流体机械2024，Vol.52Issue(3)：89-97,9.DOI:10.3969/j.issn.1005-0329.2024.03.012
基于DBN和BES-LSSVM的矿用压风机异常状态识别方法
Abnormality identification method of mining air compressor based on DBN and BES-LSSVM
摘要
Abstract
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.关键词
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Key words
mining air compressor/deep belief network/bald eagle search algorithm/least squares support vector machine/exception recognition分类
机械制造引用本文复制引用
李敬兆,王克定,王国锋,郑鑫,石晴..基于DBN和BES-LSSVM的矿用压风机异常状态识别方法[J].流体机械,2024,52(3):89-97,9.基金项目
国家自然科学基金项目(51874010,61170060) （51874010,61170060）
淮北市重大科技专项(Z2020004) （Z2020004）
淮南市科技计划项目(2021A243) （2021A243）
物联网关键技术研究创新团队(201950ZX003) （201950ZX003）
