基于IPSO优化PNN方法的耕耘机齿轮箱故障诊断  

Fault Diagnosis of Cultivator Gear Box Based on IPSO Optimization PNN Method

作　　者：程友杰 Cheng Youjie(Department of Electrical Engineering,Luohe Technician College,Luohe Henan 462000,China)

机构地区：[1]漯河技师学院电气工程系,河南漯河462000

出　　处：《山西电子技术》2024年第2期14-15,57,共3页Shanxi Electronic Technology

摘　　要：分析耕作机齿轮箱的振动信号有助于判断其故障诊断结果。概率神经网络(PNN)具备自适应学习、非线性分析与优异故障信号识别能力,对于神经网络算法缺陷具有良好的弥补效果。设计了一种优化粒子群算法(IPSO)优化PNN方法,并应用于齿轮箱振动信号检测领域,实现齿轮箱振动参数的精确判断。研究结果表明:本文算法也可以消除重复迭代计算过程的冗余操作,大幅缩短振动分类过程所需的时间。该研究有助于提高农业机械设备的运行效率,可以拓展到其他的机械传动领域,具有很广的应用市场。Analyzing the vibration signal of the gear box of the tiller is helpful to judge the fault diagnosis result.Probabilistic neural network(PNN)has the ability of adaptive learning,nonlinear analysis and excellent fault signal recognition,which can make up for the defects of neural network algorithm.An IPSO optimization PNN method is designed and applied in the field of vibration signal detection of gear box to realize the accurate judgment of vibration parameters.The results show that the proposed algorithm can also eliminate the redundant operations in the repeated iterative calculation process and greatly shorten the time required for vibration classification process.This research is helpful to improve the operation efficiency of agricultural machinery and equipment,which can be extended to other mechanical transmission fields and has a wide range of application markets.

关 键 词：粒子群优化 机械传动 齿轮箱 振动 神经网络 

分 类 号：TH137[机械工程—机械制造及自动化]