摘要
将卷积神经网络(CNN)应用于振动信号分析时,往往会出现由于一维信号转化为二维特征导致的计算量巨大的问题,针对这一问题,对卷积神经网络输入构造及不同构造方式对神经网络性能的影响进行了研究。基于机泵振动信号分析特点,提出了一种新的将一维振动信号转换为二维的特征快速构造方法;基于特征快速构造方法和卷积神经网络,构建了机泵故障智能识别模型;利用某石化现场轴承故障和不平衡故障数据对故障模型进行了测试,并与其他信号转化方法及故障识别模型进行了对比。研究结果表明:不同故障类型模型均可以快速收敛,故障识别准确率均达95%以上;在故障识别准确率和训练效率方面,该模型较其他模型有着较显著的优势。
Aiming at the problems of the huge amount of computation due to the conversion of one-dimensional signal into two-dimensional characteristics,when the convolutional neural network(CNN)is applied to the vibration signal analysis,the influence of input construction and different construction methods of convolution neural network on the performance of neural network was studied.Based on the characteristics of the pump vibration signal analysis,a new fast construction method was proposed to convert one-dimensional vibration signals into two-dimensional features.Based on the feature fast construction method and the principle of CNN,an intelligent recognition model of the pump failure was constructed.Using the data of bearing fault and unbalance fault in a petrochemical field,the fault model was tested and compared with other signal conversion methods and fault recognition models.The results indicate that they can quickly converge,and the fault identification accuracy rate is more than 95%.The model has significant advantages over other models in the accuracy of fault recognition and training efficiency.
作者
焦瀚晖
胡明辉
王星
冯坤
石保虎
JIAO Han-hui;HU Ming-hui;WANG Xing;FENG Kun;SHI Bao-hu(Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education,Beijing University of Chemical Technology,Beijing 100029,China;Beijing Key Laboratory of High-end Mechanical Equipment Health Monitoring and Self-Recovery,Beijing University of Chemical Technology,Beijing 100029,China;SINOPEC Marketing South China Company,Guangzhou 510180,China)
出处
《机电工程》
CAS
北大核心
2020年第9期1063-1068,共6页
Journal of Mechanical & Electrical Engineering
基金
博士后创新人才支持计划资助项目(BX20180031)
NSFC-辽宁联合基金项目(U1708257)
中央高校基本科研业务费专项资金资助项目(JD1913)。
关键词
卷积神经网络
特征快速构造
振动信号分析
故障诊断
机泵故障
convolutional neural network(CNN)
feature fast construction
vibration signal analysis
fault diagnosis
pump failure
