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Deep graph feature learning-based diagnosis approach for rotating machinery using multi-sensor data

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Abstract

It is necessary to monitor and evaluate health state of rotating machinery, which directly affects the quality and productivity of manufacturing processes. At present, most of the existing fault diagnosis methods focus on analyzing the single sensor data. There are several problems: (1) single sensor data which only contain partial fault information are used to construct the graph, limiting the diagnosis performance; (2) the extracted traditional features containing shallow fault information have limited application scenarios. Recently, graph feature learning-based diagnosis approach shows its powerful feature learning ability, which can overcome the limitations of shallow feature extraction. In this paper, a deep graph feature learning-based diagnosis approach for rotating machinery using multi-sensor data is proposed. Singular values extracted from samples consisting of multi-sensor vibration signals are regarded as the sample node representation to construct the graph data. On the basis, a graph convolutional network is used to extract high-level features from graph data and achieve feature fusion for fault classification. Effectiveness of the proposed approach is verified on a practical experimental platform considering different working conditions (motor loads), and the results shows that it can perform well even in small training dataset.

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Abbreviations

FC:

Fully connected layer-based feature extraction

GCN:

Graph convolutional network

Raw:

Raw signal-based feature extraction

SVD:

Singular values decomposition

TF:

Time–frequency statistics-based feature extraction

A :

Adjacency matrix

Cheb :

Function of Chebyshev graph convolution

D :

Degree matrix

E :

Edge set

F :

Feature vector

G :

Undirected graph

g θ :

Filter of graph convolution

H :

Hankel matrix

H, I, J :

Scales of graph convolution layer

h :

Row number of hankel matrix

I n :

Identity matrix

K :

Chebyshev polynomial coefficient

L :

Laplacian matrix

m :

Number of elements in vector

N :

Number of nodes

P :

Node or an object

T :

Feature matrix of graph

T k :

Chebyshev polynomial

u :

Left singular vector

U :

Eigenvector matrix

V :

Node set of graph

v :

Right singular vector

W :

Weight matrix

X :

Original signal

X norm :

Normalized signal

X filter :

Output of filter

X GCN :

Output of Chebyshev graph convolution layer

Z :

Label of samples

σ :

Singular value

θ k :

Chebyshev coefficient

Λ :

Eigenvalue matrix of Laplacian matrix

\({\boldsymbol{\tilde{\Lambda}}}\) :

Normalized version of Λ

λ :

Eigenvalue

λ max :

Largest element of Λ

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1711201.

Funding

Jie Liu National Key Research and Development Program of China, 2020YFB1711201.

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Authors and Affiliations

  1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Kaibo Zhou, Chaoying Yang & Qi Xu

  2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jie Liu

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  1. Kaibo Zhou
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  2. Chaoying Yang
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  3. Jie Liu
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  4. Qi Xu
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Correspondence to Jie Liu.

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Zhou, K., Yang, C., Liu, J. et al. Deep graph feature learning-based diagnosis approach for rotating machinery using multi-sensor data. J Intell Manuf 34, 1965–1974 (2023). https://doi.org/10.1007/s10845-021-01884-y

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