Generative Adversarial Multi-Headed Attention for Aero-Engine Data Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current algorithms for reconstructing missing aero-engine data fail to effectively utilize attribute correlation and temporal correlation, leading to reduced accuracy in data imputation and subsequent predictions and maintenance.
Innovation Solution
A generative adversarial network (GAN) modeling method based on a convolutional multi-headed attention mechanism is employed to impute missing aero-engine data, leveraging the spatial and temporal information within the data to enhance imputation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional statistics-based imputation methods (mean imputation, plurality imputation) are used, then the calculation process is simple, but the accuracy of data imputation is low and effective information is lost
Solution Approach 1:
The patent replaces traditional statistics-based imputation methods with a deep learning-based generative adversarial network (GAN) that uses convolutional neural networks and self-attention mechanisms. This substitution transitions from simple statistical calculations to a more sophisticated neural network system that can capture complex correlations in multivariate time series data, thereby improving imputation accuracy while maintaining computational feasibility through the automated learning process
Solution Approach 2:
The patent changes the fundamental parameters and approach of data imputation by introducing a GAN-based neural network model with multiple attention heads that dynamically learn and adapt to the specific characteristics of the input data. This allows the system to automatically adjust to different data patterns and correlations, improving accuracy compared to fixed statistical methods
2Measurement precision
If great likelihood estimation method is used, then the accuracy of data imputation is improved, but the calculation complexity increases significantly
Solution Approach 1:
The patent replaces the complex great likelihood estimation method with a deep learning-based generative adversarial network that learns data distributions and correlations through automated training. The neural network model, with its convolutional layers and self-attention mechanisms, automatically captures complex relationships without requiring explicit mathematical formulation of likelihood functions, thereby maintaining high accuracy while simplifying the computational approach
Solution Approach 2:
The patent implements self-service by using the neural network model to automatically learn and adapt to the specific characteristics of the input data through the training process. The model self-adjusts its parameters and attention mechanisms to optimize imputation accuracy for the given dataset, eliminating the need for complex manual calculations and assumptions required by great likelihood estimation
3Quantity of substance
If KNN imputation method is used with large data size, then the data coverage is improved, but the computation time increases significantly
Solution Approach 1:
The patent replaces the computationally intensive KNN imputation method with a neural network-based GAN model that processes data through learned representations. The convolutional and attention-based architecture efficiently captures global patterns and correlations across the entire dataset without requiring pairwise distance calculations for each imputation point, thereby maintaining comprehensive data coverage while significantly reducing computation time
Solution Approach 2:
The patent applies preliminary action by training the neural network model beforehand on the available data to learn the underlying patterns and correlations. Once trained, the model can quickly perform imputation on new or incomplete data without requiring intensive real-time calculations, thus achieving both comprehensive coverage and efficient processing
4Loss of time
If KNN imputation method is used with small data size, then the computation time is reduced, but the imputation accuracy cannot be guaranteed
Solution Approach 1:
The patent changes the fundamental approach from distance-based KNN to a neural network-based GAN that learns data distributions and correlations through automated training. The model's attention mechanisms and convolutional layers enable it to capture even subtle patterns in small datasets, improving accuracy while maintaining computational efficiency through the pre-trained model architecture
Data Source
AI summary
A generative adversarial multi-headed attention neural network self-learning method for aero-engine data reconstruction belongs to the field of end-to-end self-learning of aero-engine missing data. First, the samples are pre-processed, and the machine learning algorithm is used to pre-fill the normalized data first, and the pre-filled information is involved in the network training as part of the training information. Second, a generative adversarial multi-headed attention network model is constructed and the trained sample set is used to train the generative adversarial multi-headed attention network model. Finally, the samples are generated using the trained sample generator G. The method uses the generative adversarial network to better learn the distribution information of the data, and uses parallel convolution and multi-headed attention mechanism to fully exploit the spatial and temporal information among the aero-engine data.