Engine Health Monitoring for Fault Isolation and Model Alignment
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Solution Overview
Problem
Vehicle engine systems face challenges in accurately identifying and isolating health degradation and faults due to aging and wear, which can lead to misalignment of control models with reality, necessitating new approaches for continuous monitoring and mitigation.
Innovation Solution
A configurable controller system that includes a state observer, feature calculator, optimizer, health estimator, fault isolator, and mitigator to monitor and adjust the engine's operation based on real-time data from sensors, allowing for the identification of faults and health degradation, and modifying control models to maintain optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If model based control methods are introduced to manage complex technology and requirements, then system control capability is improved, but model alignment with reality deteriorates over time due to component aging and wear
Solution Approach 1:
The system performs preliminary health degradation estimation and fault isolation before actual failures occur. By continuously monitoring feature parameters and comparing them against baseline data, the system detects early signs of component degradation and takes preventive actions, preventing the model-reality misalignment from developing into critical failures.
Solution Approach 2:
The system implements continuous feedback loops where sensor data is processed to estimate health degradation, which then feeds back to adjust control parameters and update system models. This closed-loop feedback mechanism ensures models remain aligned with actual component conditions by dynamically adapting to aging and wear.
2Measurement precision
If continuous health monitoring is implemented to identify degradation occurrence, then fault detection accuracy is improved, but system complexity increases
Solution Approach 1:
The health monitoring system is segmented into distinct functional modules: feature parameter extraction, health degradation estimation, fault isolation, and mitigation. Each module handles a specific aspect of the monitoring process, making the overall complex system manageable through modular design and enabling targeted optimization of each component.
Solution Approach 2:
The system introduces intermediate feature parameters as mediators between raw sensor data and final fault detection. Instead of directly analyzing complex sensor signals, the system extracts meaningful feature parameters that serve as intermediaries, simplifying the analysis process while maintaining high detection accuracy.
3Reliability
If fault isolation is performed to identify the source of change, then system reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary fault isolation by continuously comparing current feature parameters against baseline data and identifying deviations. This early identification allows the system to isolate potential fault sources before they develop into actual failures, reducing the time needed for diagnostic processing when issues do arise.
Data Source
AI summary
Methods and systems for fault identification and mitigation in an engine system. A state observer obtains current state information from the engine system, and a feature calculator uses data obtained from the state observer to calculate one or more feature indicators, which are monitored by a health estimator for the occurrence of a change using one or more change probability models. When the health estimator identifies a change, a fault isolator determines a component of the engine system that is subject to fault or health deterioration.