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首页>《中国测试》期刊>本期导读>特征指标信息融合的电动调节阀故障诊断

特征指标信息融合的电动调节阀故障诊断

104    2019-09-28

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作者:王印松, 王珏

作者单位:华北电力大学控制与计算机工程学院, 河北 保定 071003


关键词:故障诊断;电动调节阀;D-S证据理论;神经网络;特征指标


摘要:

调节阀作为控制系统的重要组成部分,它的故障诊断对于指导控制过程保险稳定地运行至关重要。为提高故障诊断的精确率,解决电动调节阀不同故障间可能存在相互关联的问题,提出一种基于特征指标信息融合的诊断方法。利用电动调节阀可测变量间的关系,计算能够反映电动调节阀不同故障特点的指标,并建立与之对应的神经网络;然后将每个神经网络的输出看作独立的证据体进行D-S证据融合,得到最终的诊断结果。实验结果及现场分析表明:该方法充分利用数据的有效信息,从不同侧面对故障进行诊断,能够有效地应用于电动调节阀的故障诊断,具有较高的应用价值。


Electric control valve fault diagnosis method based on feature index information fusion
WANG Yinsong, WANG Jue
School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Abstract: The control valve acts as an important part of control system, and it's fault diagnosis is essential to guide the safe and stable operation of the control process. In order to improve the accuracy of fault diagnosis and solve the problem that there may be correlation between different faults of electric control valve, a fault diagnosis method based on information fusion of feature index is proposed. First, using the relationship between measurable variables of electric control valve to calculate indexes that can reflect different fault characteristic of the electric control valve, and neural network corresponding is established. Then the output of each neural network is regarded as an independent evidence body for D-S evidence fusion to obtain the final diagnosis result. Experimental results and on-site analysis show that this method makes full use of the effective information of the data and diagnoses the fault from different sides, it can be effectively applied to the fault diagnosis of electric control valve, and has high application value.
Keywords: fault diagnosis;electric control valve;D-S evidence theory;neural network;feature index
2019, 45(9):6-12  收稿日期: 2019-03-25;收到修改稿日期: 2019-04-18
基金项目: 国家自然基金联合基金项目(U1709211)
作者简介: 王印松(1967-),男,河北河间市人,教授,博士,研究方向为先进控制策略和控制系统故障诊断技术
参考文献
[1] YANG J, ZHU F, WANG X, et al. Robust sliding-mode observer-based sensor fault estimation, actuator fault detection and isolation for uncertain nonlinear systems[J]. International Journal of Control, Automation and Systems, 2015, 13(5):1037-1046
[2] 周东华, 刘洋, 何潇. 闭环系统故障诊断技术综述[J]. 自动化学报, 2013, 39(11):1933-1943
[3] GONCALVES L F, BOSA J L, BALEN T R, et al. Fault detection, diagnosis and prediction in electrical valves using self-organizing maps[J]. Journal of Electronic Testing, 2011, 27(4):551-564
[4] GARCIA O P, TIKKALA V M, ZAKHAROV A, et al. Integrated FDD system for valve stiction in a paperboard machine[J]. Control Engineering Practice, 2013, 21(6):818-828
[5] HAFAIFA A, DJEDDI A Z, DAOUDI A. Fault detection and isolation in industrial control valve based on artificial neural networks diagnosis[J]. Control engineering and applied Informatics, 2013, 15(3):61-69
[6] 郭胜辉, 朱芳来. 基于区间观测器的执行器故障检测[J]. 控制与决策, 2016, 31(6):1118-1122
[7] 黄孝彬. 火电厂控制系统故障检测与诊断的研究[D]. 保定:华北电力大学, 2004.
[8] 黄爱芹. 基于数据驱动的调节阀故障诊断方法研究[D].济南:山东大学, 2015.
[9] 刘吉臻, 高萌, 吕漫游, 等. 过程运行数据的稳态检测方法综述[J]. 仪器仪表学报, 2013, 34(8):61-70
[10] 李士哲, 王印松, 田靖雨, 等. 气动阀门粘滞模型仿真及控制系统振荡分析[J]. 计算机仿真, 2016, 33(1):239-244
[11] YAMASHITA Y. An automatic method for detection of valve stiction in process control loops[J]. Control Engineering Practice, 2006, 14(5):503-510
[12] 王印松, 田靖雨, 李士哲, 等. 基于时域指标的火电机组负荷控制系统性能模糊综合评价[J]. 热力发电, 2016, 45(10):99-103
[13] KHAMSEH S A, SEDIGH A K, MOSHIRI B, et al. Control performance assessment based on sensor fusion techniques[J]. Control Engineering Practice, 2016, 49:14-28
[14] 韩德强, 杨艺, 韩崇昭. DS证据理论研究进展及相关问题探讨[J]. 控制与决策, 2014, 29(1):1-11
[15] 张文元, 赵卫国, 晋涛, 等. 多神经网络与证据理论的变压器故障诊断方法[J]. 高压电器, 2018, 54(8):207-211