锻压技术

萤火虫优化BP神经网络离合器制动器故障诊断

英文标题：Fault diagnosis on clutch brake based on firefly optimization BP neural network

作者：胡凯曹春平孙宇王禹

分类号：TH165

出版年,卷(期):页码：2023,48(6):124-129

摘要：

为解决高速压力机离合器制动器的故障问题，提出了萤火虫优化BP神经网络故障诊断算法，可防止传统的BP神经网络算法中容易进入局部最优等问题。介绍了一般离合器制动器故障的类型和原因，并分析了萤火虫优化BP神经网络故障诊断算法的基本理论，建立了基于该算法的离合器制动器故障诊断模型。以高速压力机中的离合器制动器为研究对象，采用Matlab进行仿真验证。结果表明：萤火虫算法优化BP神经网络的故障诊断误差明显小于传统BP神经网络和遗传算法优化BP神经网络的误差，故障诊断正确率可达89.167%。

In order to solve the fault problem of clutch brake in high speed press, a fault diagnosis algorithm based on firefly optimization BP neural network was proposed, which prevented the problems such as easy entry into local optimum in the traditional BP neural network algorithm. Then, the types and causes of general clutch brake faults were introduced, and the basic theory for fault diagnosis algorithm of firefly optimization BP neural network was analyzed to establish a clutch brake fault diagnosis model based on this algorithm. For the clutch brake in high-speed press, the simulation verification was carried out by Matlab. The results show that the fault diagnosis error of firefly optimization BP neural network is significantly smaller than that of the traditional BP neural network and the genetic algorithm optimization BP neural network, and the correct rate of fault diagnosis reaches 89.167%.

基金项目：

江苏省科技成果转化专项资金（BA2021067）

胡凯（1996-），男，硕士研究生

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