CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
Reseach Article

Real Time Fault Detection and Isolation: A Comparative Study

by Shahrzad Hekmat, Reza Ravanmehr
journal cover thumbnail
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134 - Number 6
Year of Publication: 2016
Authors: Shahrzad Hekmat, Reza Ravanmehr
10.5120/ijca2016907931

Shahrzad Hekmat, Reza Ravanmehr . Real Time Fault Detection and Isolation: A Comparative Study. International Journal of Computer Applications. 134, 6 ( January 2016), 5-12. DOI=10.5120/ijca2016907931

@article{ 10.5120/ijca2016907931,
author = { Shahrzad Hekmat, Reza Ravanmehr },
title = { Real Time Fault Detection and Isolation: A Comparative Study },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 134 },
number = { 6 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 5-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume134/number6/23916-2016907931/ },
doi = { 10.5120/ijca2016907931 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:33:24.316615+05:30
%A Shahrzad Hekmat
%A Reza Ravanmehr
%T Real Time Fault Detection and Isolation: A Comparative Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 134
%N 6
%P 5-12
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Design and implementation of a complicated real-time system, completely free of fault is difficult and fault tolerance methods require features which are not usually follow the characteristics of a real-time systems. To deal with this issue, an appropriate automatic classification system to detect and diagnosis faults in run time should be utilized. This fault detection system must be independent of main real-time system and based on the received information it controls the behavior of real-time system. Type and content of this information has a main role to monitor and control real-time system and must be selected in such a way to determine the overall system status in normal and abnormal conditions. In this paper, after briefly discuss the major types of faults that can be happened in real-time systems , different methods of fault detention and isolation in real-time system are studied and evaluated.

References
  1. M. Witaczak, “Advances in Model–base Fault Diagnosis With Evolutionary Algorithms and Neural Network”, International Journal of Applied Mathematics and Computer Science, vol.16, no. 1, Mar. 2006, p. 85–99.
  2. R. Micalizio, “On-line Monitoring and Diagnosis of a Multi-Agent System: a Model-Based Approach”, Doctoral Dissertation, Dep. Of Informatics, Univ. Torino, Italy, 2007.
  3. K. Wang, Intelligent Condition Monitoring and Diagnosis Systems (A Computational Intelligence Approach), Dep. Production and Quality Engineering, Univ. Norwegian, Norway,Ohmsha, IOS Press, 2003.
  4. A. M. Whelan, “An Intelligent Real-Time System Architecture Implemented in ADA”, Master of Science Thesis, Ins. Air Force, Univ. Air Technology, 1992.
  5. M. A. Aborizka, “An Architectural Framework for the Specification, Analysis and Design of Intelligent Real-time Monitoring Agent-based Software Systems”, Doctoral Dissertation, Dep.
  6. Electrical and Computer Engineering, Univ. Alabama in Huntsville, 2002.
  7. C. Wang, L. Xu and W. Pong, “Conceptual Design of Remote Monitoring and Fault Diagnosis Systems”, Information Systems, Nov. 2007, pp. 996–1004.
  8. C. Ündey, E. Tatara And A. Çınar, “Intelligent Real-time Performance Monitoring and Quality Prediction for Batch/fed-batch Cultivations”, Journal of Biotechnology, Feb. 2004, pp. 61–77.
  9. E. Coskun And M. Grabowski, “An Interdisciplinary Model of Complexity in Embedded Intelligent Real-time Systems”, Information and Software Technology, Aug. 2001, pp. 527-537.
  10. I. J. Bate, “Scheduling and Timing Analysis for Safety Critical Real-Time Systems”, Doctoral Dissertation, Dep. Computer Science, Univ. York, York, 1999.
  11. R. Micalizio, “On-line Monitoring and Diagnosis of a Team of Service Robots: a Model-based Approach”, AI Communications, Dec. 2006, pp.313–349.
  12. M. M. El Emary, A. Al Ahliyya, and A. Balqa, “Fault Detection of Computer Communication Networks Using an Expert System”, American Journal of Applied Sciences, vol. 2, no. 10,2005, pp. 1407-1411.
  13. C. B. Yeh, “Design considerations in Boeing 777 fly-by-wire computers”, Proc. 3th IEEE High-Assurance Systems Engineering Sym. (HASE 98), Washington DC, 1998, pp.64-73.
  14. A. Burns and A. J. Wellings, Real-Time Systems and Programming Languages, 3rd edition, Addison-Wesley, 2001.
  15. E. Coskun And M. Grabowski, “Software Complexity and its Impact in Embedded Intelligent Real-time Systems”, The Journal of Systems and Software, Dec. 2005, pp. 128-145.
  16. I. Broster, “Distributed Real-time Safety-critical Control Systems”, dissertation, Dep. Computer Science, Univ. York, York, 2000.
  17. j. Rushby, “Critical System Properties: Survey and Taxonomy”, Technical Report CSL-93-01, SRI International, Feb. 1994.
  18. A.Avizˇienis, J.C. Laprie and B. Randell, “Fundamental Concepts of Computer System Dependability”, Technological Challenge of Dependable Robots in Human Environments – Seoul, Korea, May, 2001.
  19. M. Ruusunen and M. Paavola, “Quality Monitoring and Fault Detection in an Automated Manufacturing System - a Soft Computing Approach”, Control Engineering Laboratory, Dep.Process and Environmental Engineering, Univ. University of Oulu, 2002.
  20. N. Kandasemy, J.P. Hayes2, B.T. Murray, “Dependable Communication Synthesis for Distributed Embedded Systems”, Proc. Int’l Conf. Computer Safety, Reliability & Security (SAFECOMP 2003).
  21. j. c. Laprie, Dependability: Basic Concepts and Terminology, volume 5 of Dependable Computing and Fault-Tolerant Systems. Springer-Verlag, 1992.
  22. C. Angeli, “On-Line Fault Detection Techniques for Technical Systems”, International Journal of Computer Science & Applications, vol. I, no. 1, Sep. 2004, pp. 12-30.
  23. H. Kopetz, “Real-time systems: Design principles for distributed embedded applications”, Computers & Mathematics with Applications, vol. 34, no. 10, Nov. 1997, p. 142.
  24. Ukpong, “Real-time intelligent monitoring and diagnostic system for a cnc turret lathe in a production environment using multi-sensing and neural network”, Doctoral Dissertation, University of Missouri – Rolla,1998.
  25. S. J. Albus and J. A. Barbera, “A cognitive architecture for intelligent multi-agent systems”, Annual Reviews in Control, 2005, pp 87–99.
  26. P. J. Mosterman and G. Biswas, “Monitoring, prediction and fault isolation in dynamic physical systems”, in AAAI-97 Proceedings, 1997, pp. 100-105.
  27. Y. Kato and T. Mukai, “A real-time intelligent gas sensor system using a nonlinear dynamic response”, Sensors and Actuators B: Chemical, vol. 120, no. 2, Jan. 2007, pp. 514-520.
  28. Iqbal, N. He, L. Li, and N. U. Dar, “A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process”, Expert Systems with Applications:An International Journal, vol. 32 , no. 4, May 2007, pp. 1020-1027.
  29. S. C. Tan, C. P. Lim, and M. V. C. Rao, “A hybrid neural network model for rule generation and its application to process fault detection and diagnosis”, Engineering Applications of Artificial Intelligence, vol. 20, no. 2, Mar. 2007, pp. 203-213.
  30. Nikolopoulos, Expert Systems, Introduction to first and second Generation and Hybrid Knowledge Based Systems. New York, US: Marcel Dekker Inc., 1997.
  31. V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri, “A review of process fault detection and diagnosis: Part I: Quantitative model-based methods”, Computers & Chemical Engineering, vol. 27, no. 3, Mar. 2003, pp. 293-311.
  32. Lee, R. L. Alena, and P. Robinson, “Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station”, NASA Ames Research Center , Technical Report, 2004.
  33. L. A. Zadeh, “Fuzzy Logic and Approximate Reasoning”, Synthese, vol. 30, no. 3-4, Dec. 2004, pp. 407-428.
  34. M. K.O. Scielny and M. Syfert, “Fuzzy diagnostic reasoning that takes into account the uncertainty of the relation between faults and symptoms”, International Journal of Applied Mathematics and Computer Science, vol. 16, no. 1, Mar. 2006, pp. 27-35.
  35. F. V. Jensen, S. L. Lauritzen, and K. G. Olesen, “Bayesian updating in causal probabilistic networks by local computations”, Computational Statistics Quarterly, vol. 5, no. 4, pp. 269-282, 1990.
  36. Angeli and D. Atherton, “A Model-Based Method for an Online Diagnostic Knowledge-Based System”, Expert Systems, vol. 18, no. 3, Jul. 2001, pp. 150-158.
  37. T. Mikaelian, B. C. Williams, and M. Sachenbacher, “Model-based monitoring and diagnosis of systems with software-extended behavior”, in Aaai Conference On Artificial Intelligence, Pennsylvania, 2005, pp. 327-333.
  38. C. Angeli and A. Chatzinikolaou, “On-Line Fault Detection Techniques for Technical Systems: A Survey”, International Jornal of Computer Science & Applications, vol. 1, no. 1,2004, pp. 12-30.
  39. X. Zheng, Z. Wang, F. Qian, “An Expert System for Real-time Fault Diagnosis and Its Application in PTA Process”, Proc. IEEE Sixth World Congress on Intelligent Control and Automation, WCICA 2006, 2006, pp. 5623-5627.
  40. M. Witczak, “Identification and fault detection of non-linear dynamic systems”, Lecture Notes in Control and Computer Science, vol. 1, 2003, p. 124.
  41. Agarwal, Deepshikha, and Nand Kishor. "An approach to real-time fault detection in health monitoring of offshore wind-farms." Wireless and Mobile, 2014 IEEE Asia Pacific Conference on. IEEE, 2014
  42. He, Hongbo, et al. "Real-time fault detection for solar hot water systems using adaptive resonance theory neural networks." ASME 2011 5th International Conference on Energy Sustainability. American Society of Mechanical Engineers, 2011
  43. Guo, Meng, Dimos V. Dimarogonas, and Karl Henrik Johansson. "Distributed real-time fault detection and isolation for cooperative multi-agent systems." American Control Conference (ACC), 2012. IEEE, 2012
  44. Baggiani, F. and Marsili-Libelli, S. Real-time fault detection and isolation in biological wastewater treatment plants. Water Science and Technology, 60(11), 2949-2961. . (2009).
  45. Lee, Hanmin, Steve Snyder, and Naira Hovakimyan. "An Adaptive Unknown Input Observer for Fault Detection and Isolation of Aircraft Actuator Faults." (2014).
  46. Hwang, Inseok, et al. “A survey of fault detection, isolation, and reconfiguration methods.” Control Systems Technology, IEEE Transactions on 18.3 (2010): 636-653.
  47. Leite, D. F., et al. "Real-time model-based fault detection and diagnosis for alternators and induction motors." Electric Machines & Drives Conference, 2007. IEMDC'07. IEEE International. Vol. 1. IEEE, 2007
  48. Kirubarajan, Thiagalingam, et al. “Fault detection algorithms for real-time diagnosis in large-scale systems.” Aerospace/Defense Sensing, Simulation, and Controls. International Society for Optics and Photonics, 2001
  49. Sait, Abdulrahman S. “Real-Time Condition Monitoring and Fault Diagnosis of Gear Train Systems Using Instantaneous Angular Speed (IAS) Analysis.” (2013).
  50. Oh, Sukjoon. "a review of real-time fault detection & diagnostics (fdd), real-time commissioning, real-time m&v, and bulding automation system (bas)/energy management & control system (emcs)." (2014).
  51. V. Venkatasubramanian, R. RengaswamyE-mail The Corresponding Author, and S. N. Kavuri,“A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies”, Computers & Chemical Engineering, vol. 27, no. 3, Mar. 2003, pp. 313-326.
Index Terms

Computer Science
Information Sciences

Keywords

Real-time systems Fault Fault Detection Fault Isolation.