Robotic Fault Diagnostics Using Causal Traces in Real Time
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicle systems face challenges in real-time fault detection and root cause analysis due to the need to process thousands of operational parameters with low latency, requiring significant computational power and time to identify contributing parameters and subsystems.
Innovation Solution
A diagnostic service within the autonomous vehicle system uses a processor and programming instructions to create derived signals from primary signals, aggregate them, and generate a causal trace to determine fault causes, enabling real-time monitoring and safety-related actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple parameters are considered together in formulas or equations to detect faults, then fault detection accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the computational process into distinct functional modules: a signal reception module that collects primary signals from multiple parameters, a signal processing module that applies formulas and equations to detect faults, and a result analysis module that interprets detection outcomes. This segmentation allows each module to specialize in specific computational tasks, improving overall efficiency while maintaining the holistic analysis of multiple parameters for accurate fault detection.
2Reliability
If thousands of operational parameters are collected and analyzed in real-time, then fault detection capability is improved, but processing speed and latency increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring formulas and equations in the signal processing module before real-time operation. These pre-programmed computational relationships allow the system to rapidly process thousands of operational parameters without requiring complex runtime decision-making about which formulas to apply. The system also pre-establishes threshold values and detection criteria, enabling immediate fault detection when parameters exceed these predetermined limits, thus maintaining high processing speed while analyzing comprehensive data.
3Reliability
If multiple instances of functions are created for each valence of keyed signals, then diagnostic coverage is improved, but memory usage and computational overhead increase
Solution Approach 1:
The patent applies universality by designing a single configurable signal processing module that can handle multiple valences of keyed signals through parameterization rather than creating separate dedicated modules for each valence. The module uses configuration files to load different formula sets and processing rules dynamically, allowing the same computational infrastructure to serve multiple diagnostic purposes. This approach maintains comprehensive diagnostic coverage across all signal valences while significantly reducing memory consumption compared to having separate function instances for each valence.
Data Source
AI summary
To determine a cause of a fault in a robotic system, a diagnostic service of the robotic system will receive primary signals from various processes running on the robotic system. The service will access a graph representation of functions as stored in memory, and the service will perform the functions on one or more of the primary signals to yield one or more derived signals. A sink of the robotic system will subscribing to a causal trace that includes a value for a specified one of the derived signals and an identification of each signal from which the specified derived signal was derived. During runtime, the sink will receive updates to the causal trace as the value for the specified derived signal changes. The diagnostic service will use the causal trace to identify a process that caused the fault.