Multi-Scale Quality Detection for Complex Industrial Processes
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Solution Overview
Problem
Current product quality detection in complex industrial processes relies heavily on post-production sampling and manual detection, leading to high costs and lag, and traditional data-driven methods struggle to accurately capture multi-scale and multi-process data features, failing to provide real-time monitoring and quality control.
Innovation Solution
A product quality detection method that integrates prior knowledge to fuse features across multiple processes using multi-scale feature extraction networks and a feature fusion module, employing convolutional neural networks to analyze production data from multiple scales and processes, and a two-layer full-connection network for quality prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional sampling and manual detection methods are used for product quality detection, then detection cost is reduced and operational simplicity is maintained, but real-time monitoring capability is lost and detection lag increases
Solution Approach 1:
The patent replaces manual detection mechanisms with an automated machine learning system that processes production data in real-time. The system uses trained models to automatically predict product quality based on production process data, eliminating the need for manual sampling and detection while achieving real-time monitoring capability.
2Measurement precision
If traditional data-driven modeling methods are used to establish relationship models between production process data and product quality, then model construction simplicity is maintained, but feature representation accuracy deteriorates due to inability to capture complex multi-process relationships
Solution Approach 1:
The patent segments the complex industrial production process into multiple distinct process stages (e.g., heating process, rolling process, cooling process). Each process stage is modeled separately with its own feature extraction and relationship modeling, allowing the system to capture the specific characteristics and relationships of each process while maintaining overall system accuracy.
Solution Approach 2:
The patent introduces a multi-dimensional modeling approach by considering both temporal dimensions (time-series data within each process) and process dimensional (multiple distinct production processes). This multi-dimensional framework enables the model to capture complex relationships across different processes and time scales simultaneously.
3Loss of information
If traditional feature extraction methods that consider only single time scale features are used, then computational simplicity is maintained, but feature completeness deteriorates by ignoring correlations among multiple variables across different scales
Solution Approach 1:
The patent applies periodic action by extracting features at multiple different time scales (short-term, medium-term, long-term patterns). The model periodically analyzes data at various granularities, capturing both fine-grained transient features and coarse-grained trend features, thereby achieving comprehensive feature representation without excessive computational burden.
Data Source
AI summary
Provided is a product quality detection method in a complex industrial process fusing prior knowledge. Related data of product production in the complex industrial process is acquired to serve as samples; The acquired samples are preprocessed and a training data set is constructed by the preprocessed samples; a product quality detection model in the complex industrial process fusing prior knowledge is constructed; the product quality detection model is trained by the training data set; and the trained quality detection model is used for product quality detection to obtain product quality. The present invention designs that feature extraction on multiple scales is supported by a multi-scale feature extraction network with convolutional kernels with different sizes, and designs that the extracted features of each process are fused by a multi-process feature fusion method of the prior knowledge, thereby obtaining more accurate feature representation and increasing the production benefit.