nach oben

Journal of Intelligent Manufacturing

Erschienen in:

09.08.2017

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

verfasst von: Qiang Zhou, Ping Yan, Huayi Liu, Yang Xin

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel An evolutionary computation based method for creative design inspiration generation

Nächster Artikel Metacognitive learning approach for online tool condition monitoring

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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

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.CrossRef

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

Titel: A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis
verfasst von: Qiang Zhou
Ping Yan
Huayi Liu
Yang Xin
Publikationsdatum: 09.08.2017
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 4/2019
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-017-1351-1

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 4/2019

Manufacturing paradigm-oriented PHM methodologies for cyber-physical systems

On the robustness of joint production and maintenance scheduling in presence of uncertainties

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

A general architecture of a 3D visualization system for shop floor management

An evolutionary clustering search for the total tardiness blocking flow shop problem

Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques

Premium Partner

Marktübersichten