[1]肖祯怀.基于粒子群算法的旋转机械故障诊断[J].机械与电子,2023,41(07):71-75.
　XIAO Zhenhuai.Rotating Machinery Fault Diagnosis Based on Particle Swarm Optimization[J].Machinery & Electronics,2023,41(07):71-75.
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基于粒子群算法的旋转机械故障诊断()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
41
期数:
2023年07期
页码:
71-75
栏目:
机电一体化技术
出版日期:
2023-07-25

文章信息/Info

Title:
Rotating Machinery Fault Diagnosis Based on Particle Swarm Optimization
文章编号:
1001-2257 ( 2023 ) 07-0071-05
作者:
肖祯怀
商丘职业技术学院计算机工程学院,河南 商丘 476005
Author(s):
XIAO Zhenhuai
( College of Computer Engineering , Shangqiu Polytechnic , Shangqiu 476005 , China )
关键词:
粒子群算法; 旋转机械; 故障诊断; 小波阈值消噪算法
Keywords:
particle swarm algorithm ;  rotating machinery ;  fault diagnosis ;  wavelet threshold denoising algorithm
分类号:
TP393
文献标志码:
A
摘要:
为有效解决旋转机械故障诊断需人工干预以及诊断结果不准确等问题,提出基于粒子群算法的旋转机械故障诊断方法。首先,利用振动传感器采集故障信号,通过小波阈值消噪算法完成故障信号去噪处理,降噪后的振动信号进行 SPWVD 时频图图像纹理特征参量提取,将提取的各种参量汇总,组成故障特征集。然后,利用 SVM 及其学习法搭建旋转机械故障诊断分类器,把故障特征集录入到分类器中,通过粒子群算法对分类器中的参数进行优化处理。最后,得到输出的智能识别结果,完成旋转机械故障的诊断。实验结果表明,所提方法得到了准确率更高的旋转机械故障诊断结果。
Abstract:
In order to effectively solve the problems of manual intervention and inaccurate diagnosis resulting in rotating machinery fault diagnosis , a rotating machinery fault diagnosis method based on particle swarm optimization algorithm is proposed.Firstly , the vibration sensor is used to collect the fault signal , and the wavelet threshold denoising algorithm is used to denoise the fault signal.After noise reduction , the texture feature parameters of SPWVD time-frequency image are extracted from the vibration signal , and the extracted parameters are summarized to form a fault feature set.Secondly , by using SVM and its learning method , a rotating machine fault diagnosis classifier is built , the fault feature set is input into the classifier , and the parameters in the classifier are optimized by particle swarm optimization algorithm.Finally , the output intelligent recognition results are obtained to complete the fault diagnosis of rotating machinery.The experimental results show that the proposed method has higher accuracy in rotating machinery fault diagnosis.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2022-04-08
基金项目:河南省高等学校重点科研项目( 19B520025 , 21B880029 )
作者简介:肖祯怀 ( 1979- ),男,河南潢川人,硕士,讲师,研究方向为计算机应用、智能控制。
更新日期/Last Update: 2023-09-01