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