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2023 | OriginalPaper | Chapter

Discovering Actionable Knowledge for Industry 4.0: From Data Mining to Predictive and Prescriptive Analytics

Authors : Christoph G. Schuetz, Matt Selway, Stefan Thalmann, Michael Schrefl

Published in: Digital Transformation

Publisher: Springer Berlin Heidelberg

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Abstract

Making sense of the vast amounts of data generated by modern production operations—and thus realizing the full potential of digitization—requires adequate means of data analysis. In this regard, data mining represents the employment of statistical methods to look for patterns in data. Predictive analytics then puts the thus gathered knowledge to good use by making predictions about future events, e.g., equipment failure in process industries and manufacturing or animal illness in farming operations. Finally, prescriptive analytics derives from the predicted events suggestions for action, e.g., optimized production plans or ideal animal feed composition. In this chapter, we provide an overview of common techniques for data mining as well as predictive and prescriptive analytics, with a specific focus on applications in production. In particular, we focus on association and correlation, classification, cluster analysis and outlier detection. We illustrate selected methods of data analysis using examples inspired from real-world settings in process industries, manufacturing, and precision farming.

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previous chapter Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective

next chapter Process Mining—Discovery, Conformance, and Enhancement of Manufacturing Processes

https://www.cs.waikato.ac.nz/ml/weka/

Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th International Conference on Data Engineering. pp. 3–14 (1995). https://doi.org/10.1109/ICDE.1995.380415

Agrawal, R., Imieliundefinedski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. pp. 207–216 (1993). https://doi.org/10.1145/170035.170072

Ahmad, R., Kamaruddin, S.: An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering 63(1), 135–149 (2012)CrossRef

Altman, N., Krzywinski, M.: Association, correlation and causation. Nature Methods 12, 899–900 (2015). https://doi.org/10.1038/nmeth.3587CrossRef

Birajdar, R.S., Patil, R.S., Khanzode, K.: Vibration and noise in centrifugal pumps - sources and diagnosis methods. In: Proc. 3rd International Conference on Integrity, Reliability and Failure (2009)

Breiman, L.: Random forests. Machine learning 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324CrossRef

Daly, F., Hand, D.J., Jones, M., Lunn, A., McConway, K.: Elements of statistics. Addison-Wesley Publishing Company (1995)

Dobson, S., Golfarelli, M., Graziani, S., Rizzi, S.: A reference architecture and model for sensor data warehousing. IEEE Sensors Journal 18(18), 7659–7670 (2018). https://doi.org/10.1109/JSEN.2018.2861327CrossRef

Doran, D., Schulz, S., Besold, T.R.: What does explainable AI really mean? A new conceptualization of perspectives. CoRR abs/1710.00794 (2017), http://arxiv.org/abs/1710.00794

10.

Ehrendorfer, M., Fassmann, J.A., Mangler, J., Rinderle-Ma, S.: Conformance checking and classification of manufacturing log data. In: 2019 IEEE 21st Conference on Business Informatics (CBI). vol. 1, pp. 569–577. IEEE (2019)

11.

Fletcher, G.P., Groth, P.T., Sequeda, J.: Knowledge scientists: Unlocking the data-driven organization. ArXiv (2020), http://arxiv.org/abs/2004.07917

12.

Gashi, M., Ofner, P., Ennsbrunner, H., Thalmann, S.: Dealing with missing usage data in defect prediction: A case study of a welding supplier. Computers in industry 132, 103505 (2021)CrossRef

13.

Gashi, M., Thalmann, S.: Taking complexity into account: A structured literature review on multi-component systems in the context of predictive maintenance. In: European, Mediterranean, and Middle Eastern Conference on Information Systems. pp. 31–44. Springer (2019)

14.

Gatica, C.P., Koester, M., Gaukstern, T., Berlin, E., Meyer, M.: An industrial analytics approach to predictive maintenance for machinery applications. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA). pp. 1–4 (2016)

15.

Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016), http://www.deeplearningbook.org

16.

Halachmi, I., Klopčič, M., Polak, P., Roberts, D., Bewley, J.: Automatic assessment of dairy cattle body condition score using thermal imaging. Computers and Electronics in Agriculture 99, 35–40 (2013). https://doi.org/10.1016/j.compag.2013.08.012CrossRef

17.

Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edition. Morgan Kaufmann (2011), http://hanj.cs.illinois.edu/bk3/

18.

Hoffmann, G., Schmidt, M., Ammon, C., Rose-Meierhöfer, S., Burfeind, O., Heuwieser, W., Berg, W.: Monitoring the body temperature of cows and calves using video recordings from an infrared thermography camera. Veterinary Research Communications 37(2), 91–99 (2013)CrossRef

19.

International Organization for Standardization: Condition monitoring and diagnostics of machines—vibration condition monitoring—part 1: General procedures. International Standard, ISO 13373-1:2002, ISO (2002)

20.

International Organization for Standardization: Condition monitoring and diagnostics of machines—data processing, communication and presentation—part 1: General guidelines. International Standard, ISO 13374-1:2003, ISO (2003)

21.

International Organization for Standardization: Automation systems and integration—oil and gas interoperability—part 1: Overview and fundamental principles. Technical Specification, ISO/TS 18101-1:2019, ISO (2019)

22.

de Jonge, B., Scarf, P.A.: A review on maintenance optimization. European Journal of Operational Research 285(3), 805–824 (2020). https://doi.org/10.1016/j.ejor.2019.09.047CrossRefMATH

23.

de Jonge, B., Teunter, R., Tinga, T.: The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance. Reliability engineering & system safety 158, 21–30 (2017)CrossRef

24.

Kans, M., Galar, D.: The impact of maintenance 4.0 and big data analytics within strategic asset management. In: 6th International Conference on Maintenance Performance Measurement and Management, 28 November 2016, Luleå, Sweden. pp. 96–103. Luleå University of Technology (2017)

25.

Kaur, K., Selway, M., Grossmann, G., Stumptner, M., Johnston, A.T.: Towards an open-standards based framework for achieving condition-based predictive maintenance. In: Proceedings of the 8th International Conference on the Internet of Things (IoT 2018). pp. 16:1–16:8 (2018). https://doi.org/10.1145/3277593.3277608

26.

Khoshafian, S., Rostetter, C.: Digital prescriptive maintenance. Internet of Things, Process of Everything, BPM Everywhere (2015)

27.

Koller, D., Friedman, N.: Probabilistic graphical models: principles and techniques. MIT press (2009)

28.

Lee, C., Cao, Y., Ng, K.H.: Big data analytics for predictive maintenance strategies. In: Supply Chain Management in the Big Data Era, pp. 50–74. IGI Global (2017)

29.

Lohr, S.: For big-data scientists, ‘janitor work’ is key hurdle to insights (2014), https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html, accessed: 5 May 2021

30.

Narayan, V.: Business performance and maintenance: How are safety, quality, reliability, productivity and maintenance related? Journal of Quality in Maintenance Engineering 18(2), 183–195 (2012)CrossRef

31.

OpenO &M For Manufacturing Joint Working Group: Condition based operations for manufacturing. Whitepaper, MIMOSA, OPC Foundation, ISA (2004), http://www.openoandm.org/files/whitepapers/2004-10-06_Condition_Based_Operations_for_Manufacturing.pdf

32.

Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: A review. ACM Computing Surveys 54(2) (2021). https://doi.org/10.1145/3439950

33.

Pauker, F., Mangler, J., Rinderle-Ma, S., Pollak, C.: centurio.work—modular secure manufacturing orchestration. In: Proceedings of the Dissertation Award, Demonstration, and Industrial Track at BPM 2018 co-located with 16th International Conference on Business Process Management (BPM 2018). CEUR Workshop Proceedings, vol. 2196, pp. 164–171. CEUR-WS.org (2018), http://ceur-ws.org/Vol-2196/BPM_2018_paper_33.pdf

34.

Phillips, J., Cripps, E., Lau, J.W., Hodkiewicz, M.: Classifying machinery condition using oil samples and binary logistic regression. Mechanical Systems and Signal Processing 60–61, 316–325 (2015). https://doi.org/10.1016/j.ymssp.2014.12.020CrossRef

35.

Rabatel, J., Bringay, S., Poncelet, P.: Anomaly detection in monitoring sensor data for preventive maintenance. Expert Systems with Applications 38(6), 7003–7015 (2011). https://doi.org/10.1016/j.eswa.2010.12.014CrossRef

36.

Research, A.: Maintenance analytics to generate $24.7 billion in 2019, driven by predictive maintenance and internet of things (March 2014), https://www.abiresearch.com/press/maintenance-analytics-to-generate-247-billion-in-2/

37.

Roland, L., Lidauer, L., Sattlecker, G., Kickinger, F., Auer, W., Sturm, V., Efrosinin, D., Drillich, M., Iwersen, M.: Monitoring drinking behavior in bucket-fed dairy calves using an ear-attached tri-axial accelerometer: A pilot study. Computers and Electronics in Agriculture 145, 298–301 (2018). https://doi.org/10.1016/j.compag.2018.01.008CrossRef

38.

Ruiz, A.: The 80/20 data science dilemma (2017), https://www.infoworld.com/article/3228245/the-80-20-data-science-dilemma.html, accessed: 5 May 2021

39.

Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics 21(3), 660–674 (1991). https://doi.org/10.1109/21.97458CrossRef

40.

Schuetz, C.G., Schausberger, S., Schrefl, M.: Building an active semantic data warehouse for precision dairy farming. Journal of Organizational Computing and Electronic Commerce 28(2), 122–141 (2018). https://doi.org/10.1080/10919392.2018.1444344CrossRef

41.

Shi, W., Dustdar, S.: The promise of edge computing. Computer 49(5), 78–81 (2016). https://doi.org/10.1109/MC.2016.145CrossRef

42.

Steensels, M., Maltz, E., Bahr, C., Berckmans, D., Antler, A., Halachmi, I.: Towards practical application of sensors for monitoring animal health; design and validation of a model to detect ketosis. Journal of Dairy Research 84(2), 139–145 (2017). https://doi.org/10.1017/S0022029917000188CrossRef

43.

Steensels, M., Maltz, E., Bahr, C., Berckmans, D., Antler, A., Halachmi, I.: Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield. Journal of Dairy Research 84(2), 132–138 (2017). https://doi.org/10.1017/S0022029917000176CrossRef

44.

Stojanovic, L., Dinic, M., Stojanovic, N., Stojadinovic, A.: Big-data-driven anomaly detection in industry (4.0): an approach and a case study. In: Joshi, J., Karypis, G., Liu, L., Hu, X., Ak, R., Xia, Y., Xu, W., Sato, A., Rachuri, S., Ungar, L.H., Yu, P.S., Govindaraju, R., Suzumura, T. (eds.) 2016 IEEE International Conference on Big Data (2016). https://doi.org/10.1109/BigData.2016.7840777

45.

Suschnigg, J., Ziessler, F., Brillinger, M., Vukovic, M., Mangler, J., Schreck, T., Thalmann, S.: Industrial production process improvement by a process engine visual analytics dashboard. In: Proceedings of the 53rd Hawaii International Conference on System Sciences. pp. 1320–1329 (2020)

46.

Tabachnick, B.G., Fidell, L.S.: Using multivariate statistics. Pearson, 6 edn. (2014)

47.

Thalmann, S., Gursch, H., Suschnigg, J., Gashi, M., Ennsbrunner, H., Fuchs, A.K., Schreck, T., Mutlu, B., Mangler, J., Kappl, G., et al.: Cognitive decision support for industrial product life cycles: A position paper. In: COGNITIVE 2019: The Eleventh International Conference on Advanced Cognitive Technologies and Applications. pp. 3–9. IARIA (2019)

48.

Thalmann, S., Mangler, J., Schreck, T., Huemer, C., Streit, M., Pauker, F., Weichhart, G., Schulte, S., Kittl, C., Pollak, C., et al.: Data analytics for industrial process improvement a vision paper. In: 2018 IEEE 20th Conference on Business Informatics (CBI). vol. 2, pp. 92–96. IEEE (2018)

49.

Thirumuruganathan, S., Tang, N., Ouzzani, M., Doan, A.: Data curation with deep learning. In: Proceedings of the 23rd International Conference on Extending Database Technology (EDBT 2020). pp. 277–286. OpenProceedings.org (2020). https://doi.org/10.5441/002/edbt.2020.25

50.

Thurston, M., Lebold, M.: Standards developments for condition-based maintenance systems. Tech. rep., Pennsylvania State Univ University Park Applied Research Lab (2001)

51.

Vaisman, A., Zimányi, E.: Data Warehouse Systems – Design and Implementation. Springer, Berlin Heidelberg (2014)CrossRef

52.

Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 4 edn. (2017)

53.

Yan, H., Wan, J., Zhang, C., Tang, S., Hua, Q., Wang, Z.: Industrial big data analytics for prediction of remaining useful life based on deep learning. IEEE Access 6, 17190–17197 (2018)CrossRef

Title: Discovering Actionable Knowledge for Industry 4.0: From Data Mining to Predictive and Prescriptive Analytics
Authors: Christoph G. Schuetz
Matt Selway
Stefan Thalmann
Michael Schrefl
Publisher: Springer Berlin Heidelberg
Book: Digital Transformation
Print ISBN: 978-3-662-65003-5

Electronic ISBN: 978-3-662-65004-2

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-662-65004-2_14

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