Development Of Edge Control System For Wooden Door Processing Production Lin

Digitization and flexibility are the development trend of wooden door production lines,and establishing a reliable data collection system and fault diagnosis system is the key to ensuring its stable operation.The design of traditional fault diagnosis methods is relatively time-consuming and difficult to guarantee versatility.With the advent of the big data Era,new developments have been made in the field of fault diagnosis.The edge control system of the wooden door production line was proposed.Its main research content is data acquisition,data compression and a diagnosis method based on deep learning,which aims to improve the production efficiency of the wooden door production line.Firstly,the composition of the wooden door production line was sorted out and its current situation was analyzed.Combined with edge computing technology,the overall plan of the control system for the wooden door production line was proposed.Secondly,a collection and transmission plan for multiple heterogeneous devices was designed,so that the collected data can flow to the site control System and cloud platform quickly and efficiently.Thirdly,an end-to-end bearing fault diagnosis method based on deep learning was proposed,which can diagnose bearing vibration signals under various loads and high-noise environment.The method uses a convolutional neural network combined with a channel attention mechanism to extract efficiently global features and high-dimensional features are extracted from them by multi-layer convolutional neural network and are sent into the gated recurrent unit network to mine the interrelated information.The superiority and versatility of the proposed neural network model were confirmed through comparative experiments,and the network model was simplified through the deep separable convolutional neural network,which effectively improves the operating speed of the diagnostic method.Fourthly,a compression algorithm with high compression rate that can meet the specified compression accuracy requirements was proposed to solve the problem of excessive mechanical vibration data.The algorithm is composed of one-dimensional SPIHT encoding and dual VKTP encoding.The one-dimensional SPIHT encoding can directly compress one-dimensional vibration data,and the dual VKTP encoding can better compress the SPIHT encoding results.The compression performance is verified by comparing with other compression methods.Finally,a remote platform for the edge control system was developed,and the engineering feasibility of the overall method was verified through an example operation.The edge control system for the wooden door production line designed in this thesis realizes the real-time monitoring of the key components of the equipment on the production line,and has the function of linking the collected data to the production control system,which greatly improves the safety and production efficiency of the wooden door production line,and provides solutions for the digitalization,networking and intelligent transformation of traditional wooden door production lines.