Vehicle Motion Control Health Monitoring for Failure Mitigation
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Solution Overview
Problem
Current motor vehicle motion control systems face challenges in managing performance under various conditions, leading to potential degradation or failure, which can propagate and result in system failure. There is a need for systems and methods that reduce computational resource burden, enhance reliability and robustness, mitigate component deterioration and failures, while maintaining or reducing cost and complexity.
Innovation Solution
A motor vehicle motion control health monitoring system that includes sensors and actuators to measure and alter real-time telemetry data. A control module with a processor, memory, and I/O ports communicates with sensors and actuators, executing offline and online program code portions. The offline code collects and analyzes data, performs failure analysis, and allocates tasks, while the online code detects and predicts failures, mitigates deviations, and communicates with a remote cloud computing system for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex motion control systems with large numbers of interacting functions are implemented, then system performance and control capability are improved, but system reliability deteriorates due to potential degradation and failure propagation
Solution Approach 1:
The system segments motion control functions into independent controllable modules, each with its own health monitoring. This allows individual function isolation so that failures in one module do not propagate to others, maintaining system reliability while preserving overall motion control capability through selective function activation.
Solution Approach 2:
The system implements continuous health monitoring with feedback mechanisms that detect degradation in sensors, actuators, and control functions. The feedback loop enables real-time assessment of component health and triggers mitigation strategies before failures propagate, thereby maintaining reliability in complex multi-function systems.
2Reliability
If comprehensive health monitoring and failure analysis are implemented, then system reliability is improved, but computational resource burden increases
Solution Approach 1:
The system applies partial monitoring action by focusing health monitoring resources on critical sensors, actuators, and control functions rather than uniformly monitoring all system components. This selective approach maintains system reliability by monitoring key failure points while reducing overall computational resource burden through targeted rather than comprehensive analysis.
3Reliability
If real-time failure detection and mitigation are implemented, then system robustness is improved, but system complexity increases
Solution Approach 1:
The system implements self-service through automated health monitoring and failure mitigation that operates without external intervention. The control module autonomously detects failures, analyzes their impact, and executes mitigation strategies, thereby improving robustness while managing complexity through automation rather than manual intervention procedures.
4Measurement precision
If extensive data collection and cloud computing integration are implemented, then failure analysis accuracy is improved, but data transmission and processing complexity increases
Solution Approach 1:
The system uses cloud computing platforms as intermediaries to handle complex data processing and advanced failure analysis. The control module collects telemetry data and transmits it to the cloud, where sophisticated algorithms perform detailed analysis. This intermediary approach improves failure analysis accuracy by leveraging powerful remote computing resources while keeping on-vehicle system complexity manageable.
Data Source
AI summary
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.