Skip to main content
SpringerLink
Log in

Fault diagnosis of planetary gearbox with incomplete information using assignment reduction and flexible naive Bayesian classifier

  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

In planetary gearbox operation, there are many uncertain factors that may result in incomplete diagnostic information, such as measurement instrument faults, limitation of transmission capacity, and data processing. Therefore, it has been one of the greatest obstacles to fault diagnosis of planetary gearbox. To address this issue, a novel fault diagnosis method of planetary gearbox with incomplete information using assignment reduction and Flexible naive Bayesian classifier (FNBC) is proposed. Characteristic relation was utilized to preprocess incomplete diagnostic information. Then, assignment reduction algorithm based on characteristic relation was used to remove irrelevant or redundant condition attribute values. Finally, FNBC was constructed to reason diagnosis results. To validate the performance of the proposed method, a fault diagnosis experiment was conducted. The experimental studies demonstrate the proposed method can be utilized to diagnose planetary gearbox faults with incomplete diagnostic information, reduce computational complexity, and enhance reasoning accuracy.

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

Similar content being viewed by others

References

  1. Y. G. Lei, J. Lin, M. J. Zuo and Z. J. He, Condition monitoring and fault diagnosis of planetary gearboxes: A review, Measurement, 48 (1) (2014) 292–305.

    Article  Google Scholar 

  2. X. W. Chen and Z. P. Feng, Iterative generalized timefrequency reassignment for planetary gearbox fault diagnosis under nonstationary conditions, Mechanical Systems and Signal Processing, 80 (2016) 429–444.

    Article  Google Scholar 

  3. Y. J. Park, J. G. Kim, G. H. Lee and S. B. Shim, Load sharing and distributed on the gear flank of wind turbine planetary gearbox, J. of Mechanical Science and Technology, 29 (1) (2015) 309–316.

    Article  Google Scholar 

  4. Z. Cheng, N. Q. Hu and X. F. Zhang, Crack level estimation approach for planetary gearbox based on simulation signal and GRA, J. of Sound and Vibration, 331 (26) (2012) 5853–5863.

    Article  Google Scholar 

  5. X. H. Liang, H. S. Zhang, L. B. Liu and M. J. Zuo, The influence of tooth pitting on the mesh stiffness of a pair of external spur gears, Mechanism and Machine Theory, 106 (2016) 1–15.

    Article  Google Scholar 

  6. J. S. Nam, Y. J. Park, J. K. Kim, J. W. Han, Y. Y. Nam and G. H. Lee, Application of similarity theory to load capacity of gearboxes, J. of Mechanical Science and Technology, 28 (8) (2014) 3033–3040.

    Article  Google Scholar 

  7. Y. Gui, Q. K. Han and F. L. Chu, A vibration model for fault diagnosis of planetary gearboxes with localized planet bearing defects, J. of Mechanical Science and Technology, 30 (9) (2016) 4109–4119.

    Article  Google Scholar 

  8. M. Khazaee, H. Ahmadi, M. Omid and A. Moosavian, Feature-level fusion based on wavelet transform and artificial neural network for fault diagnosis of planetary gearbox using acoustic and vibration signals, Insight, 55 (6) (2013) 323–330.

    Article  Google Scholar 

  9. P. D. Samuel and D. J. Pines, Classifying helicopter gearbox faults using a normalized energy metric, Smart Materials and Structures, 10 (1) (2001) 145–153.

    Article  Google Scholar 

  10. M. Khazaee, H. Ahmadi, M. Omid, A. Moosavian and M. Khazaee, Vibration condition monitoring of planetary gears based on decision level data fusion using Dempster-Shafer theory of evidence, J. of Vibroengineering, 14 (2) (2012) 838–851.

    Google Scholar 

  11. M. Khazaee, H. Ahmadi, M. Omid, A. Moosavian and M. Khazaee, Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster-Shafer evidence theory, Proceedings of the Institution of Mechanical Engineers, Part E: J. of Process Mechanical Engineering, 228 (1) (2014) 21–32.

    Article  Google Scholar 

  12. J. Qu, Z. Liu, M. J. Zuo and H. Z. Huang, Feature selection for damage degree classification of planetary gearboxes using support vector machine, Proceedings of the Institution of Mechanical Engineers, Part C: J. of Mechanical Engineering Science, 225 (C9) (2011) 2259–2264.

    Google Scholar 

  13. M. Khazaee, H. Ahmadi, M. Omid and A. Moosavian, An appropriate approach for condition monitoring of planetary gearbox based on fast Fourier transform and least-square support vector machine, International J. of Multidisciplinary Sciences and Engineering, 3 (5) (2012) 22–26.

    Google Scholar 

  14. Z. L. Liu, M. J. Zuo and H. B. Xu, Feature ranking for support vector machine classification and its application to machinery fault diagnosis, Proceedings of the Institution of Mechanical Engineers, Part C: J. of Mechanical Engineering Science, 227 (9) (2013) 2077–2089.

    Google Scholar 

  15. Y. G. Lei, Z. Y. Liu, X. H. Wu, N. P. Li, W. Chen and J. Lin, Health condition identification of multi-stage planetary gearboxes using an mRVM-based method, Mechanical Systems and Signal Processing, 60–61 (2015) 289–300.

    Article  Google Scholar 

  16. H. L. Dong, Z. D. Wang, X. M. Chen and H. J. Gao, A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information, Mathematical Problems in Engineering, 416358 (2012).

    Google Scholar 

  17. H. F. Tang, J. Chen and G. M. Dong, Sparse representation based latent components analysis for machinery weak fault detection, Mechanical Systems and Signal Processing, 46 (2) (2014) 373–388.

    Article  Google Scholar 

  18. B. Walczak and D. L. Massart, Rough set theory, Chemometrics and Intelligent Laboratory Systems, 47 (1) (1999) 1–16.

    Article  Google Scholar 

  19. J. W. Grzymala-Busse, Characteristic relations for incomplete data: a generalization of the indiscernibility relation, Proceedings of the 4thInternational Conference on Rough Sets and Current Trends in Computing, Uppsala, Sweden (2004) 244–253.

    Chapter  Google Scholar 

  20. J. W. Grzymala-Busse and Z. S. Hippe, Ming incomplete data—A rough set approach, Proceedings of the International Forum on Knowledge Technology (IFKT2008), Chongqing, China (2008) 49–74.

    Google Scholar 

  21. M. Wang, N. Q. Hu and G. J. Qin, A method for rule extraction based on granular computing: Application in the fault diagnosis of a helicopter transmission system, J. of Intelligent and Robotic Systems, 71 (3–4) (2013) 445–455.

    Article  Google Scholar 

  22. J. W. Grzymala-Busse, P. G. Clark and M. Kuehnhausen, Generalized probabilistic approximations of incomplete data, International J. of Approximate Reasoning, 55 (1) (2014) 180–196.

    Article  MathSciNet  MATH  Google Scholar 

  23. J. W. Grzymala-Busse and S. Siddhaye, Rough set approaches to rule induction from incomplete data, Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Perugia, Italy (2004) 923–930.

    Google Scholar 

  24. Z. Q. Meng and Z. Z. Shi, Extended rough set-based attribute reduction in inconsistent incomplete decision systems, Information Sciences, 204 (2012) 44–69.

    Article  MathSciNet  Google Scholar 

  25. M. Kryszkiewicz, Comparative study of alternative types of knowledge reduction in inconsistent systems, International J. of Intelligent Systems, 16 (2001) 105–120.

    Article  MATH  Google Scholar 

  26. M. Li, S. B. Deng, S. Z. Feng and J. P. Fan, Fast assignment reduction in inconsistent incomplete decision systems, J. of Systems Engineering and Electronics, 25 (1) (2014) 83–94.

    Article  Google Scholar 

  27. Y. He, D. Chen, G. Sun and J. Han, Dictionary evaluation and optimization for sparse coding based speech processing, Information Sciences, 310 (2015) 77–96.

    Article  MathSciNet  Google Scholar 

  28. Y. He, G. Sun and J. Han, Optimization of learned dictionary for sparse coding in speech processing, Neurocomputing, 173 (2016) 471–482.

    Article  Google Scholar 

  29. J. Yu, W. T. Huang and X. Z. Zhao, Combined flow graphs and normal naive Bayesian classifier for fault diagnosis of gear box, Proceedings of the Institution of Mechanical Engineers, Part C: J. of Mechanical Engineering Science, 230 (2) (2016) 303–313.

    Article  Google Scholar 

  30. G. H. John and P. Langley, Estimating continuous distributions in Bayesian classifiers, Proceedings of 7thConference on Uncertainty in Artificial Intelligent, Montréal, Qué, Canada (1995) 338–345.

    Google Scholar 

  31. Y. L. He, R. Wang, S. Kwong and X. Z. Wang, Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis, Information Sciences, 259 (2014) 252–268.

    Article  MathSciNet  MATH  Google Scholar 

  32. L. M. Wang and H. Y. Zhao, Learning a flexible kdependence bayesian classifier from the chain rule of joint probability distribution, Entropy, 17 (6) (2015) 3766–3786.

    Article  MathSciNet  MATH  Google Scholar 

  33. X. Z. Wang, Y. L. He and D. D. Wang, Non-naive Bayesian classifiers for classification problems with continuous attributes, IEEE Transaction on Cybernetics, 44 (1) (2014) 21–39.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Automation, Harbin University of Science and Technology, Harbin, 150080, China

    Jun Yu, Mingyou Bai & Guannan Wang

  2. School of Mechanical and Electrical Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Jun Yu

  3. School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China

    Xianjiang Shi

Authors
  1. Jun Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingyou Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guannan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xianjiang Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Yu.

Additional information

Recommended by Associate Editor Byeng Dong Youn

Jun Yu received his M.S. and Ph.D. in Mechanical and Electrical Engineering from Harbin Institute of Technology, in China, in 2009 and 2017, respectively. He has been working in the School of Harbin University of Science and Technology. His main research interests include mechanical system fault diagnosis, knowledge discovery and data mining.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, J., Bai, M., Wang, G. et al. Fault diagnosis of planetary gearbox with incomplete information using assignment reduction and flexible naive Bayesian classifier. J Mech Sci Technol 32, 37–47 (2018). https://doi.org/10.1007/s12206-017-1205-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-017-1205-y

Keywords

Search

Navigation