Introduction

International Standard Serial Number:

ISSN 1001-4551

Sponsor:

Zhejiang University;

Zhejiang Machinery and Electrical Group

Edited by:

Editorial of Journal of Mechanical & Electrical Engineering

Chief Editor:

ZHAO Qun

Vice Chief Editor:

TANG ren-zhong,

LUO Xiang-yang

Tel:

86-571-87041360,87239525

Fax:

86-571-87239571

Add:

No.9 Gaoguannong,Daxue Road,Hangzhou,China

P.C:

310009

E-mail:

meem_contribute@163.com

Anomaly detection of centrifugal pump timing data based on generative adversarial network
Published:2023-12-26 author:LI Sihan, HUANG Qian, FU Qiang, et al. Browse: 973 Check PDF documents
Anomaly detection of centrifugal pump timing data based on generative 
adversarial network


LI Sihan1,3, HUANG Qian2,3, FU Qiang1,3, ZHANG Xinyu1,3, LI Yunpeng1,3


(1.Fluid Mechanical Engineering Technology Research Center, Jiangsu University, Zhenjiang 212001, China; 2.China Nuclear 

Power Engineering Corporation Limited, Beijing 100840, China; 3.Joint Laboratory of Intelligent Diagnosis Operation and 

Maintenance of Nuclear Electric Pump and Device, Zhenjiang 212013, China)


Abstract:  Aiming at the problem that there is abnormal data during the process of collecting data from centrifugal pumps. The causes of abnormal data in the process of collecting data from centrifugal pumps, the optimization of the generative adversarial network (GAN) and the method of abnormal data detection were studied. A method for anomaly detection of centrifugal pump timing data using generative adversarial networks had been proposed (this method could optimize generative adversarial networks to solve the problem of gradient vanishing). Firstly, the basic model in the framework of the GAN was established by using the long shortterm memory neural network, and the temporal correlation of the capture data distribution was enhanced, and the problem of gradient disappearance was solved by using the Wasserstein distance method. Then, a centrifugal pump abnormal data detection experiment bench was built to collect data during the operation of the centrifugal pump and the reasons for the abnormal data were analyzed. Finally, the generator and discriminator of the GAN were trained based on normal data, and the loss score was constructed as a threshold to detect abnormal data by using reconstruction loss and discrimination loss. The research results show that the performance of the GAN in data anomaly detection is better than other unsupervised learning anomaly data detection algorithms such as isolated forest, auto-encoder (AE), K-Means. The GAN can detect abnormal data of centrifugal pump with an accuracy rate of 89.5%, this method can effectively detect abnormal timing data of centrifugal pumps, achieving the goal of optimizing the database and improving the accuracy of rotating machinery fault diagnosis. In conclusion, the study provides a novel approach to detecting abnormal data during the process of collecting centrifugal pump data.

Key words:  centrifugal pump timing data; generative adversarial networks(GAN); data anomaly detection; unsupervised learning; long shortterm memory network(LSTM); Wasserstein distance method

Advanced Search
Online Office System
  • Chinese Core Periodicals
  • Chinese Sci-tech Core Periodicals
  • SA, INSPEC Indexed
  • CSA: T Indexed
  • UPD:Indexed


2010 Zhejiang Information Institute of Mechinery Industry

Technical Support：Hangzhou Bory science and technology

You are 1895221 visit this site