基于补偿距离评估和一维卷积神经网络的离心泵故障快速智能识别方法

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Abstract

1D convolution neural network (1DCNN) can be applied to the identification and classification of one-dimensional signals, such as vibration signal, etc.But, the recognition accuracy and recognition speed with small sample training are crucial problems to be solved, when it is directly applied to mechanical fault diagnosis.To solve these problems, a fast and intelligent fault identification method based on the compensation distance evaluation technique (CDET) and 1DCNN was proposed.First, based on the centrifugal pump vibration analysis and fault diagnosis theory, a full-featured set in mixed domains was constructed by extracting time-domain, frequency-domain, energy, and entropy features.The full-featured set can fully mine the fault information in the training samples, improve the utilization rate of a single training sample and let the fault recognition model be able to train by small sample.Then, the full-featured set was reduced in dimension and optimized through the CDET, while effectively retaining the fault feature information, so that the feature construction and fault identification model was provided with the ability of quick calculation.Finally, the DCNN was trained by the reduced dimension features of the training samples, and then the fault intelligent identification model was constructed for the fault analysis of the centrifugal pump.Through two fault cases of a petrochemical centrifugal pump, i.e., evacuation and rolling bearing damage, it is proved that the identification accuracy of the method is more than 98% under the condition of small sample training, and the recognition time of a single group of data is less than 3 s, which can meet the needs of real-time intelligent identification of faults of the centrifugal pump in engineering.

Key words： centrifugal pump fault diagnosis vibration convolutional neural network (CNN) small sample

Received: 02 March 2020 Published: 28 May 2021

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	Articles by authors
	JIAO Hanhui1
	HU Minghui1
	JIANG Zhinong1
	FENG Kun2

Cite this article:

JIAO Hanhui1,HU Minghui1,JIANG Zhinong1, et al. Fast and intelligent identification method for faults of a centrifugal pump based on the compensation distance evaluation and one-dimensional convolution neural network[J]. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(10): 41-49.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I10/41

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