首页|基于DBN和BES-LSSVM的矿用压风机异常状态识别方法

基于DBN和BES-LSSVM的矿用压风机异常状态识别方法

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对矿用压风机这类分布式系统的异常类别复杂、识别精度低等问题,提出了一种基于深度置信网络(DBN)和最小二乘支持向量机(LSSVM)的异常状态识别方法.首先,分析压风机组成系统及其运行机理,确定常见的异常状态类型;其次,采用DBN无监督学习方式充分挖掘监测数据中异常特征并快速提取;然后,利用秃鹰搜索算法(BES)优化LSSVM的超参数,构建最优的BES-LSSVM分类模型;最后,将DBN提取的异常特征作为BES-LSSVM模型的输入,对矿用压风机异常状态进行识别.试验验证与对比分析结果表明,相较于GA,PSO,GWO算法,BES算法的求解精度和收敛速度均有所提高,同时DBN-BES-LSSVM模型在测试集上平均识别精度达到94.65%,较PCA-LSSVM模型、DBN模型和DBN-LSSVM模型的识别精度分别提高了10.53%,5.84%和3.76%,验证了DBN-BES-LSSVM模型在矿用压风机异常特征提取以及特征识别方面的优越性.
Abnormality identification method of mining air compressor based on DBN and BES-LSSVM
For the problems of complex categories of abnormality and low recognition accuracy of distributed systems such as mining air compressors,an abnormal state recognition method based on deep belief network(DBN)and least squares support vector machine(LSSVM)was proposed.Firstly,the composition system of the air compressor and its operation mechanism were analyzed to determine the types of common abnormal states.Secondly,DBN unsupervised learning was used to fully mine the abnormal features in the monitoring data and quickly extract them.Then,the bald eagle search(BES)was used to optimize the hyperparameters of LSSVM to construct the optimal BES-LSSVM classification model.Finally,the abnormal features extracted by DBN were used as inputs to the BES-LSSVM model to identify the abnormal status of mining air compressor.The experimental verification and comparative analysis results show that compared to GA,PSO and GWO algorithms,the BES algorithm has improved solution accuracy and convergence speed.At the same time,the DBN-BES-LSSSVM model has an average recognition accuracy of 94.65%on the test set,which is 10.53%,5.84%and 3.76%higher than the PCA-LSSVM model,DBN model,and DBN-LSSVM model,respectively,which verifies the superiority of the DBN-BES-LSSVM model in extracting abnormal features and feature recognition of mining air compressor.

mining air compressordeep belief networkbald eagle search algorithmleast squares support vector machineexception recognition

李敬兆、王克定、王国锋、郑鑫、石晴

展开 >

安徽理工大学 电气与信息工程学院,安徽淮南 232001

淮南矿业集团,安徽淮南 232001

淮北合众机械设备有限公司,安徽淮北 235000

矿用压风机 深度置信网络 秃鹰搜索算法 最小二乘支持向量机 异常识别

国家自然科学基金国家自然科学基金淮北市科技重大专项淮南市科技计划物联网关键技术研究创新团队项目

5187401061170060Z20200042021A243201950ZX003

2024

10.3969/j.issn.1005-0329.2024.03.012

流体机械
中国机械工程学会

流体机械

CSTPCD北大核心
影响因子：1.418
ISSN：1005-0329
年,卷(期)：2024.52(3)
  • 21