Electronic Equipment Fault Detection Method Based on Deep Clustering Network
This article studies the fault detection method for electronic devices based on deep clustering networks,and designs a fault detection system for electronic devices based on deep clustering networks.Firstly,design the overall scheme of electronic equip-ment fault detection methods;Then,in order to further improve the accuracy of fault detection in the system,a self attention mecha-nism and residual blocks are introduced into the deep clustering network to improve and optimize it;Finally,experimental testing was conducted on the electronic device fault detection system constructed by the optimized deep clustering network.The experimental re-sults show that the clustering accuracy of the deep clustering network optimization algorithm after introducing self attention mechanism and residual blocks reaches 94.28％,which is 4.17％higher than the traditional deep clustering network algorithm.The CH index and contour index are significantly improved,and the Davidson Boding index is reduced by 8.06％,indicating that the clustering effect becomes better after the algorithm is optimized;The fault diagnosis accuracy of the electronic device detection system built using the optimized deep clustering network reached 93.91％,which is 24.03％higher than traditional clustering algorithms and 6.26％higher than traditional clustering algorithms based on dimensionality reduction.This indicates that the optimized deep clustering net-work used in this article has obvious advantages in electronic device fault detection,and the electronic device fault detection system based on deep clustering network is feasible and effective,And the accuracy of fault diagnosis is high.
deep clustering networkelectronic equipment fault detectionself attention mechanismresidual block
万方数据
维普
