AI Supervisor Architecture for Sensor Fault Mitigation in Control Systems
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Solution Overview
Problem
Existing technologies for supervising controlled systems are limited in efficiently detecting and mitigating anomalies like faults or attacks, often requiring prior knowledge of system dynamics and being sensitive to model inaccuracies, which can lead to delayed responses and system destabilization.
Innovation Solution
A supervisor system utilizing a Reinforcement Learning (RL) agent that monitors sensor signals, detects and localizes faults, and performs mitigating actions, combining model-based and data-driven techniques to isolate faulty sensors and maintain target behavior in controlled systems, such as chemical processes or industrial systems, without requiring prior knowledge of system dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional supervision methods are used, then prior knowledge of system dynamics is required, but this leads to delayed responses and system destabilization
Solution Approach 1:
The patent replaces traditional model-based supervision mechanisms with a Reinforcement Learning agent that learns system dynamics through interaction. The RL agent substitutes the need for pre-defined system models and prior knowledge, enabling adaptive fault detection without requiring mechanical or mathematical models of the controlled system's dynamics.
Solution Approach 2:
The RL agent performs preliminary learning of system dynamics and fault patterns during training phases before actual deployment. This preliminary action allows the agent to develop intuitive understanding of system behavior and anomaly detection capabilities in advance, enabling rapid response when faults occur during operational phases without needing real-time model analysis.
2Measurement precision
If model-based supervision is used, then system dynamics knowledge is required, but this increases sensitivity to model inaccuracies
Solution Approach 1:
The patent substitutes traditional model-based detection mechanisms with a data-driven RL agent that learns system behavior patterns directly from interactions. This replacement eliminates dependence on accurate system dynamics models, allowing the supervisor to achieve reliable fault detection precision without being sensitive to model inaccuracies or requiring prior knowledge of system parameters.
Solution Approach 2:
The RL agent dynamically adapts its detection parameters and decision thresholds based on learned system behavior rather than relying on fixed model-based parameters. This parameter adaptation allows the supervision mechanism to maintain high detection precision while being robust to uncertainties and inaccuracies in system characterization.
3Speed
If RL agent is deployed for fault detection, then response speed improves, but system complexity increases
Solution Approach 1:
The RL agent is designed to be self-contained, integrating both the learning capability and decision-making functionality within a single agent architecture. This self-service design allows the agent to autonomously learn system dynamics, detect faults, and trigger mitigation actions without requiring separate complex infrastructure for model management, parameter tuning, or coordination between multiple components.
Solution Approach 2:
The RL agent serves multiple functions simultaneously: it learns system dynamics, detects various types of faults, localizes anomalies, and triggers mitigation strategies. This multi-functionality consolidates what would traditionally require multiple separate systems into a single universal supervisor, achieving fast response speeds while managing complexity through functional integration rather than proliferation of components.
Data Source
AI summary
Embodiments described herein provide a supervisor for fault management at a production system. During operation, the supervisor can obtain a set of sensor readings and a state of the production system. A respective sensor reading is an output of a sensor in the production system. The supervisor can then determine, using an artificial intelligence (AI) model, whether the set of sensor readings accommodates a fault associated with a corresponding sensor. Subsequently, the supervisor can determine an action that mitigates an effect of the fault and modify the set of sensor readings based on the action. Here, the modified set of sensor readings is used by a controller that controls the production system.