Open AccessMulti-input Convolutional Neural Network Fault Diagnosis Algorithm Based on the Hydraulic Pump
Author(s) -
Lishan Zhang,
Lei Han,
Yuzhen Meng,
Wenkui Zhao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2095/1/012069
Subject(s) - softmax function , convolutional neural network , computer science , convolution (computer science) , fault (geology) , artificial neural network , algorithm , feature (linguistics) , pattern recognition (psychology) , artificial intelligence , vibration , feature extraction , pooling , function (biology) , control theory (sociology) , acoustics , physics , linguistics , philosophy , control (management) , evolutionary biology , seismology , biology , geology
Convolutional neural network used in fault diagnosis can effectively extract fault features in vibration signals. However, in the feature extraction of mechanical fault diagnosis, usually more than two feature signals including at least axial and radial vibration signals can be extracted. This paper proposes two multi-input convolutional neural network models based on the fault data of the aircraft hydraulic pump including axial and radial vibration. The first is the Independent Input Multi-input Convolutional Neural Network model. The two inputs are respectively used for convolution pooling operation with CNN, and are combined through the concatenate function before the fully connected layer, and then all frames are integrated and flattened by the flatten function. A one-dimensional array, finally enters the fully connected layer and outputs the result through the softmax function. The second is the Combined Input Multiinput Convolutional Neural Network, that is, combine two one-dimensional signals into a twodimensional signal in the input layer of the convolutional neural network and then perform convolution pooling, and finally output the result through the softmax function. The results show that the two models have good accuracy and stability, and the second one has a higher convergence and fitting efficiency than the first one.