基于CNN的化工机械电路故障定位模型优化  

Optimization of Chemical Machinery Circuit Fault Location Model Based on CNN

作　　者：丁硕 DING Shuo(Tai'an Technician College,Tai'an 271000,China)

机构地区：[1]泰安技师学院,泰安271000

出　　处：《价值工程》2025年第10期106-108,共3页Value Engineering

摘　　要：针对化工机械电路故障定位难题,本文提出了一种基于卷积神经网络(CNN)的优化模型,通过改进网络结构与特征提取算法,提升故障诊断的准确性与效率。文章首先分析电路故障类型及其特征,探讨传统故障诊断方法的局限性,随后设计并优化CNN模型,包括数据预处理、网络结构设计与参数调优。实验验证表明,优化后的模型在故障诊断准确率与计算效率方面具有显著优势,并通过工程案例展示了其在实际应用中的可行性与效果。This paper proposes an optimization model based on Convolutional Neural Network(CNN)to address the problem of fault location in chemical machinery circuits.By improving the network structure and feature extraction algorithm,the accuracy and efficiency of fault diagnosis are enhanced.The article first analyzes the types and characteristics of circuit faults,explores the limitations of traditional fault diagnosis methods,and then designs and optimizes a CNN model,including data preprocessing,network structure design,and parameter tuning.Experimental verification shows that the optimized model has significant advantages in fault diagnosis accuracy and computational efficiency,and its feasibility and effectiveness in practical applications have been demonstrated through engineering cases.

关 键 词：化工机械 电路故障诊断 故障定位 卷积神经网络 特征提取 

分 类 号：TU473.1[建筑科学—结构工程]