Real-Time Exception Prognosis for Safety-Critical Embedded Control
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Solution Overview
Problem
Avionics control applications face challenges in predicting and preventing exceptions due to complex computations and data dependencies, leading to erratic behaviors and prolonged system unavailability during recovery attempts.
Innovation Solution
A smart exception handler system using machine learning techniques to analyze parameters at critical nodes, compute fault probabilities, and take proactive actions to avoid faults in real-time, preventing system downtime by learning critical execution paths and thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive recovery mechanisms are used to handle exceptions, then the system can attempt to recover from faults, but the system becomes unavailable for a long period of time and may not successfully return to operational state
Solution Approach 1:
The system performs preliminary actions by continuously monitoring execution parameters and computing fault probabilities before actual faults occur. The proactive exception handling mechanism identifies critical execution paths and predicts potential exceptions in advance, allowing the system to take preventive measures before the fault manifests, thereby avoiding prolonged system unavailability while maintaining recovery capability
Solution Approach 2:
The system implements feedback by continuously analyzing execution parameters at critical nodes and using the trained machine learning model to compute fault probabilities. This real-time feedback loop allows the system to adjust its operation dynamically, switching to safe states or alternative execution paths when fault probability exceeds thresholds, thus preventing faults before they occur and minimizing system downtime
2Adaptability or versatility
If complex computation and data dependencies are implemented in avionics systems, then the system features and functionality are enhanced, but it becomes difficult to foresee every execution path that may lead to erratic behaviors
Solution Approach 1:
The system introduces an intermediary machine learning model that acts as a mediator between the complex execution paths and the exception handling mechanism. This trained model analyzes execution parameters at critical nodes and computes fault probabilities, effectively bridging the gap between complex system behavior and interpretable risk assessment, making it feasible to manage and predict exceptions in highly functional systems
Solution Approach 2:
The system changes parameters by monitoring and analyzing execution parameters at critical nodes rather than attempting to analyze all possible execution paths. By focusing on key parameters at critical points and using a trained ML model to interpret their significance, the system can effectively detect potential exceptions in complex avionics applications without requiring complete visibility into all execution paths
3Reliability
If recovery mechanisms inhibit usage of the application for a long period of time, then the system attempts to return to a reasonable operational state, but productivity and system availability are reduced
Solution Approach 1:
The system takes preliminary action by predicting faults before they occur and proactively transitioning to safe states or alternative execution paths. This prevents the need for lengthy recovery processes by avoiding the fault state entirely, thereby maintaining system availability and productivity while still ensuring the system returns to a reasonable operational state when necessary
Data Source
AI summary
A smart exception handler system for safety-critical real-time systems is provided. The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the trained exception handling model has been trained using machine learning techniques to learn the critical path of execution and/or critical range of parameters at critical nodes, wherein the critical range of parameters comprises a learned threshold at a node; compute, using the trained exception handling model, a probability of fault at the critical nodes; compare the probability of fault at a critical node against a learned threshold at the node; and take proactive action in real-time to avoid the occurrence of a fault when the probability of fault at the node is higher than the learned threshold at the node.