Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Abstract

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.

Key words： axial piston pump; fault diagnosis; convolutional neural network; multi-sensor data fusion

Nomenclature

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
$A_k^l$	The kth feature map at layer l
${\mathit{B}}_k^l$	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
$i^{\prime }$	Height index of element pixels
j	Imaginary unit
$j^{\prime }$	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
$p_k^{l+1}$	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
${\mathit{X}}_c^{l-1}$	The cth-channel component of the input feature map at layer (l – 1)
${\mathit{X}}_k^l$	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
${\mathit{W}}_{c,k}^l$	The cth-channel component of the kth group filter weight at layer l
η	Learning rate
${\mathit{\theta }}^L$	Trainable parameters at the last layer L
θnew, θold	Trainable parameters after and before update, respectively
$\tau$	Time variable of integration
$\omega$	Angular frequency

Acknowledgements

This study was supported by the National Key R&D Program of China (Grant No. 2018YFB1702503), the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems, China (Grant No. GZKF-202108), the National Postdoctoral Program for Innovative Talents, China (Grant No. BX20200210), the China Postdoctoral Science Foundation (Grant No. 2019M660086), and Shanghai Municipal Science and Technology Major Project, China (Grant No. 2021SHZDZX0102).

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