A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis | Journal of Intelligent Manufacturing Skip to main content
Springer Nature Link
Log in

A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis

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

Abstract

Fault diagnosis of mechanical components has been attracting increasing attention. Researches have been carried out to reduce unnecessary breakdowns of machinery. Signal processing approaches are the most commonly used techniques for fault diagnosis tasks. Ontology and semantic web technology have great potential in knowledge representing, organizing and utilizing. In this paper, a hybrid fault diagnosis method for mechanical components is proposed based on ontology and signal analysis (HOS-MCFD). The method is a systematic approach covering the whole process of fault diagnosis: feature extraction from raw data, fault phenomenon identification using continuous mixture Gaussian hidden Markov model and fault knowledge modeling and reasoning using ontology and semantic web technology. A semantic mapping approach is presented to relate signal analysis results to ontology elements. The hybrid method integrates the advantages of signal analysis and ontology. It can be applied to deal with fault diagnosis more accurately, systematically and intelligently. This method is assessed with vibration data of rolling bearings. The experimental results prove the proposed method effective.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

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

Similar content being viewed by others

References

  • Berners-Lee, T., & Hendler, J. (2001). Publishing on the semantic web–The coming Internet revolution will profoundly affect scientific information. Nature, 410(6832), 1023–1024. doi:10.1038/35074206.

  • Boashash, B., & Ouelha, S. (2016). Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study. Knowledge-Based Systems, 106, 38–50. doi:10.1016/j.knosys.2016.05.027.

    Article  Google Scholar 

  • Boutros, T., & Liang, M. (2011). Detection and diagnosis of bearing and cutting tool faults using hidden Markov models. Mechanical Systems and Signal Processing, 25(6), 2102–2124. doi:10.1016/j.ymssp.2011.01.013.

    Article  Google Scholar 

  • Cai, B., Liu, H., & Xie, M. (2016). A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks. Mechanical Systems and Signal Processing, 80, 31–44. doi:10.1016/j.ymssp.2016.04.019.

    Article  Google Scholar 

  • Chine, W., Mellit, A., Lughi, V., Malek, A., Sulligoi, G., & Massi Pavan, A. (2016). A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renewable Energy, 90, 501–512. doi:10.1016/j.renene.2016.01.036.

    Article  Google Scholar 

  • Chow, E., & Willsky, A. (1984). Analytical redundancy and the design of robust failure detection systems. IEEE Transactions on Automatic Control, 29(7), 603–614. doi:10.1109/TAC.1984.1103593.

    Article  Google Scholar 

  • Dai, X., & Gao, Z. (2013). From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis. IEEE Transactions on Industrial Informatics, 9(4), 2226–2238. doi:10.1109/TII.2013.2243743.

    Article  Google Scholar 

  • Dendani-Hadiby, N., & Khadir, M. T. (2012). A case based reasoning system based on domain ontology for fault diagnosis of steam turbines. International Journal of Hybrid Information Technology, 5(3), 89–104.

    Google Scholar 

  • Djebala, A., Babouri, M. K., & Ouelaa, N. (2015). Rolling bearing fault detection using a hybrid method based on Empirical Mode Decomposition and optimized wavelet multi-resolution analysis. The International Journal of Advanced Manufacturing Technology, 79(9–12), 2093–2105. doi:10.1007/s00170-015-6984-7.

    Article  Google Scholar 

  • Dong, M., & He, D. (2007). Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis. European Journal of Operational Research, 178(3), 858–878. doi:10.1016/j.ejor.2006.01.041.

    Article  Google Scholar 

  • Dou, D., Yang, J., Liu, J., & Zhao, Y. (2012). A rule-based intelligent method for fault diagnosis of rotating machinery. Knowledge-Based Systems, 36, 1–8. doi:10.1016/j.knosys.2012.05.013.

    Article  Google Scholar 

  • Dou, D., & Zhou, S. (2016). Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery. Applied Soft Computing, 46, 459–468. doi:10.1016/j.asoc.2016.05.015.

    Article  Google Scholar 

  • Du, J., Hu, Y., & Jiang, H. (2011). Boosted mixture learning of Gaussian mixture hidden Markov models based on maximum likelihood for speech recognition. IEEE Transactions on Audio Speech & Language Processing, 19(7), 2091–2100. doi:10.1109/TASL.2011.2112352.

    Article  Google Scholar 

  • Ebrahimipour, V., & Yacout, S. (2015). Ontology modeling in physical asset integrity management. Cham: Springer International Publishing.

    Book  Google Scholar 

  • Feng, Z., Liang, M., & Chu, F. (2013). Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples. Mechanical Systems and Signal Processing, 38(1), 165–205. doi:10.1016/j.ymssp.2013.01.017.

    Article  Google Scholar 

  • Frank, P. M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—A survey and some new results. Automatica, 26(3), 459–474. doi:10.1016/0005-1098(90)90018-D.

    Article  Google Scholar 

  • Frank, P. M., & Köppen-Seliger, B. (1997). New developments using AI in fault diagnosis. Engineering Applications of Artificial Intelligence, 10(1), 3–14. doi:10.1016/S0952-1976(96)00072-3.

    Article  Google Scholar 

  • Gan, M., Wang, C., & Zhu, C. A. (2016). Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mechanical Systems and Signal Processing, 72–73, 92–104. doi:10.1016/j.ymssp.2015.11.014.

    Article  Google Scholar 

  • Gertler, J. J. (1988). Survey of model-based failure detection and isolation in complex plants. IEEE Control Systems Magazine, 8(6), 3–11. doi:10.1109/37.9163.

    Article  Google Scholar 

  • Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199–220. doi:10.1006/knac.1993.1008.

    Article  Google Scholar 

  • Huang, W., Kong, F., & Zhao, X. (2015). Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-015-1174-x.

    Article  Google Scholar 

  • Isermann, R. (1984). Process fault detection based on modeling and estimation methods—A survey. Automatica, 20(4), 387–404. doi:10.1016/0005-1098(84)90098-0.

    Article  Google Scholar 

  • Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 72–73, 303–315. doi:10.1016/j.ymssp.2015.10.025.

    Article  Google Scholar 

  • Kusiak, A., & Verma, A. (2012). Analyzing bearing faults in wind turbines: A data-mining approach. Renewable Energy, 48, 110–116. doi:10.1016/j.renene.2012.04.020.

    Article  Google Scholar 

  • Kusiak, A., Zhang, Z., & Verma, A. (2013). Prediction, operations, and condition monitoring in wind energy. Energy, 60, 1–12. doi:10.1016/j.energy.2013.07.051.

    Article  Google Scholar 

  • Lee, J., Jin, C., Liu, Z., & Davari Ardakani, H. (2017). Introduction to data-driven methodologies for prognostics and health management. In S. Ekwaro-Osire, A. C. Gonçalves, & F. M. Alemayehu (Eds.), Probabilistic prognostics and health management of energy systems (pp. 9–32). Cham: Springer.

    Chapter  Google Scholar 

  • Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1–2), 314–334. doi:10.1016/j.ymssp.2013.06.004.

    Article  Google Scholar 

  • Lei, Y., He, Z., & Zi, Y. (2008). A new approach to intelligent fault diagnosis of rotating machinery. Expert Systems with Applications, 35(4), 1593–1600. doi:10.1016/j.eswa.2007.08.072.

    Article  Google Scholar 

  • Lei, Y., He, Z., Zi, Y., & Hu, Q. (2007). Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing, 21(5), 2280–2294. doi:10.1016/j.ymssp.2006.11.003.

    Article  Google Scholar 

  • Lei, Y., Lin, J., He, Z., & Zuo, M. J. (2013). A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 35(1–2), 108–126. doi:10.1016/j.ymssp.2012.09.015.

    Article  Google Scholar 

  • Li, R., Sopon, P., & He, D. (2012). Fault features extraction for bearing prognostics. Journal of Intelligent Manufacturing, 23(2), 313–321. doi:10.1007/s10845-009-0353-z.

    Article  Google Scholar 

  • Li, Z., & Ding, W. (2013). A novel fault diagnosis method for gear transmission systems using combined detection technologies. Research Journal of Applied Sciences Engineering & Technology, 6(18), 3354–3358.

    Article  Google Scholar 

  • Medina-Oliva, G., Voisin, A., Monnin, M., & Leger, J. (2014). Predictive diagnosis based on a fleet-wide ontology approach. Knowledge-Based Systems, 68, 40–57. doi:10.1016/j.knosys.2013.12.020.

    Article  Google Scholar 

  • Mehta, P., Werner, A., & Mears, L. (2015). Condition based maintenance-systems integration and intelligence using Bayesian classification and sensor fusion. Journal of Intelligent Manufacturing, 26(2), 331–346. doi:10.1007/s10845-013-0787-1.

    Article  Google Scholar 

  • Miao, Q., & Makis, V. (2007). Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models. Mechanical Systems and Signal Processing, 21(2), 840–855. doi:10.1016/j.ymssp.2006.01.009.

    Article  Google Scholar 

  • Nan, C., Khan, F., & Iqbal, M. T. (2008). Real-time fault diagnosis using knowledge-based expert system. Process Safety and Environmental Protection, 86(1), 55–71. doi:10.1016/j.psep.2007.10.014.

    Article  Google Scholar 

  • Olsson, E., Funk, P., & Bengtsson, M. (2004). Fault diagnosis of industrial robots using acoustic signals and case-based reasoning. In P. Funk & P. A. G. Calero (Eds.), Advances in case-based reasoning (Vol. 3155, pp. 686–701)., Lecture notes in computer science Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  • Patton, R. J., & Chen, J. (1997). Observer-based fault detection and isolation: Robustness and applications. Control Engineering Practice, 5(5), 671–682. doi:10.1016/S0967-0661(97)00049-X.

    Article  Google Scholar 

  • Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: A review. The International Journal of Advanced Manufacturing Technology, 50(1–4), 297–313. doi:10.1007/s00170-009-2482-0.

    Article  Google Scholar 

  • Rabiner, L. R. (1986). An introduction to hidden Markov models. PLoS ONE, 9(12), e114089. doi:10.1371/journal.pone.0114089.

    Article  Google Scholar 

  • Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286. doi:10.1109/5.18626.

    Article  Google Scholar 

  • Raich, A., & Cinar, A. (1994). Statistical process monitoring and disturbance isolation in multivariate continuous processes Advanced Control of Chemical Processes 1994 (451–456). Oxford: Pergamon.

    Google Scholar 

  • Sahin, S., Tolun, M. R., & Hassanpour, R. (2012). Hybrid expert systems: A survey of current approaches and applications. Expert Systems with Applications, 39(4), 4609–4617. doi:10.1016/j.eswa.2011.08.130.

    Article  Google Scholar 

  • Shen, C., Wang, D., Kong, F., & Tse, P. W. (2013). Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Measurement, 46(4), 1551–1564. doi:10.1016/j.measurement.2012.12.011.

    Article  Google Scholar 

  • Shu-Hsien, L. (2005). Expert system methodologies and applications—A decade review from 1995 to 2004. Expert Systems with Applications, 28(1), 93–103. doi:10.1016/j.eswa.2004.08.003.

    Article  Google Scholar 

  • Tang, X., Zhuang, L., Cai, J., & Li, C. (2010). Multi-fault classification based on support vector machine trained by chaos particle swarm optimization. Knowledge-Based Systems, 23(5), 486–490. doi:10.1016/j.knosys.2010.01.004.

    Article  Google Scholar 

  • Teng, W., Zhang, X., Liu, Y., Kusiak, A., & Ma, Z. (2017). Prognosis of the remaining useful life of bearings in a wind turbine gearbox. Energies, 10(1), 32. doi:10.3390/en10010032.

    Article  Google Scholar 

  • Wang, C., Gan, M., & Zhu, C. A. (2015a). Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-015-1153-2.

    Article  Google Scholar 

  • Wang, C., Gan, M., & Zhu, C. A. (2015b). Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-015-1056-2.

    Article  Google Scholar 

  • Wang, D., Tang, W. H., & Wu, Q. H. (2010). Ontology-based fault diagnosis for power transformers. Paper presented at the 2010 IEEE Power and Energy Society General Meeting, Providence, RI, USA. doi:10.1109/PES.2010.5589575.

  • Wang, H., Chen, Y., Chan, C. W. H., Qin, J., & Wang, J. (2012). Online model-based fault detection and diagnosis strategy for VAV air handling units. Energy & Buildings, 55(12), 252–263. doi:10.1016/j.enbuild.2012.08.016.

    Article  Google Scholar 

  • Wang, H., Li, R., Tang, G., Yuan, H., Zhao, Q., & Cao, X. (2014). A compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition. PLoS ONE, 9(10), e109166. doi:10.1371/journal.pone.0109166.

    Article  Google Scholar 

  • Wang, Y., Li, Q., Chang, M., Chen, H., & Zang, G. (2012). Research on fault diagnosis expert system based on the neural network and the fault tree technology. Procedia Engineering, 31, 1206–1210. doi:10.1016/j.proeng.2012.01.1164.

    Article  Google Scholar 

  • Wen, H., Zhen, Y., Zhang, H., Chen, A., & Liu, D. (2009). An ontology modeling method of mechanical fault diagnosis system based on RSM. In Fifth International Conference on Semantics, Knowledge and Grid, 2009 (SKG 2009), Zhuhai, China. doi:10.1109/SKG.2009.57.

  • Wu, C., Chen, T., Jiang, R., Ning, L., & Jiang, Z. (2015). A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-015-1070-4.

    Article  Google Scholar 

  • Xu, Z., Xuan, J., Shi, T., Wu, B., & Hu, Y. (2009). A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique. Expert Systems with Applications, 36(9), 11801–11807. doi:10.1016/j.eswa.2009.04.021.

    Article  Google Scholar 

  • Yang, B., Han, T., & Kim, Y. (2004). Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis. Expert Systems with Applications, 26(3), 387–395. doi:10.1016/j.eswa.2003.09.009.

    Article  Google Scholar 

  • Zhang, X., Wang, B., & Chen, X. (2015). Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowledge-Based Systems, 89, 56–85. doi:10.1016/j.knosys.2015.06.017.

    Article  Google Scholar 

  • Zhi-Ling, Y., Bin, W., Xing-Hui, D., & Hao, L. (2012). Expert system of fault diagnosis for gear box in wind turbine. Systems Engineering Procedia, 4, 189–195. doi:10.1016/j.sepro.2011.11.065.

    Article  Google Scholar 

  • Zhou, A., Yu, D., & Zhang, W. (2015). A research on intelligent fault diagnosis of wind turbines based on ontology and FMECA. Advanced Engineering Informatics, 29(1), 115–125. doi:10.1016/j.aei.2014.10.001.

  • Zhou, Q., Yan, P., & Xin, Y. (2017). Research on a knowledge modelling methodology for fault diagnosis of machine tools based on formal semantics. Advanced Engineering Informatics, 32, 92–112. doi:10.1016/j.aei.2017.01.002.

    Article  Google Scholar 

  • Ziani, R., Felkaoui, A., & Zegadi, R. (2014). Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-014-0987-3.

  • Zio, E., Baraldi, P., & Gola, G. (2008). Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery. Applied Soft Computing, 8(4), 1365–1380. doi:10.1016/j.asoc.2007.10.005.

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported by the “2016 Smart manufacturing project of China (2016ZXFB2002)”. The authors would like to thank the anonymous reviewers for their valuable time and efforts in review.

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, Chongqing University, No. 174, Shazheng Street, Shapingba District, Chongqing, China

    Qiang Zhou, Ping Yan, Huayi Liu & Yang Xin

Authors
  1. Qiang Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ping Yan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Huayi Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yang Xin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ping Yan.

Appendix

Appendix

I. Common failure modes and detecting methods of rolling bearings

See Tables 8 and 9.

Table 8 Common failure modes of rolling bearings
Full size table
Table 9 Common detecting methods of rolling bearings
Full size table

In addition, there are also other detecting methods, including micro morphology analysis, oil film resistance diagnosis, ultrasonic testing, optical fiber monitoring and diagnosis, ray detection, penetrant testing and clearance measurement.

II. Common feature parameters in time domain of rolling bearings

See Table 10.

Table 10 Feature parameters in time domain
Full size table

III. Common feature parameters in frequency domain of rolling bearings

See Table 11.

Table 11 Feature parameters in frequency domain
Full size table

IV. Wavelet packet energy extraction process

For a given signal \(x\left( t \right) \), the wavelet packet energy extraction process is:

See Table 12.

Table 12 Three layer wavelet packet energy extraction process
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Q., Yan, P., Liu, H. et al. A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis. J Intell Manuf 30, 1693–1715 (2019). https://doi.org/10.1007/s10845-017-1351-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-017-1351-1

Keywords