Distributed Prognostic Control for Subsystem Degradation
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Solution Overview
Problem
Existing control strategies for distributed systems do not effectively consider subsystem performance degradation, leading to potential system failures and increased maintenance costs, as they assume all subsystems will function properly until failure, without proactive measures to extend operational availability.
Innovation Solution
A Resilient Operation and Control Strategy (ROCS) that utilizes prognostic analysis to estimate future conditions of subsystems, adjusting utilization levels and scheduling to delay maintenance, minimize risk, and extend system reliability by diverting tasks from compromised subsystems to healthy ones, incorporating distributed prognostic engines for real-time optimization and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing control strategies assume all subsystems will function properly until failure, then system operation simplicity is maintained, but system reliability deteriorates due to potential unexpected failures and increased maintenance costs
Solution Approach 1:
The system performs preliminary prognostic analysis to estimate future conditions of subsystems before actual failures occur. By predicting future states based on current operational data, the system can proactively adjust control strategies to prevent failures, thereby improving reliability while maintaining operational simplicity through automated predictions rather than complex manual monitoring.
Solution Approach 2:
The system implements a feedback mechanism where prognostic analysis results are continuously fed back into the control strategy. The controller uses estimated future conditions to dynamically adjust subsystem utilization, creating a closed-loop system that automatically responds to predicted degradation trends, thus enhancing reliability without requiring complex manual intervention.
2Productivity
If subsystems are operated at high utilization levels, then productivity is improved, but the need for maintenance arises sooner, increasing maintenance costs and reducing system availability
Solution Approach 1:
The control strategy dynamically adjusts subsystem utilization levels based on real-time prognostic analysis. Instead of static high or low utilization settings, the system continuously adapts operational demands to match predicted subsystem health trajectories, allowing maximum productivity when subsystems are healthy while proactively reducing utilization as degradation is anticipated, thereby extending maintenance intervals.
Solution Approach 2:
The system changes operational parameters (utilization levels) based on estimated future conditions. By adjusting workload分配 and operational intensity according to prognostic predictions, the system optimizes the trade-off between productivity and maintenance timing, extending the time to maintenance while maintaining high overall productivity through intelligent parameter adaptation.
3Reliability
If proactive measures are implemented to estimate future subsystem conditions and adjust utilization, then system availability is extended, but device complexity increases due to additional prognostic engines and control mechanisms
Solution Approach 1:
The prognostic functionality is segmented and distributed across individual subsystems rather than centralized. Each subsystem has its own prognostic capabilities that analyze its own operational data, reducing the complexity of a centralized system while maintaining comprehensive monitoring. This distributed architecture improves scalability and reduces overall system complexity.
Solution Approach 2:
Subsystems perform self-diagnosis and self-assessment of their future conditions through local prognostic analysis. Each subsystem independently evaluates its own health status and communicates predictions to the controller, eliminating the need for complex external monitoring systems and reducing overall device complexity while maintaining high availability through autonomous health assessment.
Data Source
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AI summary
A method for controlling a system including a plurality of subsystems, includes receiving operational data from the plurality of subsystems of the system (S21). A future condition of each of the plurality of subsystems is estimated from the received operational data (S22). A control strategy for delaying a need for system maintenance is generated based on the estimated future condition of each of the plurality of subsystems (S23). An operation of the system is controlled based on the generated control strategy (S24).