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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.

数据驱动的基于数学模型插补和改进深度残差收缩网络的调节阀状态监控

[1]DesboroughLD, MillerRM, 2001. Increasing customer value of industrial control performance monitoring—Honeywell’s experience. Proceedings of the Chemical Process Control–VI AIChE Symposium Series Tuscon Arizona, p.98.

[2]DuJH, HuMH, ZhangWN, 2020. Missing data problem in the monitoring system: a review. IEEE Sensors Journal, 20(23):13984-13998.

[3]DuttaN, PalanisamyK, SubramaniamU, et al., 2020. Identification of water hammering for centrifugal pump drive systems. Applied Sciences, 10(8):2683.

[4]FangL, TangL, WangJD, et al., 2016. A semi-physical model for pneumatic control valves. Nonlinear Dynamics, 85(3):1735-1748.

[5]GuoC, HuWK, YangF, et al., 2020. Deep learning technique for process fault detection and diagnosis in the presence of incomplete data. Chinese Journal of Chemical Engineering, 28(9):2358-2367.

[6]HeX, WangZD, WangXF, et al., 2014. Networked strong tracking filtering with multiple packet dropouts: algorithms and applications. IEEE Transactions on Industrial Electronics, 61(3):1454-1463.

[7]JardineAKS, LinDM, BanjevicD, 2006. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7):1483-1510.

[8]KayihanA, DoyleIII FJ, 2000. Friction compensation for a process control valve. Control Engineering Practice, 8(7):799-812.

[9]KimYS, KimDW, LeeBO, et al., 2016. Experimental study of operating parameters for pneumatic control valve in abnormal conditions. Transactions of the Korean Society of Mechanical Engineers A, 40(6):613-619.

[10]LeiYG, YangB, JiangXW, et al., 2020. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mechanical Systems and Signal Processing, 138:106587.

[11]LiuH, WangYY, ChenWG, 2020. Three-step imputation of missing values in condition monitoring datasets. IET Generation, Transmission & Distribution, 14(16):‍3288-3300.

[12]Llanes-SantiagoO, Rivero-BenedicoBC, Galvez-VieraSC, et al., 2018. A fault diagnosis proposal with online imputation to incomplete observations in industrial plants. Revista Mexicana de Ingenieria Quimica, 18(1):83-98. https://doi.‍org/10.24275/uam/izt/dcbi/revmexingquim/2019v18n1/Llanes

[13]LvQ, YuXL, MaHH, et al., 2021. Applications of machine learning to reciprocating compressor fault diagnosis: a review. Processes, 9(6):909.

[14]Razavi-FarR, Farajzadeh-ZanjaniM, SaifM, et al., 2020. Correlation clustering imputation for diagnosing attacks and faults with missing power grid data. IEEE Transactions on Smart Grid, 11(2):1453-1464.

[15]SharifKM, RahmanMM, AzmirJ, et al., 2014. Experimental design of supercritical fluid extraction–a review. Journal of Food Engineering, 124:105-116.

[16]SheesleyJH, 1990. Quality engineering in production systems. Technometrics, 32(4):457-458.

[17]SoleimaniM, CampeanF, NeaguD, 2021. Diagnostics and prognostics for complex systems: a review of methods and challenges. Quality and Reliability Engineering International, 37(8):3746-3778.

[18]TripathyAK, NambiarP, PereiraA, et al., 2015. Pressure surge analysis in pump systems. Proceedings of the International Conference on Technologies for Sustainable Development, p.1-5.

[19]XieG, SunLL, WenT, et al., 2021. Adaptive transition probability matrix-based parallel IMM algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(5):2980-2989.

[20]YangJ, XieG, YangYX, 2020. An improved ensemble fusion autoencoder model for fault diagnosis from imbalanced and incomplete data. Control Engineering Practice, 98:104358.

[21]YuanY, MaGJ, ChengC, et al., 2020. A general end-to-end diagnosis framework for manufacturing systems. National Science Review, 7(2):418-429.

[22]ZhaoMH, ZhongSS, FuXY, et al., 2020. Deep residual shrinkage networks for fault diagnosis. IEEE Transactions on Industrial Informatics, 16(7):4681-4690.

[23]ZhuJL, GeZQ, SongZH, et al., 2018. Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data. Annual Reviews in Control, 46:107-133.