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

doi:10.1007/s11465-022-0692-4

Front. Mech. Eng.

2022, Vol. 17

Issue (3) : 36 https://doi.org/10.1007/s11465-022-0692-4

RESEARCH ARTICLE

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

Qun CHAO^1,^2,³, Haohan GAO¹, Jianfeng TAO^1,³(

), Chengliang LIU^1,³, Yuanhang WANG⁴, Jian ZHOU⁴

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

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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.

Keywords axial piston pump fault diagnosis convolutional neural network multi-sensor data fusion

Corresponding Author(s): Jianfeng TAO

About author: Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 22 April 2022 Issue Date: 09 October 2022

Cite this article:

Qun CHAO,Haohan GAO,Jianfeng TAO, et al. Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network[J]. Front. Mech. Eng., 2022, 17(3): 36.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-022-0692-4
https://academic.hep.com.cn/fme/EN/Y2022/V17/I3/36

Fig.1 Illustration of an axial piston pump and worn slippers.

Tab.1 Different degradation states of the axial piston pump

Fig.3 Raw vibration signals for four different pump degradation states: (a) normal; (b) slight leakage; (c) medium leakage; and (d) severe leakage.

Fig.4 Raw discharge pressure signals for four different pump degradation states.

Fig.5 Illustration of the proposed multi-sensor data fusion for the fault diagnosis of axial piston pumps.

Fig.6 RGB images under different pump degradation states: (a) three vibration signals; (b) vibration signals 2 and 3, and pressure signal; (c) vibration signals 1 and 3, and pressure signal; and (d) vibration signals 1 and 2, and pressure signal.

Fig.7 Grayscale images under different pump degradation states: (a) vibration signal 1; (b) vibration signal 2; (c) vibration signal 3; and (d) pressure signal.

Fig.8 Comparison of classification accuracy between single-sensor data and multi-sensor data.

Fig.9 Comparison of contaminated spectrograms among different pump degradation states at an SNR of 6 dB: (a) three vibration signals; (b) vibration signals 2 and 3, and pressure signal; (c) vibration signals 1 and 3, and pressure signal; and (d) vibration signals 1 and 2, and pressure signal.

Fig.10 Comparison of anti-noise performance between single-sensor data and multi-sensor data.

Fig.11 Comparison of confusion matrix before and after multi-sensor data fusion at an SNR of 6 dB: (a) vibration signal 1; (b) vibration signal 2; (c) vibration signal 3; (d) pressure signal; (e) three vibration signals; (f) vibration signals 2 and 3, and pressure signal; (g) vibration signals 1 and 3, and pressure signal; and (h) vibration signals 1 and 2 and pressure signal.

Tab.2 Classification accuracy of slipper faults in axial piston pumps for different methods and input signals

Abbreviations
1D	One-dimensional
2D	Two-dimensional
CNN	Convolutional neural network
SNR	Signal-to-noise ratio
STFT	Short-time Fourier transform
Variables
a_hw	Feature map element at pixel (h, w) in the pooling window
$A k l$	The kth feature map at layer l
$B k l$	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
$i ′$	Height index of element pixels
j	Imaginary unit
$j ′$	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
$X c l − 1$	The cth-channel component of the input feature map at layer (l – 1)
$X k l$	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
$W c, k l$	The cth-channel component of the kth group filter weight at layer l
η	Learning rate
$θ L$	Trainable parameters at the last layer L
θ^new, θ^old	Trainable parameters after and before update, respectively
$τ$	Time variable of integration
$ω$	Angular frequency

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