Spinning Box Fault Detection with Adaptive Decoder Networks
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Solution Overview
Problem
The efficiency of manual inspection of spinning boxes in spinning workshops is low, necessitating a more effective method for fault detection in the process control devices.
Innovation Solution
A detection method utilizing a decoder network with multiple decoder modules, each comprising a decoder layer and an adaptive classification head, to automatically identify faults in spinning workshop components like the heating device and melt distribution pipe, using image processing and thermal imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection is used for spinning boxes, then the inspection method is simple, but the 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 processing
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 continuously improve through adaptive classification
2Measurement precision
If a decoder network with adaptive classification head is used, then fault detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the detection system into distinct functional modules: image collection, feature extraction through encoder network, decoding through multiple decoder modules, and adaptive classification heads. Each module performs a specific function, making the complex overall system manageable and easier to implement while maintaining high accuracy
Solution Approach 2:
The adaptive classification head dynamically adjusts its classification based on the output features from the decoder layer, allowing the system to adapt to different fault types and conditions. This dynamic adaptation improves detection accuracy across varying scenarios without requiring a completely different system for each case
Data Source
AI summary
Provided is a detection method for a spinning workshop, an electronic device and a storage medium; relating to the field of data processing. The method includes: performing image collection on a process control device of a spinning box in the spinning workshop to obtain an image to be processed; extracting a first image feature from the image to be processed; and processing 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.