Wednesday , October 24 2018

Fuzzy Approach Applied in Fault Detection and Isolation
to the Compression System Control

Ahmed HAFAIFA
Dep. Industrial Processes Automation, Science and Technology Faculty, University of Djelfa
17000 DZ, Algeria

Ferhat LAAOUAD
Department of Industrial Process Automation, Faculty of Hydrocarbons and Chemistry, University of Boumerdes
35000 DZ, Algeria

Kouider LAROUSSI
Dep. Industrial Processes Automation, Science and Technology Faculty, University of Djelfa
17000 DZ, Algeria

Abstract: During the last decade, significant change of direction in the development of control theory and its application has attracted great attention from the academic and industrial communities. The concept of ‘Intelligent supervision’ has been suggested as an alternative approach to conventional supervision techniques for complex control systems. The objective is to introduce new mechanisms permitting a more flexible supervision system, but especially more robust one, able to deal with model uncertainties and parameter variations. In this work, we present an application of the fuzzy approach in fault detection and isolation of surge in this compression system. This paper illustrates an alternative implementation to the compression systems supervision task using the basic principles of model-based fault detection and isolation associated with fuzzy modelling approach. Application results of a fault detection and isolation for a compression system are provided, which illustrate the relevance of the proposed fuzzy fault detection and isolation method.

Keywords: Fault detection and isolation; Nonlinear systems; Fuzzy logic; Fuzzy fault detection and isolation; Compression system; Centrifugal compressor; Surge phenomena.

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CITE THIS PAPER AS:
Ahmed HAFAIFA, Ferhat LAAOUAD, Kouider LAROUSSI, Fuzzy Approach Applied in Fault Detection and Isolation to the Compression System Control, Studies in Informatics and Control, ISSN 1220-1766, vol. 19 (1), pp. 17-26, 2010.

1. Introduction

Today the compression systems are subjected to highly hostile working conditions. The manufacturer is greatly interested with any improvement in performance, life and weight reduction without loss of reliability [11]. Therefore, it is worthwhile to carefully estimate the reliability of rotating systems in order to improve the supervision and the control system or eventually modify the design. Reliability analyses of the supervision structure require some information on the model of the compression system. We know it is difficult to obtain the mathematical model for a complicated mechanical structure. The turbo compressor is considered as a complex system where many modelling and controlling efforts have been made. In the regard to the complexity and the strong non linearity of the turbo compressor dynamics, and the attempt to find a simple model structure which can capture in some appropriate sense the key of the dynamical properties of the physical plant, we propose to study the application possibilities of the recent supervision approaches and evaluate their contribution in the practical and theoretical fields consequently.

In the literature, numerous applications of faultdiagnosis are reported for many manufacturing industries. For the last two decades, there have been extensive research efforts on developing model-based FDI techniques [3],[15],[23]. A residual signal is generated by comparing the measured output signal and the estimated one from a nominal system model. After being processed, this residual can be used as the indicator of abnormal behavior (faults) of the system. More advanced methods are data-driven process monitoring methods [2], [14], most heavily used in many industrials applications. Other methods rely on analytical redundancy [6], [12], the comparison of the actual plant behaviour to that expected on the basis of a mathematical model. Sometimes, further insight is required as to the explicit behavior of the model involved and it is here that fuzzy and even neuron fuzzy methods come into their own in fault diagnosis applications [12]. Other authors have used evolutionary programming tools to design observers and neural networks. The work on fault diagnosis Artificial Intelligence community was initially focused on the expert system or knowledge based approaches [14], where heuristics are applied to explicitly associate symptoms with fault hypothesis. The short comings of a pure expert system approach led to the development of model-based approaches based on qualitative models in form of qualitative differential equations, signed diagraphs, qualitative functional and structural models, etc ., [16],[19],[20].

Facing to the studied industrial process complexity, we choose to make recourse to fuzzy logic for analysis and treatment of its supervision problem owing to the fact that these technique constitute the only framework in which the types of imperfect knowledge can jointly be treated (uncertainties, inaccuracies, …) offering suitable tools to characterise them. This choice is motivated by the effectiveness of fuzzy logic solution for many industry problems [5], [14], [15]. Indeed, based only on human expert knowledge, fuzzy logic solutions are very powerful on nonlinear systems modelling and control.

This work illustrates an alternative implementation to the compression systems supervision task using the basic principles of model-based fault detection and isolation associated with the self-tuning of surge measurements with subsequent appropriate corrective actions. Using a combination of fuzzy modelling approach makes it possible to devise a fault-isolation scheme based on the given incidence matrix.

The presented approach is based on the use of the fuzzy model. As was introduced in [3], [15], by applying a Takagi-Sugeno- type fuzzy model with interval parameters, one is able to approximate the upper and lower boundaries of the domain of functions that result from an uncertain system.

The fuzzy model is therefore intended for robust modelling purposes; on the other hand, studies show it can be used in fault detection as well. The novelty lies in defining of confidence bands over finite sets of input and output measurements in which the effects of unknown process inputs are already included. Moreover, it will be shown that by data pre-processing the fuzzy model parameter-optimization problem will be significantly reduced. By calculating the normalized distance of the system output from the boundary model outputs, a numerical fault measure is obtained. The main idea of the proposed approach is to use the fuzzy model in an FDI system as residual generators, and combine the fuzzy model outputs for the purpose of fault isolation. Due to data pre-processing, the decision stage is robust to the effects of system disturbances.

The paper presents an application of the fuzzy model in fault detection and isolation for the compression system with interval type uncertain parameters. The FDI problem was split into two steps. In the former step the fuzzy model along with data pre-processing and low-pass filtering were introduced into the fault detection scheme. In the latter the combination of residuals was used in the fault-isolation stage. In its final part the paper gives some conclusions about this application.

REFERENCE

  1. ABID, H., M. CHTOUROU, A. TOUMI, Robust Fuzzy Sliding Mode Controller for Discrete Nonlinear Systems, International Journal of Computers, Communications & Control, Agora University Editing House – CCC Publications, vol. 3(1), 2008, pp. 6-20.
  2. ALBERTSON, F., H. BODÉN, J. GILBERT, Comparison of Different Methods to Couple Nonlinear Source Descriptions in the Time Domain to Linear System Descriptions in the Frequency Domain-Application to a Simple Valveless One-cylinder Cold Engine, Journal of Sound and Vibration, Elsevier, vol. 291(3-5), 2006, pp. 963-985.
  3. HOLLOWAY, A., L. E. HOLLOWAY, Automated Control, Observation, and Diagnosis of Multi-layer Condition Systems, Studies in Informatics and Control SIC Journal, ICI Publishing House, vol. 16(1), 2007, pp.97-114.
  4. BOCANIALA, C. D., S. BUMBARU, Experimental Validation of a Novel Fuzzy Classifier Performance, Studies in Informatics and Control SIC Journal, ICI Publishing House, vol. 14(3), 2005, pp. 167-180.
  5. BENREJEB, M., D. SOUDANI, A. SAKLY, P. BORNE, New Discrete TSK Fuzzy Systems Characterization and Stability Domain. International Journal of Computers, Communications & Control, Agora University Editing House – CCC Publications, vol. 1(4), 2006, pp. 9-19.
  6. BOUYAHIAOUI, C., L. I. GRIGORIEV, F. LAAOUAD, A. KHELASSI, Optimal Fuzzy Control to Reduce Energy Consumption in Distillation Columns, Automation and Remote Control, Springer, vol. 66(2), 2005, pp. 200-208.
  7. Dieulot, J. Y., Fuzzy Control and Estimation Using Model Inversion. Studies in Informatics and Control SIC Journal, ICI Publishing House, vol. 13(3), 2004, pp. 153-160.
  8. DIEULOT, J. Y., P. BORNE, Inverse Fuzzy Sum-product Composition and its Application to Fuzzy Linguistic Modelling. Studies in Informatics and Control SIC Journal, ICI Publishing House, vol. 14(2), 2005, pp. 73-78.
  9. GALINDO, J. R. SERRANO, H. CLIMENT, A. TISEIRA, Experiments and Modelling of Surge in Small Centrifugal Compressor for Automotive Engines, Experimental Thermal and Fluid Science, Elsevier, vol. 32(3), 2008, pp. 818-826.
  10. J. T. GRAVDAHL, O. EGELAND, S. O. VATLAND, Drive Torque Actuation in Active Surge Control of Centrifugal Compressors, Automatica, Elsevier, vol. 38(11), 2002, pp. 1881-1893.
  11. GRAVDAHL, J. T., F. WILLEMS, B. JAGER DE, O. EGELAND, Modeling of Surge in Free-Spool Centrifugal Compressors: Experimental Validation, Journal of Propulsion and Power, Elsevier, vol. 20(5), 2004, pp. 849-857.
  12. HAFAIFA, F. LAAOUAD, M. GUEMANA, A New Engineering Method for Fuzzy Reliability Analysis of Surge Control in Centrifugal Compressor, American Journal of Engineering and Applied Sciences, Science Publisher Inc, vol. 2(4), 2009, pp. 676-682.
  13. HELVOIRT, J. V., B. D. JAGER, Dynamic Model Including Piping Acoustics of a Centrifugal Compression System, Journal of Sound and Vibration Elsevier, vol. 302(1-2), 2007, pp. 361-378.
  14. MATTONE, R., A. DE LUCA, Nonlinear Fault Detection and Isolation in a Three-tank Heating System, IEEE Transactions on Control Systems Technology, vol. 14(6), 2006, pp. 1158-1166.
  15. MATTONE, R., A. DE LUCA, Relaxed Fault Detection and Isolation: An Application to a Nonlinear Case Study, Automatica, Elsevier, vol. 42(1), 2006, pp. 109-116.
  16. MOISE, G., Applying Fuzzy Control in the Online Learning Systems. Studies in Informatics and Control SIC Journal, ICI Publishing House, vol. 18(2), 2009, pp. 165-172.
  17. NAYFEH, M., E. H. ABED, High-gain Feedback Control of Rotating Stall in Axial Flow Compressors, Automatica, Elsevier, vol. 38(6), 2002, pp. 995-1001.
  18. PADUANO, J. D., E.M. GREITZER, A.H. EPSTEIN, Compression System Stability and Active Control, Annual Review of Fluid Mechanics, Elsevier, (2001), vol. 33, pp. 491-517.
  19. POP, B., I. DZITAC, Fuzzy Control Rules in Convex Optimization. Studies in Informatics and Control SIC Journal, ICI Publishing House, vol. 15(4), 2007, pp. 363-366.
  20. POP, B., I. DZITAC, Mixed Variables Fuzzy Programming Algorithm. Studies in Informatics and Control SIC Journal, ICI Publishing House, vol. 16(2), 2007, pp. 185-190.
  21. RUDAS, I. J., L. HORVÁTH, Intelligence for Assistance of Engineering Decisions in Modeling Systems, Studies in Informatics and Control SIC Journal, ICI Publishing House, vol. 15(3), 2006, pp. 297-306.
  22. SPAKOVSKY, Z. S., J. B. GERTZ, O. P. SHARMA, J. D. PADUANO, A. H. EPSTEIN, E. M. GREITZER, Influence of Compressor Deterioration on Engine Dynamic Behaviors and Transient Surge Margin, ASME Journal of Turbomachinery, vol. 122(3), 2000, pp. 477-484.
  23. ZHANG, X., M. M. POLYCARPOU, T. PARISINI, A Robust Detection and Isolation Scheme for Abrupt and Incipient Faults in Nonlinear Systems, IEEE transactions on automatic control, vol. 47(4), 2002, pp. 576-593.

https://doi.org/10.24846/v19i1y201002