A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Abstract

Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

Key words： deep reinforcement learning; hyper parameter optimization; convolutional neural network; fault diagnosis

Nomenclature

Abbreviations
ACNN	Automatic convolutional neural network
ANN	Artificial neural network
AVG	Average prediction accuracy
AWMS-CNN	Adaptive weighted multiscale convolutional neural network
BO	Bayesian optimization
CNN	Convolutional neural network
DAN	Domain adaption network
DDPG	Deep deterministic policy gradient
DL	Deep learning
DRL	Deep reinforcement learning
ELM	Extreme learning machine
FC	Fully connected
GS	Grid search
HCNN	Hierarchical convolutional neural network
HPO	Hyper parameter optimization
ICN	CNN based on a capsule network with an inception block
IF	Inner race fault
LSTM	Long short-term memory
ML	Machine learning
MSCNN	Multiscale convolutional neural network
NAS	Neural architecture search
OF	Outer race fault
RF	Roller fault
RL	Reinforcement learning
RS	Random search
SMAC	Sequential model-based algorithm configuration
SVM	Support vector machine
TPE	Tree Parzen estimator
VI-CNN	CNN based on vibration image
WDCNN	Deep convolutional neural networks with wide first-layer kernels
WMSCCN	Wide convolution and multiscale convolution
Variables
a	Action of DDPG algorithm
at	Current action
bt	Current batch size
bmax, bmin	The upper and lower boundaries for batch size
E_{\mathrm{\Pi }}	Expected value under policy \mathrm{\Pi }
f	Fourier frequency in short-time Fourier transform
lt	Current L2-regulation value
lmax, lmin	The upper and lower boundaries for L2-regulation value
lrmax, lrmin	The upper and lower boundaries for learning rate, respectivley
lrt	Current learning rate
losst	Loss value of the CNN model at time step t
L	Training loss of critic network
M	Number of the sequencing training loss
n	Number of samples in the experience storage D
p	Transition probability function
Q^{\mathrm{\Pi }} (s, a)	Q-value function under policy \mathrm{\Pi }
r	Reward of DDPG algorithm
s	State of DDPG algorithm
st	State at time step t
STFT	Short-time Fourier transform formulation
t	Time step
w(t)	Window function
yt	Actual Q-value
α	Factor to control the degree of soft updating
γ	Discount factor
\mathrm{\Pi }	Policy of the agent to choose the action
{\theta }^{\mu }	Online actor network
{\theta }^{{\mu }^{\prime }}	Target actor network
{\omega }^Q	Online critic network
$$\begin{gathered} {\omega }^{Q^{\prime }} \\ \end{gathered}$$	Target critic network

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 51805192 and U21B2029), the Major Special Science and Technology Project of Hubei Province, China (Grant No. 2020A EA009), and the State Key Laboratory of Digital Manufacturing Equipment and Technology of Huazhong University of Science and Technology, China (Grant No. DMETKF2020029).

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