Skip to main content
SpringerLink
Log in

An approach to robust fault diagnosis in mechanical systems using computational intelligence

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

In this paper a novel approach to design robust fault diagnosis systems in mechanical systems using historical data and computational intelligence techniques is presented. First, the pre-processing of the data to remove the outliers is performed with the aim of reducing the classification errors. To accomplish this objective, the Density Oriented Fuzzy C-Means (DOFCM) algorithm is used. Later on, the Kernel Fuzzy C-Means (KFCM) algorithm is used to achieve greater separability among the classes, and reducing the classification errors. Finally, an optimization process of the parameters used in the training state by the DOFCM and KFCM for improving the classification results is developed using the bioinspired algorithm Ant Colony Optimization. The proposal was validated using the DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems) benchmark. The satisfactory results obtained indicate the feasibility of the proposal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Aydin, I., Karakose, M., & Akin, E. (2014). An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space. ISA Transactions, 53, 220–229.

    Article  Google Scholar 

  • Bartys, M., Patton, R., Syfert, M., de las Heras, S., & Quevedo, J. (2006). Introduction to the DAMADICS actuator FDI benchmark study. Journal of Control Engineering Practice, 14, 577–596.

    Article  Google Scholar 

  • Bedoya, C., Uribe, C., & Isaza, C. (2012). Unsupervised feature selection based on fuzzy clustering for fault detection of the Tennessee Eastman process. Advances in artificial intelligence (LNAI 7637 pp. 350 – 360), Springer-Verlag.

  • Bernal de Lázaro, J. M., Llanes-Santiago, O., Prieto Moreno, A., Knupp, D. C., & Silva-Neto, A. J. (2016). Enhanced dynamic approach to improve the detection of small-magnitude faults. Chemical Engineering Science, 146, 166–179.

    Article  Google Scholar 

  • Bernal de Lázaro, J. M., Prieto Moreno, A., Llanes-Santiago, O., & Silva Neto, A. J. (2015). Optimizing kernel methods to reduce dimensionality in fault diagnosis of industrial systems. Computers & Industrial Engineering, 87, 140–149.

    Article  Google Scholar 

  • Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum.

    Book  Google Scholar 

  • Bocaniala, C. D., Costa, J. S. D., & Palade, V. (2005). Fuzzy-based refinement of the fault diagnosis task in industrial devices. Journal of Intelligent Manufacturing, 16(6), 599–614. doi:10.1007/s10845-005-4365-z.

    Article  Google Scholar 

  • Botia, J., Isaza, C., Kempowsky, T., LeLann, M. V., & Aguilar-Martín, J. (2013). Automation based on fuzzy clustering methods for monitoring industrial processes. Engineering Applications of Artificial Intelligence, 26, 1211–1220.

    Article  Google Scholar 

  • Camps Echevarría, L., de Campos Velho, H. F., Becceneri, J. C., Silva Neto, A. J., & Llanes-Santiago, O. (2014a). The fault diagnosis inverse problem with ant colony optimization and ant colony optimization with dispersion. Applied Mathematics and Computation, 227, 687–700.

    Article  Google Scholar 

  • Camps Echevarría, L., Llanes-Santiago, O., Hernández Fajardo, J. A., Silva Neto, A. J., & Jímenez Sánchez, D. (2014b). A variant of the particle swarm optimization for the improvement of fault diagnosis in industrial systems via faults estimation. Engineering Applications of Artificial Intelligence, 28, 36–51.

    Article  Google Scholar 

  • Camps Echevarría, L., Llanes-Santiago, O., & Silva Neto, A. J. (2010). Chap. An approach for Fault diagnosis based on bio-inspired strategies. In: Nature inspired cooperative strategies for optimization (NICSO 2010) (Vol. 284, , pp. 53 – 63), Springer.

  • Chintalapudi, K. K., & Kam, M. (1998). A noise resistant fuzzy c-means algorithm for clustering. IEEE Conference on Fuzzy Systems Proceedings, 2, 1458–1463.

    Google Scholar 

  • Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2008). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501–521.

    Article  Google Scholar 

  • da Silva, A. M., Povinelli, R. J., & Demerdash, N. A. (2008). Induction machine broken bar and stator short-circuit fault diagnostics based on three-phase stator current envelopes. IEEE Transactions on Industrial Electronics, 55, 1310–1318.

    Article  Google Scholar 

  • Dave, R. N. (1991). Characterization and detection of noise in clustering. Pattern Recognition Letters, 12, 657–664.

    Article  Google Scholar 

  • Dave, R. N., & Krishnapuram, R. (1997). Robust clustering methods: A unified view. IEEE Transactions on Fuzzy Systems, 5, 270–293.

    Article  Google Scholar 

  • Díaz, C. A., Echevarría, L. C., Prieto-Moreno, A., Neto, A. J. S., & Llanes-Santiago, O. (2016). A model-based fault diagnosis in a nonlinear bioreactor using an inverse problem approach and evolutionary algorithms. Chemical Engineering Research and Design, 114, 18–29.

    Article  Google Scholar 

  • Ding, S. X. (2008). Model-based fault diagnosis techniques. Berlin: Springer.

    Google Scholar 

  • Ester, M., Kriegel, H., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd ACM SIGKDD (pp. 226 – 231), Portland, Oregon.

  • Fan, J., & Wang, Y. (2014). Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis. Information Sciences, 259, 369–379.

    Article  Google Scholar 

  • García, S., & Herrera, F. (2008). An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. Journal of Machine Learning Research, 9, 2677–2694.

    Google Scholar 

  • García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: A case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristic, 15, 617–644.

    Article  Google Scholar 

  • Gosain, A., & Dahika, S. (2016). Performance analysis of various fuzzy clustering algorithms: A review. In: 7th international conference on communication, computing and virtualization(Vol. 79, pp. 100 – 111).

  • Hou, T. H. T., Liu, W. L., & Lin, L. (2003). Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets. Journal of Intelligent Manufacturing, 14(2), 239–253. doi:10.1023/A:1022911715996.

    Article  Google Scholar 

  • Hu, Q., He, Z., Zhang, Z., & Zi, Y. (2007). Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical Systems and Signal Processing, 21(2), 688–705.

    Article  Google Scholar 

  • Hwang, I., Kim, S., Kim, Y., & Seah, C. (2010). A survey of fault detection, isolation, and reconfiguration methods. IEEE Transactions on Control Systems Technology, 18, 636–656.

    Article  Google Scholar 

  • Isermann, R. (2011). Fault-diagnosis applications: Model-based condition monitoring: Actuators, drives, machinery, plants, sensors and fault-tolerant systems. Berlin: Springer.

    Google Scholar 

  • Jahromi, A. T., Er, M. J., Li, X., & Lim, B. S. (2016). Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis. Neurocomputing, 196, 31–41.

    Article  Google Scholar 

  • Jiang, X. L., Wang, Q., He, B., Chen, S. J., & Li, B. L. (2016). Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints. Neurocomputing, 207, 22–35.

    Article  Google Scholar 

  • Karami, F., Poshtan, J., & Poshtan, M. (2010). Detection of broken rotor bars in induction motors using nonlinear Kalman filters. ISA Transactions, 49, 189–195.

    Article  Google Scholar 

  • Kaur, P. (2011). A density oriented fuzzy c-means clustering algorithm for recognising original cluster shapes from noisy data. International Journal of Innovative Computing and Applications, 3, 77–87.

    Article  Google Scholar 

  • Kaur, P., Soni, A., & Gosain, A. (2013). Robust kernelized approach to clustering by incorporating new distance measure. Engineering Applications of Artificial Intelligence, 26, 833–847.

    Article  Google Scholar 

  • Kesemen, O., Tezel, O., & Ozkul, E. (2016). Fuzzy c-means clustering algorithm for directional data (FCM4DD). Expert Systems with Applications, 58, 76–82.

    Article  Google Scholar 

  • Kourd, Y., Lefebvre, D., & Guersi, N. (2012). FDI with neural network models of faulty behaviours and fault probability evaluation: Application to DAMADICS. In: 8th IFAC symposium on fault detection, supervision and safety of technical processes (SAFEPROCESS) (pp. 744 – 749), August 29–31, 2012.

  • Krishnapuram, R., & Keller, J. M. (1993). A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1, 98–110.

    Article  Google Scholar 

  • Leski, J. M. (2016). Fuzzy c-ordered-means clustering. Fuzzy Sets and Systems, 286, 114–133.

    Article  Google Scholar 

  • Liu, Z., He, Z., Guo, W., & Tang, Z. (2016). A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery. ISA Transactions, 61, 211–220.

    Article  Google Scholar 

  • Liu, Q., & Lv, W. (2009). The study of fault diagnosis based on particle swarm optimization algorithm. Computer and Information Science, 2, 87–91.

    Google Scholar 

  • Li, Y., Yang, G., He, H., Jiao, L., & Shang, R. (2016). A study of large-scale data clustering based on fuzzy clustering. Soft Computing, 20, 3231–3242.

    Article  Google Scholar 

  • Li, C., Zhou, J., Kou, P., & Xiao, J. (2012). A novel chaotic particle swarm optimization based fuzzy clustering algorithm. Neurocomputing, 83, 98–109.

    Article  Google Scholar 

  • Lobato, F., Steffen, F, Jr., & Silva Neto, A. (2009). Solution of inverse radiative transfer problems in two-layer participating media with differential evolution. Inverse Problems in Science and Engineering, 18, 183–195.

    Article  Google Scholar 

  • Luengo, J., García, S., & Herrera, F. (2009). A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests. Expert Systems with Applications, 36, 7798–7808.

    Article  Google Scholar 

  • Pakhira, M., Bandyopadhyay, S., & Maulik, S. (2004). Validity index for crisp and fuzzy clusters. Pattern Recognition, 37, 487–501.

    Article  Google Scholar 

  • Pal, N. R., Pal, K., Keller, J. M., & Bezdek, J. C. (2005). A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems, 13, 517–530.

    Article  Google Scholar 

  • Pang, Y. Y., Zhu, H. P., & Liu, F. M. (2014). Fault diagnosis method based on kpca and selective neural network ensemble. Advanced Materials Research, 915, 1272–1276.

    Article  Google Scholar 

  • Patan, K. (2008). Artificial neural networks for the modelling and fault diagnosis of technical processes. Berlin: Springer.

    Google Scholar 

  • Rodríguez Ramos, A., Domínguez Acosta, C., Rivera Torres, P. J., Serrano Mercado, E. I., Beauchamp Baez, G., Rifón, L. A., et al. (2016). An approach to multiple fault diagnosis using fuzzy logic. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-016-1256-4.

    Google Scholar 

  • Saltos, R., & Weber, R. (2016). A rough-fuzzy approach for support vector clustering. Information Sciences, 339, 353–368.

    Article  Google Scholar 

  • Seera, M., Lim, C. P., Loo, C. K., & Singh, H. (2015). A modified fuzzy min–max neural network for data clustering and its application to power quality monitoring. Applied Soft Computing, 28, 19–29.

    Article  Google Scholar 

  • Sharifi, R., & Ebrahimi, M. (2011). Detection of stator winding faults in induction motors using three-phase current monitoring. ISA Transactions, 50, 14–20.

    Article  Google Scholar 

  • Sina, S., Sadough, Z. N., & Khorasani, K. (2014). Dynamic neural network-based fault diagnosis of gas turbine engines. Neurocomputing, 125, 153–165.

    Article  Google Scholar 

  • Thong, P. H., & Son, L. H. (2016). Picture fuzzy clustering: A new computational intelligence method. Soft Computing, 20, 3549–3562.

    Article  Google Scholar 

  • Thong, P. H., & Son, L. H. (2016). Picture fuzzy clustering for complex data. Engineering Applications of Artificial Intelligence, 56, 121–130.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Vonga, C. M., 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.

    Article  Google Scholar 

  • Wang, J., & Hu, H. (2009). Vibration-based fault diagnosis of pump using fuzzy technique. Measurement, 39, 176–185.

    Article  Google Scholar 

  • Wu, K., & Yang, M. (2005). A cluster validity index for fuzzy clustering. Pattern Recognition, 26, 1275–1291.

    Article  Google Scholar 

  • Xu, Z., Li, Y., Wang, Z., & Xuan, J. (2016). A selective fuzzy artmap ensemble and its application to the fault diagnosis of rolling element bearing. Neurocomputing, 19, 25–35.

    Article  Google Scholar 

  • Yin, S., Ding, S. X., Haghani, A., Hao, H., & Zhang, P. (2012). A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control, 22(9), 1567–1581.

    Article  Google Scholar 

  • Zhang, D. Q., & Chen, S. C. (2003). Clustering incomplete data using kernel based fuzzy c-means algorithm. Neural Process Letters, 18, 155–162.

    Article  Google Scholar 

  • Zhang, D. Q., & Chen, S. C. (2004). A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine, 32, 37–50.

    Article  Google Scholar 

  • Zhang, L., Lu, W., Liu, X., Pedrycz, W., & Zhong, C. (2016). Fuzzy c-means clustering of incomplete data based on probabilistic information granules of missing values. Knowledge-Based Systems, 99, 51–70.

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the financial support provided by FAPERJ, Fundacão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico; CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, research supporting agencies from Brazil; UERJ, Universidade do Estado do Rio de Janeiro and CUJAE, Universidad Tecnológica de La Habana José Antonio Echeverría.

Author information

Authors and Affiliations

  1. Departamento de Automática y Computación, Universidad Tecnológica de la Habana “José Antonio Echeverría” (CUJAE), calle 114 No. 11901, CUJAE, Marianao, CP 19390, Havana, Cuba

    Adrián Rodríguez Ramos, José M. Bernal de Lázaro, Alberto Prieto-Moreno & Orestes Llanes-Santiago

  2. Instituto Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ), Rua Bonfim, 25 - Campus UERJ, Nova Friburgo, RJ, 28625-570, Brazil

    Antônio José da Silva Neto

Authors
  1. Adrián Rodríguez Ramos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José M. Bernal de Lázaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alberto Prieto-Moreno
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antônio José da Silva Neto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Orestes Llanes-Santiago
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Orestes Llanes-Santiago.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rodríguez Ramos, A., Bernal de Lázaro, J.M., Prieto-Moreno, A. et al. An approach to robust fault diagnosis in mechanical systems using computational intelligence. J Intell Manuf 30, 1601–1615 (2019). https://doi.org/10.1007/s10845-017-1343-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-017-1343-1

Keywords

Search

Navigation