Prognostic Rules for Aircraft Part Failure Prediction
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Solution Overview
Problem
Identifying future part failures in aircraft is challenging due to the vast amount of collected information and the complexity of aircraft systems, making it difficult, time-consuming, and resource-intensive to pinpoint useful data for predictive purposes.
Innovation Solution
A prognostic platform that analyzes operational, maintenance, and environmental information from multiple aircraft operations to generate prognostic rules, which are then used to predict future part failures, thereby enabling preventative or remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vast amounts of operational and maintenance data are collected from aircraft systems, then prediction accuracy for part failures is improved, but data processing complexity and resource consumption increase
Solution Approach 1:
The system extracts and isolates specific prognostic indicators from vast amounts of operational and maintenance data. By identifying and extracting only the most relevant features and patterns associated with part failures, the system achieves high prediction accuracy while avoiding the need to process entire datasets, thereby reducing computational complexity.
Solution Approach 2:
The patent segments the large volume of aircraft data into distinct categories (operational data, maintenance data, environmental data) and further divides it into equipment-specific subsets. This segmentation allows the system to process manageable portions of data independently, reducing overall processing complexity while maintaining comprehensive analysis for accurate predictions.
2Reliability
If comprehensive equipment information and anomalies are analyzed to predict part failures, then reliability improvement is achieved, but time and resources required for analysis increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing and organizing operational and maintenance data during normal operations. Prognostic indicators are calculated and stored in advance, so when failure prediction is needed, the system can quickly retrieve and analyze pre-computed information rather than processing raw data from scratch, significantly reducing analysis time while maintaining comprehensive reliability assessment.
Solution Approach 2:
The patent replaces manual or traditional mechanical analysis methods with automated computational algorithms and machine learning models. This substitution enables the system to rapidly process and analyze comprehensive equipment information, reducing the time required for analysis while improving the accuracy and reliability of failure predictions.
3Measurement precision
If detailed operational information and messages are processed to identify associations with part failures, then prediction quality is improved, but computing resource consumption increases
Solution Approach 1:
The system applies local quality by focusing computational resources on analyzing specific equipment components and their associated data rather than uniformly processing all aircraft data. By identifying and concentrating analysis on areas with highest failure risk or most significant prognostic value, the system achieves high prediction quality while minimizing overall computing resource consumption.
Data Source
AI summary
A device may receive equipment information, associated with a first equipment, including information associated with anomalies identified based on operational information collected during operation of the first equipment, and messages generated during the operation of the first equipment. The device may receive maintenance information, associated with the first equipment, that identifies one or more part failures associated with one or more equipment parts. The device may identify associations between the one or more part failures and the first equipment information. The device may receive equipment information, associated with a second equipment, including information associated with anomalies identified based on operational information collected during operation of the second equipment, and messages generated during the operation of the second equipment. The device may generate and provide a prediction, associated with a future failure of an equipment part of the second equipment, based on the second equipment information and the associations.