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

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.