Motor Fault Detection and Diagnosis Based on a Meta-cognitive Random Vector Functional Link Network

  • Choiru Za’in
  • Mahardhika Pratama
  • Mukesh Prasad
  • Deepak Puthal
  • Chee Peng Lim
  • Manjeevan Seera


Accurate prediction of faults before they occur is vital because the intricate, uncertain, and intercorrelated natures of industrial processes can lead to multiple component failures or to a complete shutdown of the overall prediction cycle. While the first principle-based fault detection approach demands significant expert knowledge and is component-specific, learning-based approaches offer a plausible alternative because of their learning capability of offline data. Learning-based fault detection and diagnosis still deserve in-depth investigation because current approaches must happen offline, are static, and must be supervised; this makes them hardly applicable for the live scenarios of industrial processes. This chapter proposes a novel approach using an evolving type-2 random vector functional link network, which combines the meta-cognitive learning concept with the random vector functional link theory. The efficacy of evolving type-2 random vector functional link networks was validated with an experimental study on diagnosing different fault conditions of induction motors – namely broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems – using a laboratory-scale test rig. Our algorithm was compared with other prominent algorithms and was found to deliver state-of-the-art performance in terms of accuracy, simplicity, and scalability.


Faulty detection MCSA Meta-cognitive learning Random vector functional link Randomized neural network Hybrid system 


  1. 1.
    Montanari, M., Peresada, S. M., Rossi, C., & Tilli, A. (2007). Speed sensorless control of induction motors based on a reduced-order adaptive observer. IEEE Transactions on Control Systems Technology, 15, 1049–1064.CrossRefGoogle Scholar
  2. 2.
    CusidÓ, J., Romeral, L., Ortega, J. A., Rosero, J. A., & Espinosa, A. G. (2008). Fault detection in induction machines using power spectral density in wavelet decomposition. IEEE Transactions on Industrial Electronics, 55, 633–643.CrossRefGoogle Scholar
  3. 3.
    Benbouzid, M. E. H. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics, 47, 984–993.CrossRefGoogle Scholar
  4. 4.
    Toubakh, H., & Sayed-Mouchaweh, M. (2016). Hybrid dynamic classifier for drift-like fault diagnosis in a class of hybrid dynamic systems: Application to wind turbine converters. Neurocomputing, 171, 1496–1516.CrossRefGoogle Scholar
  5. 5.
    Traore, M., Duviella, E., & Lecoeuche, S. (2009). Comparison of two prognosis methods based on neuro fuzzy inference system and clustering neural network. IFAC Proceedings Volumes, 42, 1468–1473.CrossRefGoogle Scholar
  6. 6.
    Sayed-Mouchaweh, M., & Lughofer, E. (2012). Learning in non-stationary environments: Methods and applications. New York: Springer.CrossRefzbMATHGoogle Scholar
  7. 7.
    Bangalore, P., & Tjernberg, L. B. (2015). An artificial neural network approach for early fault detection of gearbox bearings. IEEE Transactions on Smart Grid, 6, 980–987.CrossRefGoogle Scholar
  8. 8.
    Martin, T. (2005). Fuzzy sets in the fight against digital obesity. Fuzzy Sets and Systems, 156, 411–417.MathSciNetCrossRefGoogle Scholar
  9. 9.
    López, V., del Río, S., Benítez, J. M., & Herrera, F. (2015). Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data. Fuzzy Sets and Systems, 258, 5–38.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Broomhead, D. S., & Lowe, D. (1988). Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal Signals and Radar Establishment Malvern (UK).Google Scholar
  11. 11.
    Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281–305.MathSciNetzbMATHGoogle Scholar
  12. 12.
    Chen, C. P., & Wan, J. Z. (1999). A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29, 62–72.CrossRefGoogle Scholar
  13. 13.
    Rong, H.-J., Ong, Y.-S., Tan, A.-H., & Zhu, Z. (2008). A fast pruned-extreme learning machine for classification problem. Neurocomputing, 72, 359–366.CrossRefGoogle Scholar
  14. 14.
    Deng, Z., Choi, K.-S., Cao, L., & Wang, S. (2014). T2fela: Type-2 fuzzy extreme learning algorithm for fast training of interval type-2 TSK fuzzy logic system. IEEE Transactions on Neural Networks and Learning Systems, 25, 664–676.CrossRefGoogle Scholar
  15. 15.
    Feng, G., Huang, G.-B., Lin, Q., & Gay, R. (2009). Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks, 20, 1352–1357.CrossRefGoogle Scholar
  16. 16.
    Mirza, B., Lin, Z., & Liu, N. (2015). Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift. Neurocomputing, 149, 316–329.CrossRefGoogle Scholar
  17. 17.
    Pratama, M., Zhang, G., Er, M. J., & Anavatti, S. (2017). An incremental type-2 meta-cognitive extreme learning machine. IEEE Transactions on Cybernetics, 47, 339–353.Google Scholar
  18. 18.
    Subramanian, K., Suresh, S., & Sundararajan, N. (2013). A metacognitive neuro-fuzzy inference system (McFIS) for sequential classification problems. IEEE Transactions on Fuzzy Systems, 21, 1080–1095.CrossRefGoogle Scholar
  19. 19.
    Millan-Almaraz, J. R., Romero-Troncoso, R., Contreras-Medina, L. M., & Garcia-Perez, A. (2008). Embedded FPGA based induction motor monitoring system with speed drive fed using multiple wavelet analysis. In international symposium on industrial embedded systems, 2008. SIES 2008 (pp. 215–220).Google Scholar
  20. 20.
    Seera, M., Lim, C. P., Ishak, D., & Singh, H. (2012). Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM–CART model. IEEE Transactions on Neural Networks and Learning Systems, 23, 97–108.CrossRefGoogle Scholar
  21. 21.
    Douglas, H., Pillay, P., & Ziarani, A. (2005). Broken rotor bar detection in induction machines with transient operating speeds. IEEE Transactions on Energy Conversion, 20, 135–141.CrossRefGoogle Scholar
  22. 22.
    Penman, J., Sedding, H., Lloyd, B., & Fink, W. (1994). Detection and location of interturn short circuits in the stator windings of operating motors. IEEE Transactions on Energy Conversion, 9, 652–658.CrossRefGoogle Scholar
  23. 23.
    Cameron, J., Thomson, W., & Dow, A. (1986). Vibration and current monitoring for detecting airgap eccentricity in large induction motors. In IEEE proceedings B (electric power applications) (pp. 155–163).Google Scholar
  24. 24.
    Dorrell, D. G., Thomson, W. T., & Roach, S. (1997). Analysis of airgap flux, current, and vibration signals as a function of the combination of static and dynamic airgap eccentricity in 3-phase induction motors. IEEE Transactions on Industry Applications, 33, 24–34.CrossRefGoogle Scholar
  25. 25.
    Rodríguez, P. V. J., Negrea, M., & Arkkio, A. (2008). A simplified scheme for induction motor condition monitoring. Mechanical Systems and Signal Processing, 22, 1216–1236.CrossRefGoogle Scholar
  26. 26.
    Arabacı, H., & Bilgin, O. (2010). Automatic detection and classification of rotor cage faults in squirrel cage induction motor. Neural Computing and Applications, 19, 713–723.CrossRefGoogle Scholar
  27. 27.
    Lau, E. C., & Ngan, H. (2010). Detection of motor bearing outer raceway defect by wavelet packet transformed motor current signature analysis. IEEE Transactions on Instrumentation and Measurement, 59, 2683–2690.CrossRefGoogle Scholar
  28. 28.
    Abe, S., & Lan, M.-S. (1995). A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Transactions on Fuzzy Systems, 3, 18–28.CrossRefGoogle Scholar
  29. 29.
    Palmero, G. S., Santamaria, J. J., de la Torre, E. M., & González, J. P. (2005). Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system. Engineering Applications of Artificial Intelligence, 18, 867–874.CrossRefGoogle Scholar
  30. 30.
    Yang, B.-S., Oh, M.-S., & Tan, A. C. C. (2009). Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Systems with Applications, 36, 1840–1849.CrossRefGoogle Scholar
  31. 31.
    Sii, H. S., Ruxton, T., & Wang, J. (2001). A fuzzy-logic-based approach to qualitative safety modelling for marine systems. Reliability Engineering & System Safety, 73, 19–34.CrossRefGoogle Scholar
  32. 32.
    Brotherton, T., Chadderdon, G., & Grabill, P. (1999). Automated rule extraction for engine vibration analysis. In 1999 IEEE aerospace conference proceedings (pp. 29–38).Google Scholar
  33. 33.
    Sadeghian, A., Ye, Z., & Wu, B. (2009). Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Transactions on Instrumentation and Measurement, 58, 2253–2263.CrossRefGoogle Scholar
  34. 34.
    Lee, S.-h., Wang, Y.-q., & Song, J.-i. (2010). Fourier and wavelet transformations application to fault detection of induction motor with stator current. Journal of Central South University of Technology, 17, 93–101.CrossRefGoogle Scholar
  35. 35.
    Liu, X., Ma, L., & Mathew, J. (2009). Machinery fault diagnosis based on fuzzy measure and fuzzy integral data fusion techniques. Mechanical Systems and Signal Processing, 23, 690–700.CrossRefGoogle Scholar
  36. 36.
    Ondel, O., Boutleux, E., Clerc, G., & Blanco, E. (2008). FDI based on pattern recognition using Kalman prediction: Application to an induction machine. Engineering Applications of Artificial Intelligence, 21, 961–973.CrossRefGoogle Scholar
  37. 37.
    Adjallah, K. H., Han, T., Yang, B.-S., & Yin, Z.-J. (2007). Feature-based fault diagnosis system of induction motors using vibration signal. Journal of Quality in Maintenance Engineering, 13, 163–175.CrossRefGoogle Scholar
  38. 38.
    Jang, J.-S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665–685.CrossRefGoogle Scholar
  39. 39.
    Patra, J. C., & Kot, A. C. (2002). Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 32, 505–511.CrossRefGoogle Scholar
  40. 40.
    Savitha, R., Suresh, S., & Kim, H. J. (2014). A meta-cognitive learning algorithm for an extreme learning machine classifier. Cognitive Computation, 6, 253–263.CrossRefGoogle Scholar
  41. 41.
    Lughofer, E., & Buchtala, O. (2013). Reliable all-pairs evolving fuzzy classifiers. IEEE Transactions on Fuzzy Systems, 21, 625–641.CrossRefGoogle Scholar
  42. 42.
    Pratama, M., Er, M. J., Anavatti, S. G., Lughofer, E., Wang, N., & Arifin, I. (2014). A novel meta-cognitive-based scaffolding classifier to sequential non-stationary classification problems. In 2014 IEEE international conference on fuzzy systems (FUZZ-IEEE) (pp. 369–376).Google Scholar
  43. 43.
    Pratama, M., Anavatti, S. G., & Lughofer, E. (2014). GENEFIS: Toward an effective localist network. IEEE Transactions on Fuzzy Systems, 22, 547–562.CrossRefGoogle Scholar
  44. 44.
    Han, H., Wu, X.-L., & Qiao, J.-F. (2014). Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm. IEEE Transactions on Cybernetics, 44, 554–564.CrossRefGoogle Scholar
  45. 45.
    Mitra, P., Murthy, C., & Pal, S. K. (2002). Unsupervised feature selection using feature similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 301–312.CrossRefGoogle Scholar
  46. 46.
    Oentaryo, R. J., Pasquier, M., & Quek, C. (2011). RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction. Expert Systems with Applications, 38, 12066–12084.CrossRefGoogle Scholar
  47. 47.
    Yu, L., & Liu, H. (2004). Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5, 1205–1224.MathSciNetzbMATHGoogle Scholar
  48. 48.
    Zhang, R., Lan, Y., Huang, G.-B., & Xu, Z.-B. (2012). Universal approximation of extreme learning machine with adaptive growth of hidden nodes. IEEE Transactions on Neural Networks and Learning Systems, 23, 365–371.CrossRefGoogle Scholar
  49. 49.
    Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70, 489–501.CrossRefGoogle Scholar
  50. 50.
    Huang, G.-B., Chen, L., & Siew, C. K. (2006). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17, 879–892.CrossRefGoogle Scholar
  51. 51.
    Angelov, P. P., & Filev, D. P. (2004). An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34, 484–498.CrossRefGoogle Scholar
  52. 52.
    Angelov, P., & Filev, D. (2005). Simpl_eTS: A simplified method for learning evolving Takagi-Sugeno fuzzy models. In The 14th IEEE international conference on fuzzy systems, 2005. FUZZ’05 (pp. 1068–1073).Google Scholar
  53. 53.
    Wu, S., & Er, M. J. (2000). Dynamic fuzzy neural networks-a novel approach to function approximation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 30, 358–364.Google Scholar
  54. 54.
    Wang, N., Er, M. J., & Meng, X. (2009). A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks. Neurocomputing, 72, 3818–3829.CrossRefGoogle Scholar
  55. 55.
    Zadeh L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning–1. Inf. Sci., 8, 199–249.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Choiru Za’in
    • 1
  • Mahardhika Pratama
    • 2
  • Mukesh Prasad
    • 3
  • Deepak Puthal
    • 4
  • Chee Peng Lim
    • 5
  • Manjeevan Seera
    • 6
  1. 1.School of Engineering and Mathematical Science, La Trobe UniversityMelbourneAustralia
  2. 2.School of Computer Science and Engineering, Nanyang Technological UniversitySingaporeSingapore
  3. 3.Centre for Artificial Intelligence, School of Software, FEIT, University of Technology SydneyUltimoAustralia
  4. 4.School of Electrical and Data Engineering, FEIT, University of Technology SydneyUltimoAustralia
  5. 5.Institute of Intelligent System Research and Innovation, Deakin UniversityGeelongAustralia
  6. 6.Swinburne University of TechnologyKuchingMalaysia

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