基于一维卷积神经网络的机电作动器故障诊断

doi:10.19595/j.cnki.1000-6753.tces.L90390

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Abstract To address the problem that traditional fault diagnosis methods of electromechanical actuators largely depend on artificial feature extraction and engineering experience, this paper proposes an intelligent fault diagnosis method based on one dimensional convolutional neural network (1DCNN). Compared with the separation of feature extraction and classification in the traditional fault diagnosis algorithm, the proposed method combines the two into one. Firstly, the normal signals and fault signals of direct-driven electromechanical actuators are preprocessed by overlapping sampling to acquire data samples. Subsequently, the obtained samples are fed into the designed one-dimensional convolutional neural network model, and the effective feature representation is acquired through multi-layer data transformation, thereby establishing a mapping relationship between the raw data and operating state and achieving end-to-end fault diagnosisof electromechanical actuators. The experimental results demonstrate that the proposed algorithm can effectively diagnose the fault of the electromechanical actuator, and the fault recognition accuracy can reach about 98%. In addition, the proposed method can still maintain a high fault recognition accuracy under different white noise conditions, which shows that it has good robustness and generalization performance.

Key words： Direct-drive electromechanical actuator one-dimensional convolutional neural network fault diagnosis deep learning

Received: 11 July 2020

PACS:	TM307.1
	TP206.3

Corresponding Authors: State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong University Xi’an 710049 China

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	Li Shixiao
	Du Jinhua
	Long Yun

Cite this article:

Li Shixiao,Du Jinhua,Long Yun. Fault Diagnosis of Electromechanical Actuators Based on One-Dimensional Convolutional Neural Network[J]. Transactions of China Electrotechnical Society, 2022, 37(zk1): 62-73.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L90390 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/Izk1/62

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