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Research Papers: Gas Turbines: Turbomachinery

Robust Fault Detection to Determine Compressor Surge Point Via Dynamic Neural Network-Based Subspace Identification Technique

[+] Author and Article Information
Sayyid Mahdi Alavinia

Department of Electrical and
Robotic Engineering,
Shahrood University of Technology,
Shahrood 3619995161, Iran
e-mail: zalloi@shahroodut.ac.ir

Mohammad Ali Sadrnia

Department of Electrical and
Robotic Engineering,
Shahrood University of Technology,
Shahrood 3619995161, Iran
e-mail: masadrnia@shahroodut.ac.ir

Mohammad Javad Khosrowjerdi

Department of Electrical Engineering,
Sahand University of Technology,
Tabriz 3619995161, Iran
e-mail: khosrowjerdi@sut.ac.ir

Mohammad Mehdi Fateh

Department of Electrical and
Robotic Engineering,
Shahrood University of Technology,
Shahrood 3619995161, Iran
e-mail: mmfateh@shahroodut.ac.ir

1Corresponding author.

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received November 27, 2013; final manuscript received January 26, 2014; published online February 28, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 136(8), 082602 (Feb 28, 2014) (8 pages) Paper No: GTP-13-1440; doi: 10.1115/1.4026610 History: Received November 27, 2013; Revised January 26, 2014

In this paper, a dynamic neural network (DNN) based on robust identification scheme is presented to determine compressor surge point accurately using sensor fault detection (FD). The main innovation of this paper is to present different and complementary technique for surge suppressing studies within sensor FD. The proposed method aims to utilize the embedded analytical redundancies for sensor FD, even in the presence of uncertainty in the compressor and sensor noise. The robust dynamic neural network is developed to learn the input–output map of the compressor for residual generation and the required data is obtained from the compressor Moore–Greitzer simulated model. Generally, the main drawback of DNN method is the lack of systematic law for selecting of initial Hurwitz matrix. Therefore, the subspace identification method is proposed for selecting this matrix. A number of simulation studies are carried out to demonstrate the advantages, capabilities, and performance of our proposed FD scheme and a worthwhile direction for future research is also presented.

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References

Bloch, H. P., 2006, Application Guide to Compressor Technology, 2nd ed., Wiley, New York.
Bloch, H. P., 2006, Compressors and Modern Applications, 2nd ed., Wiley, New York, pp. 115–127.
Helivort, J. V., 2007, “Centrifugal Compressor Surge Modeling and Identification for Control,” Ph.D. thesis, Eindhoven University of Technology, Eindhoven, Netherlands.
Billam, M. R., 2011, “Compressors Used in Oil & Gas Industry,” Dresser-Rand, Olean, NY, available at: http://www.ipt.ntnu.no/~jsg/undervisning/naturgass/lysark/LyarkBillam2011.pdf
Gatewood, J., 2012, “Future Compressor Station Technologies and Applications,” Gas Electric Partnership Conference, Southwest Research Institute.
Betta, G., and Pietrosanto, A., 2000, “Instrument Fault Detection and Isolation: State of the Art and New Research Trends,” IEEE Trans. Instrum. Meas., 49(1), pp. 100–107. [CrossRef]
Qin, S., and Li, W., 2001, “Detection and Identification of Faulty Sensors in Dynamic Processes,” AIChE J., 47(7), pp. 1581–1593. [CrossRef]
Sami Shaker, M., 2012, “Active Fault-Tolerant Control of Nonlinear Systems With Wind Turbine Application,” Ph.D. thesis, University of Hull, Yorkshire, UK.
Alag, S., Agogino, A., and Morjaria, M., 2001, “A Methodology for Intelligent Sensor Measurement, Validation, Fusion, and Fault Detection for Equipment Monitoring and Diagnostics. (AI EDAM),” Artificial Intell. Eng. Design, Anal. Manufacturing, 15(4), pp. 307–320. [CrossRef]
Jiang, L., 2011, “Sensor Fault Detection and Isolation Using System Dynamics Identification Techniques,” Ph.D. thesis, The University of Michigan, Ann Arbor, MI.
Pike, P., and Pennycook, K., 1992, Commissioning of BEMS: Code of Practice, Building Services Research & Information Association, Berkshire, UK.
Keliris, C., Polycarpou, M., and Parisini, T., 2013, “A Distributed Fault Detection Filtering Approach for a Class of Interconnected Continuous-Time Nonlinear Systems,” IEEE Trans. Auto. Control, 58(8), pp. 2032–2047. [CrossRef]
Patton, R. J., Frank, P. M., and Clark, R. N., 2000, Issues in Fault Diagnosis for Dynamic Systems, Springer, New York.
Qayyum Khan. A., 2010, “Observer-Based Fault Detection in Nonlinear Systems,” Ph.D. thesis, University of Duisburg-Essen, Essen, Germany.
Ding, S. X., 2012, “Data-Driven Design of Model-Based Fault Diagnosis Systems,” 8th IFAC Symposium on Advanced Control of Chemical Processes, The International Federation of Automatic Control, Furama Riverfront, Singapore, July 10–13.
Terra, M. H., and Tinos, R., 2001, “Fault Detection and Isolation in Robotic Manipulators Via Neural Networks: A Comparison Among Three Architectures for Residual Analysis,” J. Robot. Syst., 18(7), pp. 357–374. [CrossRef]
Bakshi, B. R., and Stephanopoulos, G., 1993, “Wave-Net: A Multiresolution, Hierarchical Neural Network With Localized Learning,” J. Am. Inst. Chem. Eng., 39(1), pp. 57–81. [CrossRef]
Guo, T. H., and Nurre, J., 1991, “Sensor Failure Detection and Recovery by Neural Networks,” International Joint Conference on Neural Networks (IJCNN-91-Seattle), Seattle, WA, July 8–14, pp. 221–226. [CrossRef]
Perla, R., Mukhopadhyay, S., and Samanta, A., 2004, “Sensor Fault Detection and Isolation Using Artificial Neural Networks,” IEEE Region 10 Conference (TENCON 2004), Chiang Mai, Thailand, November 21–24, pp. 676–679. [CrossRef]
Frank, P., 1987, “Fault Diagnosis in Dynamic Systems Via State Estimation—A Survey,” System Fault Diagnostics, Reliability, and Related Knowledge-Based Approaches, Vol. 1, D. Reidel Publishing Co., Dordrecht, Netherlands, pp. 35–98. [CrossRef]
Pham, D., and Liu, X., 1992, “Dynamic System Identification Using Partially Recurrent Neural Networks,” Neural Networks for Identification, Prediction and Control, Springer-Verlag, London, pp. 47–61.
Deng, Jiamei, 2013, “Dynamic Neural Networks With Hybrid Structures for Nonlinear System Identification,” Eng. Appl. Artificial Intell., 26(1), pp. 281–292. [CrossRef]
Gupta, M., Jin, L., and Homma, N., 2003, Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory, Wiley, New York.
Funahashi, K., and Nakamura, Y., 1993, “Approximation of Dynamic Systems by Continuous-Time Recurrent Neural Networks,” Neural Networks, 6(6), pp. 801–806. [CrossRef]
Polycarpou, M., and Ioannou, P., 1991, “Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis,” University of Southern California, Los Angeles, CA, Systems Report No. 91-09-01.
Wen, Y., Poznyak, A., and Xiaoou, L., 2001, “Multilayer Dynamic Neural Networks for Non-Linear System On-Line Identification,” Int. J. Control, 74(18), pp. 1858–1864. [CrossRef]
Poznyak, A. S., Wen, Y., Poznyak, T. I., and Najim, K., 2004, “Simultaneous States and Parameters Estimation of an Ozonation Reactor Based on Dynamic Neural Network,” Diff. Equ. Dyn. Syst., 12(1–2), pp. 195–221.
García, A., Poznyak, A., Chairez, I., and Poznyak, T., 2008, “Projectional Differential Neural Network Observer With Stable Adaptation Weights,” 47th IEEE Conference on Decision and Control (CDC 2008), Cancun, Mexico, December 9–11. [CrossRef]
Mirak, S. M., 2013, “Neural Network-Based Fault Diagnosis of Satellites Formation Flight,” M.Sc. thesis, Concordia University, Portland, OR.
Li, L., Ma, L., and Khorasani, K., 2005, “A Dynamic Recurrent Neural Network Fault Diagnosis and Isolation Architecture for Satellite’s Actuator/Thruster Failures,” Advances in Neural Networks – ISNN 2005 (Lecture Notes in Computer Science, Vol. 3498), Springer, Berlin, pp. 574–583. [CrossRef]
Valdes, A., and Khorasani, K., 2010, “A Pulsed Plasma Thruster Fault Detection and Isolation Strategy for Formation Flying of Satellites,” Appl. Soft Comput., 10(3), pp. 746–758. [CrossRef]
Patan, K., and Parisini, T., 2005, “Identification of Neural Dynamic Models for Fault Detection and Isolation: The Case of a Real Sugar Evaporation Process,” IFAC J. Process Control, 15(1), pp. 67–79. [CrossRef]
Greitzer, E. M., 1997, “Surge and Rotating Stall in Axial Flow Compressors: Part I—Theoretical Compression System Model,” ASME J. Eng. Power, 98(2), pp. 190–198. [CrossRef]
Greitzer, E. M., 1997, “Surge and Rotating Stall in Axial Flow Compressors: Part II—Experimental Results and Comparison With Theory,” ASME J. Eng. Power, 98(2), pp. 199–217. [CrossRef]
Gravdahl, J. T., and EgelandO., 1999, Compressor Surge and Rotating Stall: Modelling and Control, Springer, New York.
Guoxiang, G., Andrew, S., and CalinB., 1999, “Stability Analysis for Rotating Stall Dynamics in Axial Flow Compressors,” Circuits, Syst., Signal Process., 18(4), pp. 331–350. [CrossRef]
Christensen, D., Armor, J., Dhingra, M., Cantin, P., Gutz, D., Neumeier, Y., Prasad, J. V., Szucs, A., and Wadia, R., 2008, “Development and Demonstration of a Stability Management System for Gas Turbine Engines” ASME J. Turbomach., 130(3), p. 031011. [CrossRef]
Hunt, K. J., Sbarbaro, D., Zbikowski, R., and Gawthrop, P. J., 1992, “Neural Networks for Control Systems—A Survey,” Automatica, 28(6), pp. 1083–1112. [CrossRef]
Hush, D. R., and Horne, B. G., 1993, “Progress in Supervised Neural Networks,” IEEE Signal Process, 10(1), pp. 8–39. [CrossRef]
Tayarani-Bathaie, S. S., Sadough Vanini, Z. N., and Khorasani, K., 2013, “Dynamic Neural Network-Based Fault Diagnosis of Gas Turbine Engines,” Neuro Computing, Vol. 125, Elsevier, New York, pp. 153–165.
Dinh, H., 2012, “Dynamic Neural Network-Based Robust Control Methods for Uncertain Nonlinear Systems,” Ph.D. thesis, University of Florida, Gainesville, FL.
Lewis, F. L., Selmic, R., and Campos, J., 2002, Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities, SIAM, Philadelphia, PA.
Garcia, B.Poznyak, A.Chairez, I. and PoznyakT., 2007, “Projectional Dynamic Neural Network Observer,” 3rd IFAC Symposium on System, Structure and Control, Foz do Iguaçu, Brazil, October 17–19. [CrossRef]
Van Overschee, P., De Moor, B., 1994, “N4SID: Subspace Algorithms for the Identification of Combined Deterministic-Stochastic Systems,” Automatica, 30(1), pp. 75–93. [CrossRef]
Trnka, P., 2005, “Subspace Identification Methods, Technical Report,” Czech Technical University in Prague, Prague, Czech Republic.
Naik, A. S., 2010, “Subspace Based Data-Driven Designs of Fault Detection Systems,” Ph.D. thesis, University Duisburg-Essen, Essen, Germany.
Poznyak, A. S., Sanchez, E. N., and Wen, Y., 2001, Differential Neural Networks for Robust Nonlinear Control Identification, State Estimation and Trajectory Tracking, World Scientific, Singapore.
Verhaegen, M., and Dewilde, P., 1992, “Subspace Model Identification, Part 1: The Output-Error State-Space Model Identification Class of Algorithms,” Int. J. Control, 56(5), pp. 1187–1210. [CrossRef]

Figures

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Fig. 1

Fault detection based on analytical redundancy

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Fig. 2

Compressor performance curve

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Fig. 3

Dynamic neural network schematic

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Fig. 4

DNN based on robust property schematic

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Fig. 5

The residual generation schematic via RDNN

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Fig. 6

The fitness of actual and identified signals

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Fig. 7

Mean squared error (MSE)

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Fig. 8

DNN identifier and compressor outputs

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Fig. 9

RDNN identifier and compressor outputs in the healthy scenario

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Fig. 10

RDNN identifier and compressor outputs in the faulty scenario

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Fig. 11

Residual signal in the RDNN identifier

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Fig. 12

Residual signal and threshold

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Fig. 13

y, y∧ and residual outputs in sensor cut off scenario

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