Research Papers

Full- and Reduced-Order Fault Detection Filter Design With Application in Flow Transmission Lines

[+] Author and Article Information
Saeed Salavati

Department of Mechanical Engineering,
University of Houston,
Houston, TX 77004
e-mail: ssalavatidezfuli@uh.edu

Karolos Grigoriadis, Matthew Franchek

Department of Mechanical Engineering,
University of Houston,
Houston, TX 77004

Reza Tafreshi

Department of Mechanical Engineering,
Texas A&M University at Qatar,
Doha, Qatar

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received March 7, 2018; final manuscript received August 30, 2018; published online October 10, 2018. Assoc. Editor: Shankar Coimbatore Subramanian.

J. Dyn. Sys., Meas., Control 141(2), 021010 (Oct 10, 2018) (12 pages) Paper No: DS-18-1114; doi: 10.1115/1.4041383 History: Received March 07, 2018; Revised August 30, 2018

The full- and reduced-order fault detection filter design is examined for fault diagnosis in linear time-invariant (LTI) systems in the presence of noise and disturbances. The fault detection filter design problem is formulated as an H problem using a linear fractional transformation (LFT) framework and the solution is based on the bounded real lemma (BRL). Necessary and sufficient conditions for the existence of the fault detection filter are presented in the form of linear matrix inequalities (LMIs) resulting in a convex problem for the full-order filter design and a rank-constrained nonconvex problem for the reduced-order filter design. By minimizing the sensitivity of the filter residuals to noise and disturbances, the fault detection objective is fulfilled. A reference model can be incorporated in the design in order to shape the desired performance of the fault detection filter. The proposed fault detection and isolation (FDI) framework is applied to detect instrumentation and sensor faults in fluid transmission and pipeline systems. To this end, a lumped parameter framework for modeling infinite-dimensional fluid transient systems is utilized and a low-order model is obtained to pursue the instrumentation fault diagnosis objective. Full- and reduced-order filters are designed for sensor FDI. Simulations are conducted to assess the effectiveness of the proposed fault detection approach.

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Frank, P. M. , and Ding, X. , 1997, “Survey of Robust Residual Generation and Evaluation Methods in Observer-Based Fault Detection Systems,” J. Process Control, 7(6), pp. 403–424. [CrossRef]
Gertler, J., 1998, Fault Detection and Diagnosis in Engineering Systems, CRC Press, New York.
Simani, S. , Fantuzzi, C. , and Patton, R. J. , 2003, Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques, Springer-Verlag, London, UK.
Hwang, I. , Kim, S. , Kim, Y. , and Seah, C. E. , 2010, “A Survey of Fault Detection, Isolation, and Reconfiguration Methods,” IEEE Trans. Control Syst. Technol., 18(3), pp. 636–653. [CrossRef]
Gao, Z. , Cecati, C. , and Ding, S. X. , 2015, “A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches,” IEEE Trans. Ind. Electron., 62(6), pp. 3757–3767. [CrossRef]
Varga, A., 2017, Solving Fault Diagnosis Problems: Linear Synthesis Techniques, Springer International Publishing, New York.
Venkatasubramanian, V. , Rengaswamy, R. , Yin, K. , and Kavuri, S. N. , 2003, “A Review of Process Fault Detection and Diagnosis—Part I: Quantitative Model-Based Methods,” Comput. Chem. Eng., 27(3), pp. 293–311. [CrossRef]
Simani, S. , Fantuzzi, C. , and Beghelli, S. , 2000, “Diagnosis Techniques for Sensor Faults of Industrial Processes,” IEEE Trans. Control Syst. Technol., 8(5), pp. 848–855. [CrossRef]
Qin, S. J. , and Li, W. , 2001, “Detection and Identification of Faulty Sensors in Dynamic Processes,” AIChE J., 47(7), pp. 1581–1593. [CrossRef]
Van Eykeren, L. , and Chu, Q. P. , 2014, “Sensor Fault Detection and Isolation for Aircraft Control Systems by Kinematic Relations,” Control Eng. Pract., 31, pp. 200–210. [CrossRef]
Davoodi, M. , Meskin, N. , and Khorasani, K. , 2018, “A Single Dynamic Observer-Based Module for Design of Simultaneous Fault Detection, Isolation and Tracking Control Scheme,” Int. J. Control, 91(3), pp. 508–523. [CrossRef]
Kim, J. , and Lee, H. , 2011, “Sensor Fault Detection and Isolation Algorithm for a Continuous Damping Control System,” Proc. Inst. Mech. Eng., Part D, 225(10), pp. 1347–1364. [CrossRef]
Christophe, C. , Cocquempot, V. , and Jiang, B. , 2004, “Link Between High-Gain Observer-Based and Parity Space Residuals for FDI,” Trans. Inst. Meas. Control, 26(4), pp. 325–337. [CrossRef]
Stoustrup, J. , Grimble, M. J. , and Niemann, H. , 1997, “Design of Integrated Systems for the Control and Detection of Actuator Sensor Faults,” Sensor Rev., 17(2), pp. 138–149. [CrossRef]
Abdalla, M. , Nobrega, E. , and Grigoriadis, K. , 2008, “ LMI-Based Filter Design for Fault Detection and Isolation Using a Reference Model,” Eng. Sci., 35(1), pp. 35–43. https://journals.ju.edu.jo/DirasatEng/article/view/702/700
Duan, G.-R., and Yu, H.-H., 2013, LMIs in Control Systems: Analysis, Design and Applications, CRC Press, New York.
Nobrega, E. G. , Abdalla, M. O. , and Grigoriadis, K. M. , 2008, “Robust Fault Estimation of Uncertain Systems Using an LMI-Based Approach,” Int. J. Robust Nonlinear Control, 18(18), pp. 1657–1680. [CrossRef]
Zhong, M. , Ding, S. X. , Lam, J. , and Wang, H. , 2003, “An LMI Approach to Design Robust Fault Detection Filter for Uncertain LTI Systems,” Automatica, 39(3), pp. 543–550. [CrossRef]
Guo, J. , Huang, X. , and Cui, Y. , 2009, “Design and Analysis of Robust Fault Detection Filter Using LMI Tools,” Comput. Math. Appl., 57(11–12), pp. 1743–1747. [CrossRef]
Vandenberge, L. , and Boyd, S. , 1996, “ Semi-Definite Programming,” SIAM Rev., 38(1), pp. 49–95. [CrossRef]
Mohamed, N. , Jawhar, I. , Al-Jaroodi, J. , and Zhang, L. , 2011, “Sensor Network Architectures for Monitoring Underwater Pipelines,” Sensors, 11(11), pp. 10738–10764. [CrossRef] [PubMed]
Ruiz-Cárcel, C. , Cao, Y. , Mba, D. , Lao, L. , and Samuel, R. T. , 2015, “Statistical Process Monitoring of a Multiphase Flow Facility,” Control Eng. Pract., 42, pp. 74–88. [CrossRef]
Bouzid, S. , and Ramdani, M. , 2013, “Sensor Fault Detection and Diagnosis in Drinking Water Distribution Networks,” Eighth International Workshop on Systems, Signal Processing and Their Applications (WoSSPA), pp. 378–383.
Goodson, R. E. , and Leonard, R. G. , 1972, “A Survey of Modeling Techniques for Fluid Line Transients,” ASME J. Basic Eng., 94(2), pp. 474–482. [CrossRef]
Hullender, D. A. , 2016, “Alternative Approach for Modeling Transients in Smooth Pipe With Low Turbulent Flow,” ASME J. Fluids Eng., 138(12), p. 121202. [CrossRef]
Matko, D. , and Geiger, G. , 2002, “Models of Pipelines in Transient Mode,” Math. Comput. Modell. Dyn. Syst., 8(1), pp. 117–136. https://www.tandfonline.com/doi/abs/10.1076/mcmd.
Stecki, J. S. , and Davis, D. C. , 1986, “Fluid Transmission Lines-Distributed Parameter Models—Part 1: A Review of the State of the Art,” Proc. Inst. Mech. Eng., Part A, 200(4), pp. 215–228. [CrossRef]
Skelton, R. E., Iwasaki, T., and Grigoriadis, K. M., 1998, “A Unified Algebraic Approach to Linear Control Design,” Taylor and Francis, New York.
Grigoriadis, K. M., and Beran, E. B., 2000, “Alternating Projection Algorithms for Linear Matrix Inequality Problems With Rank Constraints,” Advances in Linear Matrix Inequality Approach to Control, L. El Ghaoui and S.-I. Niculescu, eds., SIAM, Philadelphia, PA, pp. 251–267.
Fazel, M. , Hindi, H. , and Boyd, S. P. , 2001, “A Rank Minimization Heuristic With Application to Minimum Order System Approximation,” American Control Conference (ACC), Arlington, VA, June 25–27, pp. 4734–4739.
Grigoriadis, K. M. , and Watson, J. T. , 1997, “ Reduced-Order H ∞ and L 2 − L ∞ Filtering Via Linear Matrix Inequalities,” IEEE Trans. Aerosp. Electron. Syst., 33(4), pp. 1326–1338. [CrossRef]
Van Schothorst, G. , 1997, “Modelling of Long-Stroke Hydraulic Servo-Systems for Flight Simulator Motion Control and System Design,” Ph.D. thesis, Technical University of Delft, Delft, The Netherlands.
Chaudhry, M. H., 2014, Applied Hydraulic Transients, 3rd ed. Springer, New York.
Mäkinen, J. , Piché, R. , and Ellman, A. , 1998, “Fluid Transmission Line Modeling Using a Variational Method,” ASME J. Dyn. Syst. Meas. Control, 122(1), pp. 153–162. [CrossRef]
Soumelidis, M. I. , Johnston, D. N. , Edge, K. A. , and Tilley, D. G. , 2005, “A Comparative Study of Modelling Techniques for Laminar Flow Transients in Hydraulic Pipelines,” Sixth JFPS International Symposium on Fluid Power, pp. 100–105.
Meziou, A. , Chaari, M. , Franchek, M. , Borji, R. , Grigoriadis, K. , and Tafreshi, R. , 2016, “ Low-Dimensional Modeling of Transient Two-Phase Flow in Pipelines,” ASME J. Dyn. Syst. Meas. Control, 138(10), p. 101008. [CrossRef]
Petalas, N. , and Aziz, K. , 2000, “A Mechanistic Model for Multiphase Flow in Pipes,” J. Can. Pet. Technol., 39(6), pp. 43–55. [CrossRef]
Meziou, A. , Chaari, M. , Franchek, M. , Grigoriadis, K. , Tafreshi, R. , and Ebrahimi, B. , 2014, “Subsea Production Two-Phase Flow Modeling and Control of Pipeline and Manifold Assemblies,” ASME Paper No. DSCC2014-6081.
Wassar, T. , Franchek, M. A. , and Gutierrez, J. A. , 2017, “ Reduced-Order Modelling of Transient Flow in Transmission Lines Using Distributed Lumped Parameters,” Int. J. Fluid Power, 18(3), pp. 153–166. [CrossRef]
Oldenburger, R. , and Goodson, R. E. , 1964, “Simplification of Hydraulic Line Dynamics by Use of Infinite Products,” ASME J. Basic Eng., 86(1), pp. 1–8. [CrossRef]
Sonnad, J. R., and Goudar, C. T., 2007, “Explicit Reformulation of the Colebrook-White Equation for Turbulent Flow Friction Factor Calculation,” Ind. Eng. Chem. Res., 46(8), pp. 2593–2600. [CrossRef]
Ke, L., and Slattery, C. , 2014, “Electromagnetic Flow Meters Achieve High Accuracy in Industrial Applications,” Analog Dialogue, 48(1), pp. 19–26. http://www.analog.com/media/en/analog-dialogue/volume-48/number-1/articles/electromagnetic-flow-meters-achieve-high-accuracy.pdf


Grahic Jump Location
Fig. 2

Linear fractional transformation scheme for the fault detection filter

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Fig. 1

Block diagram of the proposed fault detection filter scheme

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Fig. 6

Outputs of system with pressure sensor and flow rate sensor faults

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Fig. 5

Residual signals r(t)=[r1(t) r2(t)]T for fault free system

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Fig. 7

Comparison of fault and residual signals using the full-order fault detection filter

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Fig. 8

Comparison of fault and residual signals for the reduced-order fault detection filter

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Fig. 3

Inputs to system u(t)=[Pin(t) Qout(t)]T

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Fig. 4

Outputs of fault free system yp(t)=[Pout(t) Qin(t)]T



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