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

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 co...

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Bibliographic Details
Published in:	Frontiers of Mechanical Engineering Vol. 17; no. 2; p. 17
Main Authors:	WEN, Long, WANG, You, LI, Xinyu
Format:	Journal Article
Language:	English
Published:	Beijing Higher Education Press 01.06.2022
Subjects:	convolutional neural network deep reinforcement learning Engineering fault diagnosis hyper parameter optimization Intelligent Diagnosis and Maintenance Mechanical Engineering Research Article fault diagnosis deep reinforcement learning hyper parameter optimization convolutional neural network
ISSN:	2095-0233, 2095-0241
Online Access:	Get full text
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Summary:	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.
Bibliography:	Document accepted on :2022-01-10 fault diagnosis Document received on :2021-09-03 deep reinforcement learning hyper parameter optimization convolutional neural network
ISSN:	2095-0233 2095-0241
DOI:	10.1007/s11465-022-0673-7