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

Abstract

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.

概要

目的:故障诊断在系统可靠性增强方面具有重要作用。调节阀通常运行在恶劣的环境下,故调节阀的故障时有发生。因此一种智能、稳健的调节阀健康状态检测方法对于系统的运行至关重要。针对调节阀数据驱动模型易受工况变化和数据缺失影响的问题,本文提出一种基于数学模型估算和改进深度残差收缩网络(MDRSN)的故障诊断方法。 方法:1. 采集调节阀在不同开度的多个传感器时间序列样本。2. 使用数学模型插补模型对不完整数据集进行补足,并使用MDRSN对调节阀的不同工况进行故障诊断。3. 分析该方法在缺失数据估计和故障诊断中的准确率。 结论:本文利用调节阀的数学模型对缺失数据进行处理,并提出将MDRSN用于调节阀故障诊断。基于补全后获得的完整样本,对调节阀的故障诊断模型进行分析和训练,以提高故障诊断的准确性。结果表明,在基于数学模型插补的完整数据集上使用MDRSN的在线故障诊断效果较好。