计算机集成制造系统 ›› 2022, Vol. 28 ›› Issue (9): 2852-2864.DOI: 10.13196/j.cims.2022.09.017

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基于深度学习的船舶机械微小故障快速诊断方法

宫文峰1,2,3,陈辉1+,WANG Danwei3   

  1. 1.武汉理工大学高性能舰船技术教育部重点实验室
    2.桂林电子科技大学北海校区海洋工程学院
    3.新加坡南洋理工大学电子与电气工程学院ST Engineering NTU联合实验室
  • 出版日期:2022-09-30 发布日期:2022-10-13
  • 基金资助:
    国家重点研发计划资助项目(2019YFE0104600);广西自然科学基金资助项目(2020GXNSFBA159058);国家自然科学基金资助项目(51579200,U1709215);中央高校基本科研业务经费专项资助武汉理工大学优秀博士学位论文培育项目(2019-YB-023);中国国家留学基金委博士联合培养资助项目(CSC201906950020)。

Fast diagnosis method of incipient fault of marine machinery based on deep learning

GONG Wenfeng1,2,3,CHEN Hui1+,WANG Danwei3   

  1. 1.Key Laboratory of High Performance Ship Technology of Ministry of Education in China,Wuhan University of Technology
    2.School of Marine Engineering,Beihai Campus,Guilin University of Electronic and Technology
    3.ST Engineering-NTU Joint Laboratory,School of Electrical and Electronic Engineering,Nanyang Technological University
  • Online:2022-09-30 Published:2022-10-13
  • Supported by:
    Project supported by the National Key Research and Development Program,China(No.2019YFE0104600),the Natural Science Foundation of Guangxi Zhuang Autonomous Region,China(No.2020GXNSFBA159058),the National Natural Science Foundation,China(No.51579200,U1709215),the Excellent Doctoral Thesis Cultivation Project of Wuhan University of Technology under the Fundamental Research Funds for the Central Universities,China(No.2019-YB-023),and the Doctoral Joint Training Program of China Scholarship Council,China(No.CSC201906950020).

摘要: 微小故障的快速诊断是预防和减少重大显著性故障发生的关键。近年来,基于卷积神经网络(CNN)的智能诊断方法已成为船舶机械领域研究的热点。然而,现行的基于图像处理框架的2D-CNN算法在处理多传感器、多通道故障数据时存在检测时间长、数据融合效率低的不足。为此,提出一种改进的1DCNN-GAP的深度学习新算法,用于船舶旋转机械的故障快速诊断。该方法首先引入1D-CNN处理多传感器数据融合问题,然后设计了一维全局均值池化层(1D-GAP)改进全连接层结构,减少传统CNN的模型参数量和诊断时间。通过将提出的方法用于滚动轴承在1马力、2马力和3马力多种负载工况下的2通道振动传感器故障监测数据进行诊断,诊断精确率分别为99.84%、99.51%和99.33%。通过与主流的SVM、KNN、DNN和2DCNN-FC算法进行对比验证,结果表明,所提方法具备更加优越的诊断性能,更适用于多传感器监测环境下微小故障的快速诊断。

关键词: 故障诊断, 一维卷积神经网络, 多通道数据融合, 船舶机械, 滚动轴承, 深度学习

Abstract: The rapid diagnosisof incipient faults is an effective approach to preventing and reducing significant faults.Intelligent fault diagnosis methods based on Convolutional Neural Networks (CNN) have become a hotspot in the field of marine machinery.However,the complexity of the current 2D-CNN algorithm is too high and the model parameters are too large,which is not suitable for the rapid diagnosis of micro faults.A novel improved 1D CNN—Global Average Pooling (1DCNN-GAP) algorithm was proposed for fast fault diagnosis,which introduced 1D-CNN to deal with multi-sensor data fusion problem.Then,a 1D (1D-GAP) layer was designed to improve the structure of the fully connected layer and reduce the model parameter quantity and diagnosis time of the existing 1D-CNN.The proposed method was used to diagnose 2-channel vibration sensor fault data from rolling bearing under the 1-horsepower,2-horsepower and 3-horsepower.The diagnostic accuracy was 99.84%,99.51% and 99.33% respectively.The proposed method was compared with the mainstream Support Vector Machine (SVM),K-Nearest Neighbor (KNN),Deep Neural Networks (DNN) and 2DCNN-FC algorithms.The experiment results confirmed that the proposed method had better diagnostic performance and was more suitable for the rapid diagnosis of incipient faults.

Key words: fault diagnosis, 1D convolutional neural network, multi-channel data fusion, marine machinery, rolling bearing, deep learning

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