Spinning Box Fault Detection With DETR for Automated Inspection
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Solution Overview
Problem
The efficiency of manual inspection of spinning boxes in spinning workshops is low, necessitating an automated and accurate fault detection method for critical components like the heating device and melt distribution pipe to ensure consistent temperature control and detect open areas for engineering auxiliary materials.
Innovation Solution
A detection method utilizing a DETR (DEtection Transformer) network for image processing, including a decoder network with adaptive classification heads and query sorting layers, to automatically identify faults in the spinning box components and detect material accumulation, using drones for image collection and thermal imaging for temperature monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection is used for spinning boxes, then operation simplicity is maintained, but inspection efficiency is low
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated detection system that uses image collection devices, encoder networks, and decoder networks with adaptive classification heads to automatically identify faults in spinning boxes, thereby improving inspection efficiency while managing system complexity through intelligent algorithms
Solution Approach 2:
The detection system performs self-service by automatically collecting images, extracting features, processing through encoder-decoder networks, and generating fault detection results without requiring manual intervention, enabling the system to inspect itself and maintain continuous monitoring
2Extent of automation
If automated detection is implemented, then inspection efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments the detection system into distinct functional modules: image collection units, encoder networks for feature extraction, decoder networks with multiple decoder modules for fault detection, and adaptive classification heads for fault type identification. This segmentation allows each component to perform its specific function independently, managing overall system complexity while achieving high automation
Solution Approach 2:
The system incorporates dynamic elements through the adaptive classification heads that adjust their behavior based on input features and the decoder network that processes information through multiple sequential modules, enabling the automated detection system to adapt to different fault conditions while maintaining manageable complexity through structured architecture
3Measurement precision
If manual inspection is used, then system simplicity is maintained, but fault detection accuracy is insufficient
Solution Approach 1:
The patent replaces manual visual inspection with an automated system that uses image collection devices to capture detailed images, encoder networks to extract comprehensive features, and decoder networks with adaptive classification heads to accurately identify fault types and positions, significantly improving fault detection accuracy through intelligent processing
Solution Approach 2:
The patent introduces intermediary processing components including the encoder network that acts as a mediator between image collection and fault detection, and the decoder network with adaptive classification heads that serves as an intermediary for translating extracted features into accurate fault diagnoses, thereby achieving high accuracy while managing system complexity through structured intermediate processing stages
Data Source
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AI summary
Provided is a detection method and apparatus for a spinning workshop, and a storage medium; relating to the field of data processing. The method includes: performing (S101) image collection on a process control device of a spinning box in the spinning workshop to obtain an image to be processed; extracting (S102) a first image feature from the image to be processed; and processing (S103) the first image feature based on a decoder network to obtain a fault detection result. The decoder network comprises a plurality of decoder modules connected in series in sequence. Each decoder module includes a decoder layer and an adaptive classification head. The adaptive classification head is configured to perform classified prediction on an output feature of the decoder layer to obtain a first fault classification result. The fault detection result output by the decoder network includes a second fault classification result and a fault position.