Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples
Xin ZHANG1, Tao HUANG1, Bo WU1, Youmin HU1(), Shuai HUANG1, Quan ZHOU2, Xi ZHANG1
1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China 2. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when processing high-dimensional samples. Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samples. First, several 1D DCNN models with different activation functions are trained through dimension reduction learning to obtain different fault features from high-dimensional samples. Second, the obtained features are constructed into 2D images with multiple channels through a conversion method. The integrated 2D feature images can effectively represent the fault characteristic contained in raw high-dimension vibration signals. Lastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features from the 2D images and identify the fault mode of the mechanical equipment by adopting a softmax classifier. The proposed method, which is validated using the bearing public dataset of Case Western Reserve University, USA and a one-stage reduction gearbox dataset, has high recognition accuracy. Compared with other classical deep learning methods, the proposed fault diagnosis method has considerable improvements.
Inner race fault state of the bearing in case study 1
MMEDL
Multi-model ensemble deep learning
N
Normal state of the bearing in case study 1
OR
Outer race fault state of the bearing in case study 1
pool
Max pooling layer
RE
Rolling element fault state of the bearing in case study 1
SVM
Support vector machine
t-SNE
t-distributed stochastic neighbor embedding
b
bias of the neurons in Eq. (1)
dk
Length of the trainable filters in Eq. (1)
k
Trainable filters in Eq. (1)
Lc
Length of the gear crack in case study 2
Rc
Radius of the root circle of the main driving wheel in case study 2
rh
Radius of the center hole of the main driving wheel in case study 2
x
Input of the convolution layer in Eq. (1)
s
Nonlinear activation functions in Eq. (1)
1
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