基于图像识别的离心泵空化故障诊断

doi:10.3969/j.issn.1000-1158.2024.05.14

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Abstract When using the one-dimensional vibration signal of a centrifugal pump to identify the cavitation state, there are problems such as inaccurate signal feature search and the signal being greatly affected by noise. A centrifugal pump cavitation fault diagnosis method based on image recognition is proposed. The savitzky-golay convolution smoothing algorithm is used to denoise the original vibration signal, and then the denoised signal is converted into a pseudo-RGB image. A convolution kernel is designed based on the characteristics displayed in the image, and the image is convolved to obtain the features picture. Single-channel transformation is performed on the feature picture to reduce data, and finally the single-channel image is used as the input of the LeNet-5 neural network for cavitation fault diagnosis. Experimental test results show that the method can accurately identify centrifugal pump cavitation faults while accelerating model training, and the accuracy can reach 1. The study provides a new method for rapid diagnosis of centrifugal pump cavitation.

Key words： vibration measurement image recognition centrifugal pump cavitation savitzky-golay convolution smoothing algorithm fault diagnosis

Received: 20 September 2022 Published: 23 May 2024

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TB936

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	Articles by authors
	HUANG Haiming
	LIU Yan
	WU Denghao
	MOU Jiegang

Cite this article:

HUANG Haiming,LIU Yan,WU Denghao, et al. Cavitation Fault Diagnosis of Centrifugal Pump Based on Image Recognition[J]. Acta Metrologica Sinica, 2024, 45(5): 706-713.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.05.14 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I5/706

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