1. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 2. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China 3. MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China 4. China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou 510610, China
Axial piston pumps have wide applications in hydraulic systems for power transmission. Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system. Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions. However, most of the previous fault diagnosis methods only used vibration or pressure signal, and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited. This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps. The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network. Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method. Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.
Nine vibration signals + one discharge flow signal
84.1
EWT and variance contribution rate [19]
Three vibration signals
66.5
Proposed method
Two vibration signals + one discharge pressure signal
100.0
Tab.2
Abbreviations
1D
One-dimensional
2D
Two-dimensional
CNN
Convolutional neural network
SNR
Signal-to-noise ratio
STFT
Short-time Fourier transform
Variables
ahw
Feature map element at pixel (h, w) in the pooling window
The kth feature map at layer l
Bias of the kth group filter at layer l
c
Index of channels for input feature maps or the group filters
C
Total number of filter channels
f(·)
Activation function
H
Pooling window height
Height index of element pixels
j
Imaginary unit
Width index of element pixels
J
Loss function
k
Index of group filters or output feature maps
l
Index of network layers
L
Total layer number
m, n
Indices of discrete sampling points
N
Size of Hanning window
Maximum element in the pooling window
q
The qth class
Q
Total classification number
s
Index of samples
S
Total number of samples
t
Time
x(τ)
Vibration signal
x(s)
The sth sample
The cth-channel component of the input feature map at layer (l – 1)
The kth output feature map at layer l
y(s)
Predicted label
w(τ), w*(τ)
Window function and its conjugated form
W
Pooling window width
The cth-channel component of the kth group filter weight at layer l
η
Learning rate
Trainable parameters at the last layer L
θnew, θold
Trainable parameters after and before update, respectively
Time variable of integration
Angular frequency
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