Component Distress Prediction Model for Reliable Maintenance Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting distress in physical components, especially in high-stress environments like aircraft engines, lack accuracy and reliability, leading to unplanned maintenance and potential in-flight shutdowns due to inadequate early detection of hardware issues.
Innovation Solution
A method and system that involve obtaining distress data, determining a distress rank, formulating a kernel using environmental and operational data, generating a predictive model, and iteratively tuning it to improve prediction accuracy, allowing for precise forecasting of component distress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current prediction methods are used, then maintenance scheduling can be performed, but prediction accuracy and reliability are insufficient leading to unplanned disruptions
Solution Approach 1:
The patent transforms physical component distress into a standardized parameter scale (0-10 distress rank) based on multiple data sources including operational data, environmental data, and inspection data. This parameter transformation enables quantitative comparison and improves prediction reliability by converting qualitative assessments into measurable parameters.
Solution Approach 2:
The patent replaces traditional mechanical prediction methods with a data-driven machine learning model. The system uses neural networks and algorithms that process operational parameters, environmental conditions, and inspection results to generate distress predictions, substituting physical/mechanical assessment methods with computational intelligence for higher accuracy.
2Reliability
If early detection methods are implemented, then in-flight shutdowns can be prevented, but detection capability is currently inadequate
Solution Approach 1:
The system performs preliminary distress assessment by continuously monitoring operational data and environmental conditions before actual distress occurs. The model predicts future distress ranks based on current trends, enabling maintenance scheduling before critical failures happen, thus preventing in-flight shutdowns through advance detection.
Solution Approach 2:
The patent introduces an intermediary distress rank scale (0-10) that mediates between raw sensor data and final distress predictions. This intermediary representation simplifies the detection process by translating complex multi-parameter inputs into an interpretable distress rank that indicates component health status and predicted remaining life.
3Measurement precision
If predictive modeling is performed, then maintenance scheduling improves, but model accuracy requires iterative tuning
Solution Approach 1:
The system implements feedback mechanisms where actual inspection results and component performance data are fed back into the predictive model to continuously refine accuracy. The model compares predicted distress ranks with actual observed distress, using this feedback to adjust parameters and improve future predictions, thereby reducing model tuning complexity through automated learning.
Solution Approach 2:
The predictive model is designed to be dynamic rather than static, automatically adapting to new data and changing operational conditions. The system updates distress predictions in real-time as new operational data becomes available, and the model parameters evolve through iterative learning, reducing the need for manual retuning while maintaining high accuracy.
Data Source
AI summary
An apparatus and method for predicting distress on a physical component. The method can include obtaining distress data. The distress data can be used to determine a distress rank. The distress rank can be compared to a distress output provided by a kernel that use parameters related to the physical component. The comparison can result in a prediction model for the physical component.