Journal of Intelligent Manufacturing

, Volume 30, Issue 1, pp 429–439 | Cite as

An approach to multiple fault diagnosis using fuzzy logic

  • Adrián Rodríguez RamosEmail author
  • Carlos Domínguez Acosta
  • Pedro J. Rivera Torres
  • Eileen I. Serrano Mercado
  • Gerson Beauchamp Baez
  • Luis Anido Rifón
  • Orestes Llanes-Santiago


The development of systems capable of diagnosing new and multiple faults in industrial systems is an active research topic. In this paper a model-based diagnostic system capable of diagnosing new and multiple faults using fuzzy logic as a fundamental tool is proposed. Also, the wavelet transform is used for isolating noise present in measurements. The proposed model was applied to the Continuously-Stirred Tank Heater model benchmark. The results demonstrate the feasibility of the proposed model, improving the robustness in the diagnostic, without loss of sensitivity to incipient or small magnitude faults.


Fault diagnosis Multiple faults Fuzzy logic Robustness Sensitivity Wavelet transform 



Continuously-Stirred Tank Heater model


Cold water


Discrete wavelet transform


Fault detection and isolation


Hot water


Linear time-invariant systems


Multi-resolution analysis




Supervisory control and data adquisition


Single location at a time


Wavelet transform


  1. Bachschmid, N., Pennacchi, P., & Vania, A. (2002). Identification of multiple faults in rotor systems. Journal of Sound and Vibration, 254, 327–366.CrossRefGoogle Scholar
  2. Bartenstein, T., Sliwinski, D., & Huisman, D. H. (2001). Diagnosing combinational logic de-signs using the single location at-a-time (slat) paradigm. In Proceedings of IEEE international test conference (ITC), Baltimore, USA (pp. 287–287).Google Scholar
  3. Bedoya, C., Uribe, C., & Isaza, C. (2012). Unsupervised feature selection based on fuzzy clustering for fault detection of the Tennessee Eastman process. In Advances in Artificial Intelligence. Springer-Verlag. LNAI (Vol. 7637, pp. 350–360).Google Scholar
  4. Botía, J., Isaza, C., Kempowsky, T., et al. (2013). Automaton based on fuzzy clustering methods for monitoring industrial processes. Engineering Applications of Artificial Intelligence, 26, 1211–1220.CrossRefGoogle Scholar
  5. Camps Echevarría, L., Llanes-Santiago, O., & Silva Neto, A. J. (2010). An approach for fault diagnosis based on bio-inspired strategies. Studies in Computational Intelligence, 284, 53–63.Google Scholar
  6. Ding, S. X. (2008). Model-based fault diagnosis techniques. London: Springer.Google Scholar
  7. Fantuzzi, C., Simani, S., & Patton, R. J. (2002). Model based fault diagnosis in dynamic systems using identification techniques. Berlin: Springer.Google Scholar
  8. Gertler, J. (2000). Designing dynamic consistency relations for fault detection and isolation. International Journal of Control, 73, 720–732.CrossRefGoogle Scholar
  9. Heng, A., Zhang, S., Tan, A., et al. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23, 724–739.CrossRefGoogle Scholar
  10. Hwang, I., Kim, S., Kim, Y., & Seah, C. E. (2010). A survey of fault detection, isolation, and reconfiguration methods. IEEE Transactions on Control Systems Technology, 18, 636–656.CrossRefGoogle Scholar
  11. Isermann, R. (2011). Fault-diagnosis applications: Model-based condition monitoring: Actuators, drives, machinery, plants, sensors, and fault-tolerant systems. London: Springer.Google Scholar
  12. Kunpeng, Z., San, W., & Soon, H. (2009). Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. International Journal of Machine Tools & Manufacture, 49, 537–553.CrossRefGoogle Scholar
  13. Mendonça, L. F., Sousa, J. M., & Sá da Costa, J. M. (2009). An architecture for fault detection and isolation based on fuzzy methods. Expert Systems with Applications, 36, 1092–1104.CrossRefGoogle Scholar
  14. Miguel, L. J. D., & Blázquez, L. F. (2005). Fuzzy logic-based decision-making for fault diagnosisin a DC motor. Engineering Applications of Artificial Intelligence, 18, 423–450.CrossRefGoogle Scholar
  15. Nooria, B. (2015). Developing a CBR system for marketing mix planning and weighting method selection using Fuzzy AHP. Applied Artificial Intelligence, 29, 1–32.CrossRefGoogle Scholar
  16. Perzyk, M., Kochanski, A., Kozlowski, J., Soroczynski, A., & Biernacki, R. (2014). Comparisonof data mining tools for significance analysis of process parameters in applications toprocess fault diagnosis. Information Sciences, 259, 380–392.CrossRefGoogle Scholar
  17. Rengaswamy, R., Dash, S., Maurya, M. R., & Venkatasubramanian, V. (2004). A novel interval-halving framework for automated identification of process trends. AIChE Journal, 50, 149–162.CrossRefGoogle Scholar
  18. Ruan, S., Zhou, Y., Feili, Y., Pattipati, K. R., Willett, P., & Patterson-Hine, A. (2009). Dynamic multiple-fault diagnosis with imperfect tests. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 39, 1224–1236.CrossRefGoogle Scholar
  19. Simani, S., & Patton, R. J. (2008). Fault diagnosis of an industrial gas turbine prototype using a system identification approach. Control Engineering Practice, 16, 769–786.CrossRefGoogle Scholar
  20. Simani, S., Farsoni, S., & Castaldi, P. (2015). Wind turbine simulator fault diagnosis via fuzzy modelling and identification techniques. Sustainable Energy, Grids and Networks, 1, 45–52.CrossRefGoogle Scholar
  21. Sobhani-Tehrani, E., Talebi, H. A., & Khorasani, K. (2014). Hybrid fault diagnosis of nonlinear systems using neural parameter estimators. Neural Networks, 50, 12–32.CrossRefGoogle Scholar
  22. Thornhill, N. F., Patwardhan, S. C., & Shah, S. L. (2008). A continuous stirred tank heater simulation model with applications. Journal of Process Control, 18, 347–360.CrossRefGoogle Scholar
  23. Uribe, C., & Isaza, C. (2011). Unsupervised feature selection based on fuzzy partition optimization for industrial processes monitoring. In IEEE international conference on computational intelligence for measurement systems and applications (CIMSA), Ottawa, Canada (pp. 1–5).Google Scholar
  24. Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003a). A review of process fault detection and diagnosis, part 1: Quantitative model-based methods. Computers and Chemical Engineering, 27, 293–311.CrossRefGoogle Scholar
  25. Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003b). A review of process fault detection and diagnosis, part 2: Qualitative models and search strategies. Computers and Chemical Engineering, 27, 313–326.CrossRefGoogle Scholar
  26. Vong, C. H., Wong, P. K., & Wong, K. I. (2014). Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis. Applied Soft Computing, 22, 238–248.CrossRefGoogle Scholar
  27. Wang, Z., Marek-Sadowska, M., Tsai, K. H., & Rajski, J. (2006). Analysis and methodology for multiple-fault diagnosis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 25, 558–575.CrossRefGoogle Scholar
  28. Wu, J. D., & Hsu, Ch. (2009). Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy-logic inference. Expert Systems with Applications, 36, 3785–3794.CrossRefGoogle Scholar
  29. Zhang, J., Ma, W., Lin, J., Ma, L., & Jia, X. (2015). Fault diagnosis approach for rotating machinery based on dynamic model and computational intelligence. Measurement, 59, 73–87.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Departamento de Automática y ComputaciónInstituto Politécnico José A. Echeverría, CUJAEMarianao, HabanaCuba
  2. 2.Department of Electrical and Computer EngineeringUniversity of Puerto Rico at MayagüezMayagüezPuerto Rico
  3. 3.Department of Telematic EngineeringAtlanTIC-ETSET-Universidade de VigoVigoSpain
  4. 4.Polytechnic University of Puerto RicoHato ReyPuerto Rico

Personalised recommendations