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On-line Access: 2022-05-10

Received: 2021-12-04

Revision Accepted: 2021-12-27

Crosschecked: 2022-05-11

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Journal of Zhejiang University SCIENCE A 2022 Vol.23 No.4 P.303-313


Data-driven fault diagnosis of control valve with missing data based on modeling and deep residual shrinkage network

Author(s):  Feng SUN, He XU, Yu-han ZHAO, Yu-dong ZHANG

Affiliation(s):  College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China

Corresponding email(s):   railway_dragon@sohu.com

Key Words:  Control valve, Missing data, Fault diagnosis, Mathematical model (MM), Deep residual shrinkage network (DRSN)

Feng SUN, He XU, Yu-han ZHAO, Yu-dong ZHANG. Data-driven fault diagnosis of control valve with missing data based on modeling and deep residual shrinkage network[J]. Journal of Zhejiang University Science A, 2022, 23(4): 303-313.

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author="Feng SUN, He XU, Yu-han ZHAO, Yu-dong ZHANG",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Data-driven fault diagnosis of control valve with missing data based on modeling and deep residual shrinkage network
%A Feng SUN
%A He XU
%A Yu-han ZHAO
%A Yu-dong ZHANG
%J Journal of Zhejiang University SCIENCE A
%V 23
%N 4
%P 303-313
%@ 1673-565X
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2100598

T1 - Data-driven fault diagnosis of control valve with missing data based on modeling and deep residual shrinkage network
A1 - Feng SUN
A1 - He XU
A1 - Yu-han ZHAO
A1 - Yu-dong ZHANG
J0 - Journal of Zhejiang University Science A
VL - 23
IS - 4
SP - 303
EP - 313
%@ 1673-565X
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2100598

A control valve is one of the most widely used machines in hydraulic systems. However, it often works in harsh environments and failure occurs from time to time. An intelligent and robust control valve fault diagnosis is therefore important for operation of the system. In this study, a fault diagnosis based on the mathematical model (MM) imputation and the modified deep residual shrinkage network (MDRSN) is proposed to solve the problem that data-driven models for control valves are susceptible to changing operating conditions and missing data. The multiple fault time-series samples of the control valve at different openings are collected for fault diagnosis to verify the effectiveness of the proposed method. The effects of the proposed method in missing data imputation and fault diagnosis are analyzed. Compared with random and k-nearest neighbor (KNN) imputation, the accuracies of MM-based imputation are improved by 17.87% and 21.18%, in the circumstances of a 20.00% data missing rate at valve opening from 10% to 28%. Furthermore, the results show that the proposed MDRSN can maintain high fault diagnosis accuracy with missing data.




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