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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

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Frontiers of Mechanical Engineering Aims and scope Submit manuscript

Abstract

Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state. However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalanced training dataset. It will degrade the performance of fault diagnosis methods significantly. To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper. Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph. And the edge connections in the graph depend on the relationship between signals. On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery. Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform, and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.

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Abbreviations

AE:

Autoencoder-based extraction

AG:

Affinity graph-based GCN

CNN:

Convolutional neural network

FFT:

Fast Fourier transform

GAN:

Generative adversarial network

GCN:

Graph convolutional network

RD:

Raw data

ReLU:

Rectified linear unit

TF:

Traditional feature-based extraction

VIG:

Vibration indicator-based GCN

A :

Adjacency matrix

b, d :

Bias vectors

Cheb :

Function of Chebyshev graph convolution

D :

Degree matrix

E :

Edge set

e θ :

Encoding function of autoencoder

f k :

Frequency value of kth spectral line

F :

Feature vector

g θ′ :

Decoding function of autoencoder

G :

Undirected graph

H, F, J :

Scales of graph convolution layer

h (i) :

Output of hidden layer of autoencoder

I n :

Identity matrix

K :

Chebyshev polynomial coefficient

L :

Reconstruction error

L :

Laplacian matrix

Loss :

Loss function of autoencoder

m, M :

Number of elements in vector

N :

Number of nodes

s f, s g :

Sigmoid function of encoder and decoder, respectively

T k :

Chebyshev polynomial

u i (i = 1, 2, …, n):

Eigenvector

U :

Eigenvector matrix

V :

Node set of graph

W :

Weight matrix

W ′ :

Parameterized weight matrix

x(n):

Data point of time-domain signal x

X :

Input signal

X′:

Output of Chebyshev graph convolution layer

x (i) :

Input of encoder

y(k):

Frequency spectrum of x

y (i) :

Output of decoder

Y :

Output of filter operation in GCN

Z :

Label of samples

σ :

Activation function of ReLU

θ, θ′ :

Parameter sets of encoder

θ k :

Chebyshev coefficient

Λ :

Eigenvalue matrix of Laplacian matrix

Λ̃ :

normalized version of Λ

λ :

Eigenvalue

λ max :

Largest element of Λ

φ θ :

Filter function of GCN

ϕ AE :

Reconstruction error of autoencoder

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Acknowledgement

This work was supported by the National Key R&D Program of China (Grant No. 2020YFB1711203).

Author information

Authors and Affiliations

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

    Jie Liu

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

    Kaibo Zhou & Chaoying Yang

  3. School of Mechanical Engineering, Shandong University, Jinan, 250061, China

    Guoliang Lu

Authors
  1. Jie Liu
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  2. Kaibo Zhou
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  3. Chaoying Yang
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  4. Guoliang Lu
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Corresponding author

Correspondence to Chaoying Yang.

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Liu, J., Zhou, K., Yang, C. et al. Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning. Front. Mech. Eng. 16, 829–839 (2021). https://doi.org/10.1007/s11465-021-0652-4

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  • DOI: https://doi.org/10.1007/s11465-021-0652-4

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