1 wherein the offline program code portion further comprises:
a second program code portion that performs failure mode determination for the one or more sensors, the one or more actuators, and the one or more functions, wherein the failure mode determination includes determining whether one or more sensors, one or more actuators and/or one or more functions is experiencing a degradation of performance, a complete failure, and/or a failure mode in which the telemetry data includes noise beyond a predefined threshold noise value.

4 . The system of claim 1 wherein the offline program code portion further comprises:
a third program code portion that performs data preparation, generation, and collection for the one or more sensors, the one or more actuators, and the one or more functions, wherein the data preparation further comprises: exclusion, filtering, and/or buffering processes.

5 . The system of claim 1 wherein the offline program code portion further comprises:
a fourth program code portion that utilizes a machine-learning architecture design to generate a task allocation scheme for the telemetry data from the one or more sensors, the one or more actuators, and the one or more functions, wherein the machine-learning architecture further comprises: machine-learning and/or artificial intelligence based clustering methods to identify failure modes and classifications, and wherein the fourth program code portion further applies a temporary or short-term mitigation to the one or more sensors, the one or more actuators, and/or the one or more functions and sending raw telemetry data to a cloud computing system for further analysis.

6 . The system of claim 1 wherein the online program code portion further comprises:
a fifth program code portion that performs data exclusion on the telemetry data from the one or more sensors, the one or more actuators, and the one or more functions.

7 . The system of claim 1 wherein the online program code portion further comprises:
a sixth program code portion that detects and predicts potential failures within the one or more sensors, the one or more actuators, and the one or more functions.

8 . The system of claim 1 wherein the online program code portion further comprises:
a seventh program code portion that communicates, via the I/O ports, the telemetry data from the one or more sensors, the one or more actuators, and the one or more functions between the motor vehicle and a I/O ports of a remote control module within a cloud computing system physically separate from the motor vehicle.

9 . The system of claim 1 wherein the online program code portion further comprises:
an eighth program code portion that mitigates deviations in the telemetry data from the one or more sensors, the one or more actuators, and the one or more functions by applying a modified estimator algorithm and/or altered calibration correction to the one or more sensors, the one or more actuators, and the one or more functions.

10 . A method for motor vehicle motion control system health monitoring, the method comprising:
measuring real-time static and dynamic telemetry data about the motor vehicle with one or more sensors disposed on the motor vehicle; altering static and dynamic behavior of the motor vehicle with one or more actuators disposed on the motor vehicle; utilizing a control module having a processor, a memory, and input/output (I/O) ports in communication with the one or more sensors and the one or more actuators, the control module executing program code portions stored in the memory, wherein the program code portions include an offline program code portion and an online program code portion; collecting, by the offline code portion, telemetry data from the motor vehicle; performing, by the offline code portion, failure analysis on the telemetry data; and allocating, by the offline code portion, tasks based on the failure analysis; analyzing, by the online code portion, the telemetry data for failures within specific sensors, actuators, or functions that utilize systems of sensors and/or actuators; and mitigating, by the online code portion, deviations in the telemetry data by sending a correction to the one or more sensors, actuators, and/or functions of a motor vehicle motion control system.

11 . The method of claim 10 further comprising:
collecting, by the offline code portion via the I/O ports, the real-time static and dynamic data from the one or more sensors, from the one or more actuators, and from one or more functions of a motor vehicle motion control system.

12 . The method of claim 10 further comprising:
performing, by the offline code portion, failure mode determination for the one or more sensors, the one or more actuators, and the one or more functions, wherein the failure mode determination includes determining whether one or more sensors, one or more actuators and/or one or more functions is experiencing a degradation of performance, a complete failure, and/or a failure mode in which the telemetry data includes noise beyond a predefined threshold noise value.

13 . The method of claim 10 further comprising:
performing, by the offline code portion, data preparation, generation, and collection for the one or more sensors, the one or more actuators, and the one or more functions, wherein the data preparation further comprises: exclusion, filtering, and/or buffering processes.

14 . The method of claim 10 further comprising:
utilizing, by the offline code portion, a machine-learning architecture design to generate a task allocation scheme for the telemetry data from the one or more sensors, the one or more actuators, and the one or more functions, wherein the machine-learning architecture further comprises: machine-learning and/or artificial intelligence based clustering methods to identify failure modes and classifications.

15 . The method of claim 10 further comprising:
applying a temporary or short-term mitigation to the one or more sensors, the one or more actuators, and/or the one or more functions and sending raw t

Assignees

• Gm Global Tech Operations Llc

Inventors

• Kasaiezadeh Mahabadi SeyedAlireza
• Adiththan Arun
• Zhao Qingrong
• Lin Wen-Chiao
• Duan Shiming
• Sethu Ramesh
• Litkouhi Bakhtiar B
• Chen Shih-Ken

Classifications

• G06Q10/20 Primary

  Administration of product repair or maintenance · CPC title

• G06Q10/06311

  Scheduling, planning or task assignment for a person or group · CPC title

• B60W50/04 Primary

  Monitoring the functioning of the control system · CPC title

• B60W50/045

  Monitoring control system parameters · CPC title

• B60W50/0205 Primary

  Diagnosing or detecting failures; Failure detection models · CPC title