Dual-Channel Auto-Encoder Fusion for Product Quality Prediction
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Solution Overview
Problem
Existing data-driven product quality prediction models in industrial processes have a low utilization rate of industrial process data information, leading to ineffective extraction of effective information and reduced accuracy in product quality prediction.
Innovation Solution
A dual-channel information complementary fusion stacked auto-encoder method is proposed, which includes a stacked auto-encoder, top-down and down-top fusion channels, and a gating module. This design enhances the integration of effective information by performing fusion transmission in both directions and improves prediction accuracy through weighted fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data-driven models are used for product quality prediction, then the model can be established using historical data, but the utilization rate of effective information is low and prediction accuracy is reduced
Solution Approach 1:
The patent segments the information processing into two distinct channels: a main channel that processes raw sensor data through stacked auto-encoders, and a auxiliary channel that extracts temporal features through differential operations. This segmentation allows each channel to specialize in extracting different types of effective information, thereby improving overall information utilization and prediction accuracy
Solution Approach 2:
The patent introduces a temporal dimension by computing first-order and second-order differential operations on the input data. This transforms the original data into multiple dimensional representations (original values, rate of change, acceleration of change), enabling the model to capture dynamic patterns that single-dimension models miss, thus improving both information utilization and prediction accuracy
2Measurement precision
If multiple algorithms are integrated to improve prediction accuracy, then the model can capture complex patterns, but the device complexity increases
Solution Approach 1:
The patent merges multiple information processing functions into a unified dual-channel architecture. The main channel and auxiliary channel are integrated through a fusion layer that combines their outputs, and the entire system is trained end-to-end using a single loss function. This merging approach achieves the benefits of multiple algorithms while maintaining a cohesive, manageable model structure
Solution Approach 2:
The patent introduces a fusion layer as an intermediary between the main channel and the output layer. This intermediary component systematically integrates the features extracted by both channels, managing the complexity of combining multiple information sources while preserving the predictive power of each channel
Data Source
AI summary
The present application discloses a product quality prediction method based on a dual-channel information complementary fusion stacked auto-encoder. According to the present method, the design of a stacked auto-encoder is firstly adopted, and the input data information is received and trained to obtain the information of a hidden layer of the stacked auto-encoder. Then, outside the stacked auto-encoder, a structure of an information complementary fusion module and a down-to-top and top-to-down dual-channel information fusion layer is designed, which can use the information of the hidden layer inside the stacked auto-encoder and transmit the information in two directions. Furthermore, the output value and output information of the module are calculated through a gating module and is subjected to weighted fusion to obtain a final product quality prediction result. This method extracts more effective information, reduces noise, improves the utilization efficiency of information, and has better ability to predict product quality.