An Approach to Reducing Input Parameter Volume for Fault Classifiers

  • Ann SmithEmail author
  • Fengshou Gu
  • Andrew D. Ball
Open Access
Research Article


As condition monitoring of systems continues to grow in both complexity and application, an overabundance of data is amassed. Computational capabilities are unable to keep abreast of the subsequent processing requirements. Thus, a means of establishing computable prognostic models to accurately reflect process condition, whilst alleviating computational burdens, is essential. This is achievable by restricting the amount of information input that is redundant to modelling algorithms. In this paper, a variable clustering approach is investigated to reorganise the harmonics of common diagnostic features in rotating machinery into a smaller number of heterogeneous groups that reflect conditions of the machine with minimal information redundancy. Naïve Bayes classifiers established using a reduced number of highly sensitive input parameters realised superior classification powers over higher dimensional classifiers, demonstrating the effectiveness of the proposed approach. Furthermore, generic parameter capabilities were evidenced through confirmatory factor analysis. Parameters with superior deterministic power were identified alongside complimentary, uncorrelated, variables. Particularly, variables with little explanatory capacity could be eliminated and lead to further variable reductions. Their information sustainability is also evaluated with Naïve Bayes classifiers, showing that successive classification rates are sufficiently high when the first few harmonics are used. Further gains were illustrated on compression of chosen envelope harmonic features. A Naïve Bayes classification model incorporating just two compressed input variables realised an 83.3% success rate, both an increase in classification rate and an immense improvement volume-wise on the former ten parameter model.


Fault diagnosis classification variable clustering data compression big data 


  1. [1]
    R. Barron. Engineering Condi tion Moni toring: Practice, Methods and Applications, Essex, UK: Add ison Wesley Longman, 1996.Google Scholar
  2. [2]
    A. Bhattacharya, P. K. Dan. Recent trend in condition monitoring for equipment fault diagnosis. International Journal of System Assurance Engineering and Management, vol. 5, no. 3, pp. 230–244, 2014. DOI: 10.1007/s13198–013–0151–z.CrossRefGoogle Scholar
  3. [3]
    E. P. Carden, P. Fanning. Vibration based condition monitoring: A review. Structural Health Monitoring, vol. 3, no. 4, pp. 355–377, 2004. DOI: 10.1177/1475921704047500.CrossRefGoogle Scholar
  4. [4]
    A. Davies. Handbook of Condition Monitoring: Techniques and Methodology, London, UK: Chapman and Hall, 1998.CrossRefGoogle Scholar
  5. [5]
    B. Flury, H. Riedwyl. Multivariate Statistics: A Practical Approach, London, UK: Chapman and Hall, 1988.CrossRefzbMATHGoogle Scholar
  6. [6]
    L. H. Chiang, E. L. Russell, R. D. Braatz. Fault diagnosis in chemical processes using fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometrics and Intelligent Laboratory Systems, vol. 50, no. 2, pp. 243–252, 2000. DOI: 10.1016/S0169–7439(99)00061–1.CrossRefGoogle Scholar
  7. [7]
    Y. Zhang, C. M. Bingham, M. Gallimore. Fault detection and diagnosis based on extensions of PCA. Advances in Military Technology, vol. 8, no. 2, pp. 27–41, 2013.Google Scholar
  8. [8]
    B. R. Bakshi. Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal, vol. 44, no. 7, pp. 1596–1610, 1998. DOI: 10.1002/aic. 690440712.CrossRefGoogle Scholar
  9. [9]
    R. P. Shao, W. T. Hu, Y. Y. Wang, X. K. Qi. The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform. Measurement, vol. 54, pp. 118–132, 2014. DOI: 10.1016/j.measurement. 2014.04.016.CrossRefGoogle Scholar
  10. [10]
    Y. W. Zhang, S. Li, Z. Y. Hu. Improved multi–scale kernel principal component analysis and its application for fault detection. Chemical Engineering Research and Design, vol. 90, no. 9, pp. 1271–1280, 2012. DOI: 10.1016/j.cherd. 2011.11.015.CrossRefGoogle Scholar
  11. [11]
    V. T. Tran, F. Al Thobiani, A. Ball. An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks. Expert Systems with Applications, vol. 41, no. 9, pp. 4113–4122, 2014. DOI: 10.1016/j.eswa.2013.12.026.CrossRefGoogle Scholar
  12. [12]
    Q. Qin, Z. N. Jiang, K. N. Feng, W. He. A novel scheme for fault detection of reciprocating compressor valves based on basis pursuit, wave matching and support vector machine. Measurement, vol. 45, no. 5, pp. 897–908, 2012. DOI: 10.1016/j.measurement.2012.02.005.CrossRefGoogle Scholar
  13. [13]
    A. Smith, F. S. Gu, A. Ball. Maintaining model efficiency, avoiding bias and reducing input parameter volume in compressor fault classification. In Proceedings of the 7th International Conference on Mechanical and Aerospace Engineering, London, UK, pp. 196–201, 2016. DOI: 10.1109/ICMAE.2016.7549534.Google Scholar
  14. [14]
    R. Hassan, B. Cohanim, O. De Weck, G. Vente. A comparison of particle swarm optimization and the genetic algorithm. In Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Structures, Structural Dynamics, and Materials and Co–located Conferences, Austin, USA, pp. 18–21, 2005. DOI: 10.2514/6.2005–1897.Google Scholar
  15. [15]
    Y. H. Lin, W. S. Lee, C. Y. Wu. Automated fault classification of reciprocating compressors from vibration data: a case study on optimization using genetic algorithm. Procedia Engineering, vol. 79, pp. 355–361, 2014. DOI: 10.1016/j.proeng.2014.06.355.CrossRefGoogle Scholar
  16. [16]
    V. R. Rao, V. D. Kalyankar. Parameter optimization of modern machining processes using teaching–learningbased optimization algorithm. Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 524–531, 2013. DOI: 10.1016/j.engappai.2012.06.007.CrossRefGoogle Scholar
  17. [17]
    B. Samanta, C. Nataraj. Use of particle swarm optimization for machinery fault detection. Engineering Applications of Artificial Intelligence, vol. 22, no. 2, pp. 308–316, 2009. DOI: 10.1016/j.engappai.2008.07.006.CrossRefGoogle Scholar
  18. [18]
    M. Ahmed, A. Smith, F. Gu, A. D. Ball. Fault diagnosis of reciprocating compressors using revelance vector machines with a genetic algorithm based on vibration data. In Proceedings of the 20th International Conference on Automation and Computing, Cranfield, UK, 2014. DOI: 10.1109/IConAC.2014.6935480.Google Scholar
  19. [19]
    M. E. Tipping. Sparse Bayesian learning and the relevance vector machine. The Journal of Machine Learning Research, vol. 1, pp. 211–244, 2001. DOI: 10.1162/15324430152748236.MathSciNetzbMATHGoogle Scholar
  20. [20]
    A. C. Faul, M. E. Tippingd. Analysis of sparse Bayesian learning. In Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, MIT Press, Vancouver, Canada, pp.383–389, 2001.Google Scholar
  21. [21]
    S. X. Ding. Model–based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools, London, UK: Springer, 2008.Google Scholar
  22. [22]
    F. S. Gu, B. Payne, A. D. Ball. Instantaneous Angular Speed Signature Extraction through Hilbert Transform and Fourier Transform, Technical Report: MERG–0402, Manchester University, UK, 2002.Google Scholar
  23. [23]
    Y. G. Lei, J. Lin, M. J. Zuo, Z. J. He. Condition monitoring and fault diagnosis of planetary gearboxes: A review. Measurement, vol.48, pp. 292–305, 2014. Doi: 10.1016/j. measurement.2013.11.012.CrossRefGoogle Scholar
  24. [24]
    G. J. Feng, J. Gu, D. Zhen, M. Aliwan, F. S. Gu, A. D. Ball. Implementation of envelope analysis on a wireless condition monitoring system for bearing fault diagnosis. International Journal of Automation and Computing, vol. 12, no. 1, pp. 14–24, 2015. D 이: 10.1007/s11633–014–0862–x.CrossRefGoogle Scholar
  25. [25]
    M. Elhaj, F. Gu, J. Shi, A. A. Ball. A comparison of the condition monitoring of reciprocating compressor valves using vibration, acoustic, temperature and pressure measurements. In Proceedings of the 6th Annual Maintenance and Reliability Conference, Gatlinburg, Tennessee, USA, 2001.Google Scholar
  26. [26]
    M. Elhaj, F. Gu, A. Ball. Numerical simulation study of a two stage reciprocating compressor for condition monitoring. In Proceedings of the 17th International Congress on Condi tion Moni toring and Diagnostic Engineering Management, Cambridge, UK, pp. 602–611, 2004.Google Scholar
  27. [27]
    C. Chatfield, A. J. Collins. Introduction to Multivariate Analysis, London, UK: Chapman and Hall, 1980.CrossRefzbMATHGoogle Scholar
  28. [28]
    B. S. Everitt, G. Dunn. Applied Multivariate Data Analysis, 2nd ed., London, UK: Arnold, 2001.CrossRefzbMATHGoogle Scholar
  29. [29]
    R. A. Johnson, D. W. Wichern. Applied Multivariate Statistical Analysis, 5th ed., Upper Saddle River, USA: Prentice Hall, 2002.Google Scholar
  30. [30]
    B. F. J. Manly. Multivariate Statistical Methods: A Primer, 3rd ed., Chatfield and Collins, London, UK, 1986.Google Scholar
  31. [31]
    S. J. Chapman. Essentials of Matlab Programming, 2nd ed., Stamford, USA: Cengage Learning, 2009.Google Scholar
  32. [32]
    S. J. Chapman. Matlab Programming with Applications for Engineers, Stamford, USA: Cengage Learning, 2013.Google Scholar
  33. [33]
    L. Breiman, J. H. Friedman, C. J. Stone, R. A. Olshen. Classification and Regression Trees, New York, USA: Chapman and Hall, 1993.Google Scholar
  34. [34]
    SAS Institute. SAS user's guide: statistics (vol. 1 and 2). Cary, USA: SAS Institute, 1985.Google Scholar
  35. [35]
    H. P. Bloch, J. J. Hoefner. Reciprocating Compressors: Operation and Maintenance, Houston, USA: Gulf Pub. Co., 1996.Google Scholar
  36. [36]
    R. Isermann. Fault–diagnosis Applications: Model–based condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault–tolerant Systems, Berlin Heidelberg: Germany: Springer, 2011. DOI: 10.1007/978–3–642–12767–0.zbMATHGoogle Scholar
  37. [37]
    R. Isermann. Fault–diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Berlin Heidelberg, Germany: Springer, 2006. DOI: 10.1007/3–540–30368–5.CrossRefGoogle Scholar
  38. [38]
    G. de Botton, J. Ben–Ari, E. Sher. Vibration monitoring as a predictive maintenance tool for reciprocating engines. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 214, no. 8, pp. 895–903, 2000. DOI: 10.1177/095440700021400808.Google Scholar
  39. [39]
    R. R. Bond. Vibration–based Condition Monitoring: Industrial, Aerospace and Automotive Applications, Wiley, London, UK, 2010.Google Scholar
  40. [40]
    D. J. Murray–Smith. Modelling and Simulation of Integrated Systems in Engineering: Issues of Methodology, Quality, Testing and Application, Cambridge, UK: Woodhead, 2012.CrossRefGoogle Scholar
  41. [41]
    J. Osarenren. Integrated Rel iab ili ty: Condi tion Moni toring and Maintenance of Equipment, Boca Raton, USA: CRC Press, Taylor and Francis Group, 2015.Google Scholar
  42. [42]
    J. S. Rao. Vi bratory Condi tion Moni toring of Machines, Alpha Science International Ltd, Oxford, UK, 2000.Google Scholar
  43. [43]
    J. R. Kolodziej, J. N. Trout. An image–based pattern recognition approach to condition monitoring of reciprocating compressor valves. Journal of Vibration and Control, vol. 24, no. 19, pp. 4433–4448, 2018. DOI: 10.1177/10775463 17726453.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Centre for Efficiency and Performance EngineeringUniversity of HuddersfieldHuddersfieldUK

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