Autonomous Situation Awareness for Sensor Overload and Anomaly Response
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Solution Overview
Problem
Existing situation awareness systems struggle to handle the flood of information from exponentially increasing sensors in complex engineering assets, leading to cognitive overload for Human-in-the-Loop (HITL) supervisory control.
Innovation Solution
An enhanced situation awareness system integrating ambient intelligence, reflexive context awareness, and sequential probability ratio test (SPRT)-based predicate classification, which autonomously filters relevant information and automates control actions using ML anomaly detection to reduce cognitive overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of sensors for monitoring engineering assets is increased, then the quantity of information available for situation awareness is improved, but the cognitive load on Human-in-the-Loop supervisory control deteriorates
Solution Approach 1:
The system segments the overwhelming sensor data stream into discrete, analyzable events by detecting anomalies against learned nominal behavior patterns. This transforms the continuous flood of sensor information into discrete anomaly events that are cognitively manageable for supervisory control, resolving the contradiction between information quantity and cognitive load.
Solution Approach 2:
The autonomous situation awareness system acts as an intermediary between the sensor array and human supervisory control. It processes, analyzes, and filters sensor data autonomously, presenting only relevant anomaly information to human operators, thereby reducing cognitive load while maintaining comprehensive monitoring capability.
2Extent of automation
If autonomous processing of sensor data is implemented, then the extent of automation is improved, but the device complexity deteriorates
Solution Approach 1:
The system implements self-service through autonomous learning of nominal asset behavior patterns and automatic detection of anomalies without human intervention. The situation awareness system independently processes sensor data, identifies deviations from learned patterns, and generates anomaly alerts, reducing the need for complex human-in-the-loop processing while managing system complexity through adaptive learning.
Data Source
AI summary
Systems, methods, and other embodiments associated with autonomous situation awareness based on ambient intelligence and permuted binary-state predicate classification are described. In one embodiment, a method includes accessing a stream of multivariate observations of system status and command variables. The method supplements the multivariate observations with ML estimates of ambient variables based on the system variables and command variables. The method determines anomalies of the system variables based on residuals between observed values and ML estimates of the system variables based on the supplemented observations. The method evaluates the anomalies with predicates to select one of the command variables to be adjusted. The method generates a suggestion for the selected command variable based on ML predictions of future values for the system variables. And, the method generates an electronic alert to adjust the controls of the asset to match the suggestion for the selected command variable.