nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Qualitative Assessment of Machine Learning Techniques in the Context of Fault Diagnostics

verfasst von : Thilo Habrich, Carolin Wagner, Bernd Hellingrath

Erschienen in: Business Information Systems

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Nowadays, in the light of high data availability and computational power, Machine Learning (ML) techniques are widely applied to the area of fault diagnostics in the context of Condition-based Maintenance (CBM). Those techniques are able to learn intelligently from data to build suitable classification models, which enable the labeling of unknown data based on observed patterns. Even though plenty of research papers deal with this topic, the question remains open, which technique should be chosen for a specific problem. In order to select appropriate methods for a given problem, the problem characteristics have to be assessed against the strengths and weaknesses of relevant ML techniques. This paper presents a qualitative assessment of well-known ML techniques based on criteria obtained from literature. It is completed by a case study to identify the most suitable techniques to perform fault diagnostics in in-vitro diagnostic instruments with regard to the presented qualitative assessment.

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

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Satellite Imagery Analysis for Operational Damage Assessment in Emergency Situations

Nächstes Kapitel A Comparative Evaluation of Log-Based Process Performance Analysis Techniques

Mahantesh, N., Aditya, P., Kumar, U.: Integrated machine health monitoring: a knowledge based approach. Int. J. Syst. Assur. Eng. Manage. 5(3), 371–382 (2014)CrossRef

Shin, J.H., Jun, H.B.: On condition based maintenance policy. J. Comput. Des. Eng. 2(2), 119–127 (2015)

Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A review of process fault detection and diagnosis part I: quantitative model-based methods. Comput. Chem. Eng. 27(3), 293–311 (2003)CrossRef

Jardine, A.K.S., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20(7), 1483–1510 (2006)CrossRef

Peng, Y., Dong, M., Zuo, M.: Current status of machine prognostics in condition-based maintenance: a review. Int. J. Adv. Manuf. Technol. 50(1–4), 297–313 (2010)CrossRef

Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., Siegel, D.: Prognostics and health management design for rotary machinery systems – reviews, methodology and applications. Mech. Syst. Signal Process. 42(1–2), 314–334 (2014)CrossRef

Gao, Z., Cecati, C., Ding, S.: A survey of fault diagnosis and fault-tolerant techniques-part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans. Industr. Electron. 62(6), 3757–3767 (2015)CrossRef

Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., Wu, B.: Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Wiley, Hoboken (2006)CrossRef

Schwabacher, M., Goebel, K.: A survey of artificial intelligence for prognostics. The intelligence report. In: Association for the Advancement of Artificial Intelligence AAAI Fall Symposium 2007, pp. 107–114. AAAI Press, Arlington (2007)

10.

Venkatasubramanian, V.: Prognostic and diagnostic monitoring of complex systems for product lifecycle management: challenges and opportunities. Comput. Chem. Eng. 29(6), 1253–1263 (2005)CrossRef

11.

Dai, X., Gao, Z.: From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Trans. Industr. Inf. 9(4), 2226–2238 (2013)CrossRef

12.

Wu, F., Wang, T., Lee, J.: An online adaptive condition based maintenance method for mechanical systems. Mech. Syst. Signal Process. 24(8), 2985–2995 (2010)CrossRef

13.

Marsland, S.: Machine Learning: An Algorithmic Perspective, 2nd edn. Chapman and Hall/CRC Press, Boca Raton (2014)

14.

ISO/IEC 2382.: Information Technology – Vocabulary. International Organization for Standardization, Geneva (2015)

15.

Zhao, X., Li, M., Xu, J., Song, G.: Multi-class semi-supervised learning in machine condition monitoring. In: 2009 International Conference on Information Engineering and Computer Science, pp. 1–4. IEEE Press, Wuhan (2009)

16.

Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)MATH

17.

Chapelle, O., Scholkopf, B., Zien, A.: Introduction to semi-supervised learning. In: Chapelle, O., Scholkopf, B., Zien, A. (eds.) Semi-Supervised Learning, pp. 1–12. The MIT Press, Cambridge (2006)CrossRef

18.

Ahmad, R., Kamaruddin, S.: A review of condition-based maintenance decision-making. Eur. J. Industr. Eng. 6(5), 519–541 (2012)CrossRef

19.

Susto, G.A., Schirru, A., Pampuri, S., McLoone, S., Beghi, A.: Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans. Industr. Inf. 11(3), 812–820 (2015)CrossRef

20.

Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. Informatica 31(3), 249–268 (2007)MathSciNetMATH

21.

Chandola, V., Banerjee, V., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)CrossRef

22.

Vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., Cleven, A.: Reconstructing the giant: on the importance of rigour in documenting the literature search process. In: ECIS, vol. 9, pp. 2206–2217 (2009)

23.

Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)

24.

Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning – a new frontier in artificial intelligence research. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)CrossRef

Titel: Qualitative Assessment of Machine Learning Techniques in the Context of Fault Diagnostics
verfasst von: Thilo Habrich
Carolin Wagner
Bernd Hellingrath
Verlag: Springer International Publishing
Buch: Business Information Systems
Print ISBN: 978-3-319-93930-8

Electronic ISBN: 978-3-319-93931-5

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93931-5_26

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"

Springer Professional "Wirtschaft"

Premium Partner