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2021 | OriginalPaper | Buchkapitel

Embedded Machine Learning for Machine Condition Monitoring

verfasst von : Michael Grethler, Marin B. Marinov, Vesa Klumpp

Erschienen in: Future Access Enablers for Ubiquitous and Intelligent Infrastructures

Verlag: Springer International Publishing

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Abstract

With the application of a new generation of information technology in the field of manufacturing and the deep integration of computer technology and manufacturing, industrial production is moving towards intellectualization and networking . Because the current production system cannot fully exploit the value of industrial data and the existence of information islands in the production process, this paper presents a study on the development, testing, and evaluation of a machine learning process that can be run on low-cost standard microcontrollers with limited computing and memory resources. This paper first analyzes the basic idea of whether it is possible to develop software for intelligent sensors whose algorithms run on microcontrollers. At the same time, it is considered whether the training and the adaptation of the model parameters can be done on the microcontroller to enable an online adaptation of the machine to be monitored. The goal is a closed system that does not need a backend and the storage of large amounts of data is not necessary.

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Lynch, J.P.: A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vibr. Digest 38(2), 91–128 (2006)CrossRef

Yick, J., Mukherjee, B., Ghosal, D.: Wireless sensor network survey. Comput. Netw. 52(12), 2292–2330 (2008)CrossRef

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

Aldrich, C., Auret, L.: Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. In: Advances in Computer Vision and Pattern Recognition. Springer, Berlin (2013). https://doi.org/10.1007/978-1-4471-5185-2

Liang, M., Zhuang, Y., Cai, C.: Face classification using sparse reconstruction embedding. In: Proceedings of International Conference on Computational Intelligence and Software Engineering (CiSE), Wuhan, China, December 2010

Patel, S., et al.: A review of wearable sensors and systems in rehabilitation. J. Neuroeng. Rehab. 1–17 (2012)

Rajan, D., Spanias, A., Ranganath, S., Banavar, M., Spanias, P.: Health monitoring laboratories by interfacing physiological sensors to mobile android devices. In: Proceedings of IEEE FIE (2013)

Zhang, X., et al.: Performance comparison of localization techniques for sequential WSN discovery. In: Proceeding of IEEE SSPD, September 2012

Zhang, X., Tepedelenlioglu, C., Banavar, M., Spanias, A.: Distributed location detection in wireless sensor networks. In: Proceedings of IEEE Asilomar Conference on Signals, Systems and Computers, November 2013

10.

Hino, M., Benami, E., Brooks, N.: Machine learning for environmental monitoring. Nat. Sustain. 1, 583–588 (2018). https://doi.org/10.1038/s41893-018-0142-9CrossRef

11.

Ivanova, M.: Methodology for analysis of gm-c filters based on statistical, fuzzy logic, and machine learning approach. In: EEET 2020: Proceedings of the 2020 3rd International Conference on Electronics and Electrical Engineering Technology, September 2020

12.

Hu, H., Wen, Y., Chua, T.-S., Li, X.: Toward scalable systems for big data analytics: a technology tutorial. IEEE Access, 652–687 (2014)

13.

Xia, W., Mita, Y., Shibata, T.: A nearest neighbor classifier employing critical boundary vectors for efficient on-chip template reduction. IEEE Trans. Neural Netw. Learn. Syst. 27(5), 1094–1107 (2016)MathSciNetCrossRef

14.

A’râbi, M.-A., Schwarz, V.: General constraints in embedded machine learning. In: SCA, Freiburg, July 2019

15.

Cross-Domain Development Kit XDK110 Platform for Application Development. Bosch Connected Devices and GmbH (2016)

16.

Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics, Springer, Heidelberg (2006)MATH

17.

Andrade, L., Prost-Boucle, A., Pétrot, F.: Overview of the state of the art in embedded machine learning. In: 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden (2018)

18.

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRef

19.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS 2012) (2012)

20.

Arm, E.M.: Learning Design for Dummies. Wiley, Hoboken (2019)

21.

Lowhead, J.: Learning GeoSpatial Analysis with Python, 2nd edn. Packt, Birmingham (2015)

22.

Zekveld, M., Hancke, G.P.: Vibration condition monitoring using machine learning. In: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, pp. 4742–4747 (2018). https://doi.org/10.1109/IECON.2018.8591167

23.

Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., Loncarski, J.: Machine learning approach for predictive maintenance in industry 4.0. In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), Oulu, pp. 1–6 (2018). https://doi.org/10.1109/MESA.2018.8449150

24.

Alsheikh, M.A., Lin, S., Niyato, D., Tan, H.: Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun. Surv. Tutor. 16(4), 1996–2018 (2014). Fourth quarter. https://doi.org/10.1109/COMST.2014.2320099

25.

Wilkinson, M., Darnell, B., Delft, T.V., Harman, K.: Comparison of methods for wind turbine condition monitoring with SCADA data. IET Renew. Pow. Gener. 8(4), 390–397 (2014). https://doi.org/10.1049/iet-rpg.2013.0318CrossRef

26.

Hodge, V.J., O’Keefe, S., Weeks, M., Moulds, A.: Wireless sensor networks for condition monitoring in the railway industry: a survey. IEEE Trans. Intell. Transp. Syst. 16(3), 1088–1106 (2015). https://doi.org/10.1109/TITS.2014.2366512CrossRef

Titel: Embedded Machine Learning for Machine Condition Monitoring
verfasst von: Michael Grethler
Marin B. Marinov
Vesa Klumpp
Verlag: Springer International Publishing
Buch: Future Access Enablers for Ubiquitous and Intelligent Infrastructures
Print ISBN: 978-3-030-78458-4

Electronic ISBN: 978-3-030-78459-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-78459-1_16

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