Aircraft Failure Prediction Explainability via Partial Dependency Functions
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Solution Overview
Problem
Existing failure prediction methods in aircraft systems lack explainability, making it difficult for maintenance personnel to understand why failures are predicted and how to prevent them, which can lead to mistrust and ineffective management of aircraft systems.
Innovation Solution
An aircraft component failure prediction apparatus that includes a database and an aircraft maintenance controller, which classifies operational data using a machine learning model to predict future maintenance messages and generates partial dependency functions to explain the predictions by identifying key operational data and their ranges, providing insights into why failures are predicted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used for failure prediction, then prediction accuracy is improved, but explainability of predictions deteriorates
Solution Approach 1:
The patent introduces partial dependency functions as an intermediary between the machine learning model and the user. These functions serve as a mediator that translates the complex internal workings of the ML model into interpretable visualizations showing which operational parameters and ranges contribute to failure predictions, thus maintaining both accuracy and explainability
Solution Approach 2:
The system changes the parameter representation by generating visualizations that display operational parameters and their ranges rather than raw model outputs. This transformation converts complex model predictions into understandable parameter-based explanations that maintain predictive accuracy while improving interpretability
2Reliability
If comprehensive operational data is analyzed, then prediction reliability is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts and removes classification obscuring data from the multidimensional operational data matrices before processing. This extraction of irrelevant or confusing data elements simplifies the processing complexity while maintaining prediction reliability by focusing only on the most relevant operational parameters
Solution Approach 2:
The system segments the comprehensive operational data into classified multidimensional operation data matrices organized by maintenance messages. This segmentation breaks down complex data into manageable categories, making processing more systematic while preserving the comprehensive nature of the analysis
Data Source
AI summary
An aircraft component failure prediction apparatus including a database and an aircraft maintenance controller coupled to the database. The controller classifies operational data, in a first classification, as corresponding with at least one maintenance message to form at least one classified multidimensional operation data matrix including classified data. The controller classifies the classified data of the at least one classified multidimensional data matrix, in a second classification, to predict an occurrence of a future maintenance message for the aircraft component. The controller preprocesses the at least one classified multidimensional operation data matrix, and generates an output of at least one partial dependency function that explains prediction of the occurrence of the future maintenance message.