Autonomous Vehicle Fault Diagnosis via ML Residual Analysis
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Solution Overview
Problem
Existing fault detection and diagnosis methods for autonomous vehicles, such as FMECA and FTA, require prior knowledge of faults and are inefficient and error-prone, making it difficult to detect and diagnose unknown faults, especially as compensation measures can mask anomalous behavior.
Innovation Solution
A machine learning-based system that compares measured vehicle behavior against expected nominal behavior to identify anomalous residuals, determining fault conditions and their causes without prior knowledge, using a residuals generator and causal engine trained on vehicle data from multiple missions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fault detection methods (FMECA, FTA) are used, then fault detection can be performed with structured analysis, but prior knowledge of faults is required and unknown faults cannot be detected
Solution Approach 1:
The patent replaces traditional mechanical/structured analysis methods (FMECA, FTA) with a machine learning-based residual analysis system. The system uses neural networks to learn normal vehicle behavior patterns from data and automatically detects deviations, eliminating the need for pre-defined fault models while maintaining systematic analysis capability.
Solution Approach 2:
The system transforms fault detection from a qualitative, knowledge-based approach to a quantitative, data-driven approach. By changing the fundamental parameter from 'prior fault knowledge' to 'measured behavior deviations', the system enables detection of unknown faults through statistical analysis of operational parameters.
2Reliability
If compensation measures are implemented to correct anomalous behavior, then vehicle operation can be maintained, but the anomalous behavior is masked and hidden
Solution Approach 1:
The patent introduces residual analysis as an intermediary layer between vehicle behavior and fault detection. By calculating residuals as the difference between measured and expected behavior, the system creates a clear signal that reveals anomalous behavior even when compensation measures are actively correcting the underlying fault.
Solution Approach 2:
The system continuously monitors vehicle behavior, compares it against expected patterns, and provides feedback about deviations. This feedback mechanism enables detection of anomalous behavior in real-time, allowing the system to identify faults even as compensation measures are being executed to maintain operational reliability.
3Productivity
If FMECA and FTA methods are used, then fault analysis can be performed, but the methods are inefficient and error prone
Solution Approach 1:
The patent replaces manual, expert-based fault analysis methods with automated machine learning systems. The neural networks process vast amounts of vehicle data automatically, eliminating human error and significantly improving both the efficiency and accuracy of fault detection compared to traditional FMECA and FTA methodologies.
Data Source
AI summary
Described are systems and methods relating to the causal detection and diagnosing of faults and anomalous operation of autonomous vehicles, such as unmanned aerial vehicles (UAVs), using machine learning. Embodiments of the present disclosure can provide systems and methods for detecting and diagnosing faults based on comparisons between the measured operation and/or behavior of a vehicle to the vehicle's expected nominal operation and/or behavior. Accordingly, the systems and methods according to embodiments of the present disclosure do not require prior knowledge of faults or modeling of the vehicle, the vehicle's operation, and/or environmental uncertainties. Further, embodiments of the present disclosure can facilitate sequencing of a vehicle's faults and/or anomalous operation and/or behavior, identify dependencies between a vehicle's faults and/or anomalous operation and/or behavior, and can detect and diagnose faults and/or anomalous operation and/or behavior in a contextual manner.