Avionic Health Assessment With Multi-Model Fusion and AdaBoost
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing health assessment methods for avionic products face challenges in harsh environmental conditions and complex working conditions due to insufficient generalization, poor assessment accuracy, and model differences across multi-level and multi-scenario applications, particularly in avionic systems with diverse electromagnetic, mechanical, and environmental stresses.
Innovation Solution
A multi-model fused avionic product health assessment method involving data collection, preprocessing, training multiple base models, and integrating them using the Adaboosting algorithm to achieve high stability and accuracy, suitable for on-condition maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single health assessment model is used for avionic products in harsh environmental conditions, then the model structure is simple, but the prediction precision and stability deteriorate due to insufficient generalization across multi-level and multi-scenario applications
Solution Approach 1:
The patent segments the health assessment task into multiple specialized models (physical model, data-driven model, hybrid model) that operate at different levels (component, module, subsystem, system). Each model is optimized for specific scenarios and environmental conditions, allowing the system to handle complex multi-level assessment requirements while maintaining individual model simplicity and precision.
Solution Approach 2:
The patent creates a universal multi-model framework that can adapt to various avionic product types, environmental conditions, and assessment scenarios. The framework integrates multiple modeling approaches and enables cross-scenario generalization through standardized interfaces and adaptive selection mechanisms, making the system universally applicable across different avionic systems while maintaining high prediction precision.
2Measurement precision
If physics-of-failure models are customized for specific avionic products, then the model can capture specific fault mechanisms, but the cost increases and wide application becomes difficult
Solution Approach 1:
The patent develops a universal physical model framework that can be adapted to different avionic products through parameter configuration rather than complete model redesign. The framework includes standardized model structures, common failure mechanisms libraries, and adaptive parameter identification methods that enable low-cost deployment across multiple product types while maintaining accurate fault mechanism recognition for each specific application.
Solution Approach 2:
The patent enables customization of physical models for specific avionic products by changing parameters (environmental conditions, operational parameters, material properties) rather than restructuring the entire model. This parameter-based adaptation allows the same model framework to accurately capture different fault mechanisms across various products at minimal cost.
3Measurement precision
If multiple specialized models are used for different scenarios, then the prediction precision improves, but the system complexity increases
Solution Approach 1:
The patent segments the assessment system into modular specialized models that can be independently developed, validated, and updated. Each model handles specific scenarios or environmental conditions, reducing the complexity burden on individual models while collectively achieving high assessment accuracy across all scenarios through the segmented architecture.
Solution Approach 2:
The patent introduces an intermediary framework that manages multiple specialized models, handling model selection, integration, and coordination. This intermediary layer abstracts the complexity of managing multiple models, allowing them to work together to improve assessment accuracy while the framework manages the system-level complexity.
4Adaptability or versatility
If health management functions are mapped to multiple module contractors, then the system can leverage specialized expertise, but the coordination difficulty and interface complexity increase
Solution Approach 1:
The patent establishes a universal health management framework with standardized interfaces and common data models that can accommodate multiple specialized module contractors. The framework defines universal communication protocols, data exchange formats, and integration patterns that enable diverse contractors to contribute their specialized expertise while reducing interface coordination complexity through standardization.
Data Source
AI summary
A multi-model fused avionic product health assessment method includes the following steps: collecting relevant data of an avionic product; performing data pre-processing on the relevant data to obtain first data and second data; training a plurality of base models on the basis of the first data; performing quantitative measurement and fusion on the plurality of base models to obtain an integrated model; and inputting into the integrated model the second data which serves as a test sample to obtain a health assessment result of the avionic product. A plurality of base models are integrated by using an AdaBoost algorithm, and a reference can be provided for a method based on data driving in terms of application in the health assessment, prediction and management of an avionic product.