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.
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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)
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)
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)
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)
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)
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)
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., Wu, B.: Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Wiley, Hoboken (2006)
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)
Venkatasubramanian, V.: Prognostic and diagnostic monitoring of complex systems for product lifecycle management: challenges and opportunities. Comput. Chem. Eng. 29(6), 1253–1263 (2005)
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)
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)
Marsland, S.: Machine Learning: An Algorithmic Perspective, 2nd edn. Chapman and Hall/CRC Press, Boca Raton (2014)
ISO/IEC 2382.: Information Technology – Vocabulary. International Organization for Standardization, Geneva (2015)
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)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
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)
Ahmad, R., Kamaruddin, S.: A review of condition-based maintenance decision-making. Eur. J. Industr. Eng. 6(5), 519–541 (2012)
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)
Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. Informatica 31(3), 249–268 (2007)
Chandola, V., Banerjee, V., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)
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)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)
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)
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Habrich, T., Wagner, C., Hellingrath, B. (2018). Qualitative Assessment of Machine Learning Techniques in the Context of Fault Diagnostics. In: Abramowicz, W., Paschke, A. (eds) Business Information Systems. BIS 2018. Lecture Notes in Business Information Processing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-93931-5_26
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DOI: https://doi.org/10.1007/978-3-319-93931-5_26
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