Acoustic signal-based fault detection of hydraulic piston pump using a particle swarm optimization enhancement CNN

Zhu Yong; Li Guangpeng; Tang ShengnanWang RuiSu HongWang Chuan

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Acoustic signal-based fault detection of hydraulic piston pump using a particle swarm optimization enhancement CNN

机译：Acoustic signal-based fault detection of hydraulic piston pump using a particle swarm optimization enhancement CNN

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摘要

As the heart of a fluid power system, hydraulic piston pumps are widely used in many critical applications, such as for marine, aerospace, and engineering equipment. The health status of a pump is important for the safety and reliability of the mechanical equipment. Hence, it is necessary to develop intelligent fault diagnosis for a hydraulic piston pump. In this research, the particle swarm optimization (PSO) algorithm is introduced to automatically select the hyperparameters of diagnosis model. A convolutional neural network (CNN) model optimized by PSO is constructed based on the standard LeNet. The PSO-LeNet model is applied to identify five common states of a hydraulic piston pump using an acoustic signal: normal state, swash plate wear, center spring failure, loose slipper, and slipper wear. Many typical deeper CNN models are compared and used for the verification of the performance of the proposed model, such as AlexNet, VGG11, VGG13, VGG16, and GoogleNet. Results indicate that the PSO-LeNet has the best stability and the highest identification accuracy. Thus, the proposed model has the laudable overall performance. (c) 2022 Published by Elsevier Ltd.

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来源
《Applied acoustics》 |2022年第4期|108718.1-108718.13|共13页
作者
Zhu Yong; Li Guangpeng; Tang ShengnanWang RuiSu HongWang Chuan;
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作者单位

Jiangsu Univ, High Tech Key Lab Agr Equipment & Intelligentizat, Zhenjiang 212013, Jiangsu, Peoples R China|GongQing Inst Sci & Technol, Int Shipping Res Inst, Jiujiang 332020, Peoples R China|Jiangsu Univ, Natl Res Ctr Pumps, Zhenjiang 212013, Jiangsu, P;

Jiangsu Univ, Natl Res Ctr Pumps, Zhenjiang 212013, Jiangsu, Peoples R China;

Jiangsu Univ, Natl Res Ctr Pumps, Zhenjiang 212013, Jiangsu, Peoples R China|Jiangsu Univ, Wenling Fluid Machinery Technol Inst, Wenling 317525, Peoples R ChinaJiangsu Univ, High Tech Key Lab Agr Equipment & Intelligentizat, Zhenjiang 212013, Jiangsu, Peoples R China|Yangzhou Univ, Coll Hydraul Sci & Engn, Yangzhou 225009, Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
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关键词
Hydraulic piston pump; Fault identification; Acoustic signal; Convolutional neural network; Particle swarm optimization;

Acoustic signal-based fault detection of hydraulic piston pump using a particle swarm optimization enhancement CNN

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