Adaptive Component Life Management via Sensor Data
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Solution Overview
Problem
Current methods for predicting the life of components and scheduling inspections are inadequate, as they do not accurately account for the probabilistic nature of component deterioration and the varying operating conditions, leading to potential failures due to undetected flaws.
Innovation Solution
An inspection system that uses sensors to collect spatial data, a computing system with a database and modules for spatial registration, estimation, and prediction, which estimates the current condition of components and predicts future conditions based on historical data, determining if replacement or repair is necessary and rescheduling inspections as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If periodic inspection is used to detect flaws, then the risk of undetected failures is reduced, but inspection costs and operational downtime increase
Solution Approach 1:
The inspection system transitions from static periodic scheduling to dynamic condition-based scheduling. Inspection intervals are adjusted in real-time based on the component's actual condition state, allowing extensions when conditions are good and compression when deterioration is detected, thereby reducing unnecessary downtime while maintaining reliability
Solution Approach 2:
The system implements continuous feedback loops where inspection data is fed back into the prediction model to update component condition estimates. This feedback mechanism enables adaptive adjustment of inspection schedules, optimizing the balance between detection reliability and operational time loss by inspecting only when necessary
2Reliability
If frequent inspection is performed to detect early flaws, then component safety is improved, but inspection costs increase
Solution Approach 1:
The system applies inspection resources partially and selectively rather than uniformly. By using prediction models to identify components at higher risk of failure, inspection efforts are concentrated on those specific components that need monitoring, reducing overall inspection costs while maintaining safety through targeted detection of early flaws
Solution Approach 2:
The system changes the parameter of inspection frequency from a fixed value to a variable determined by component condition. Risk-based parameters guide inspection scheduling, allowing the system to optimize the balance between safety and cost by adjusting inspection intensity based on actual component state and predicted deterioration trends
3Reliability
If component replacement is performed based on conservative safe life models, then failure risk is reduced, but replacement costs and resource loss increase
Solution Approach 1:
The system performs preliminary condition assessment and prediction before replacement decisions are made. By estimating current condition and forecasting future deterioration, the system enables proactive planning of replacement timing, avoiding both premature replacement of still-functional components and delayed replacement of deteriorating components
Solution Approach 2:
The system replaces conservative mechanical replacement schedules with data-driven predictive analytics. Machine learning models and condition monitoring data substitute for traditional safe life calculations, enabling more accurate determination of optimal replacement timing that reduces unnecessary component loss while maintaining acceptable failure risk levels
4Duration of action of moving object
If traditional safe life models are used to predict component life, then a baseline replacement schedule is established, but accuracy in predicting actual component condition deteriorates
Solution Approach 1:
The system segments the component population into risk groups based on individual condition assessments rather than treating all components uniformly. By dividing components into segments with different deterioration rates and risk profiles, the system achieves more accurate predictions for each segment while maintaining overall population management
Solution Approach 2:
The system introduces condition monitoring data and prediction models as intermediaries between theoretical safe life models and actual component behavior. These intermediaries translate conservative theoretical predictions into accurate real-world condition assessments by incorporating actual inspection data, operating history, and environmental factors
Data Source
AI summary
A framework for adaptively managing the life of components. A sensor provides non-destructive test data obtained from inspecting a component. The inspection data may be filtered using reference signatures and by subtracting a baseline. The filtered inspection data and other inspection data for the component is analyzed to locate flaws and estimate the current condition of the component. The current condition may then be used to predict the component's condition at a future time or to predict a future time at which the component's condition will have deteriorated to a certain level. A current condition may be input to a precomputed database to look up the future condition or time. The future condition or time is described by a probability distribution which may be used to assess the risk of component failure. The assessed risk may be used to determine whether the part should continue in service, be replaced or repaired. A hyperlattice database is used with a rapid searching method to estimate at least one material condition and one usage parameter, such as stress level for the component. The hyperlattice is also used to rapidly predict future condition, associated uncertainty and risk of failure.