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

  • Chapter
  • First Online:
Digital Transformation

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

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

References

  1. 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

  2. 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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. 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)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  8. 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.2018.2861327

    Article  Google Scholar 

  9. 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)

    Google Scholar 

  11. Fletcher, G.P., Groth, P.T., Sequeda, J.: Knowledge scientists: Unlocking the data-driven organization. Ar**v (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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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.2013.08.012

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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.2019.09.047

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

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

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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.2014.12.020

    Article  Google Scholar 

  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.2010.12.014

    Article  Google Scholar 

  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.2018.01.008

    Article  Google Scholar 

  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/10.1109/21.97458

    Article  Google Scholar 

  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/10.1080/10919392.2018.1444344

    Article  Google Scholar 

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

    Article  Google Scholar 

  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/S0022029917000188

    Article  Google Scholar 

  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/S0022029917000176

    Article  Google Scholar 

  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., **a, 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)

    Google Scholar 

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

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

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

    Book  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christoph G. Schuetz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Schuetz, C.G., Selway, M., Thalmann, S., Schrefl, M. (2023). Discovering Actionable Knowledge for Industry 4.0: From Data Mining to Predictive and Prescriptive Analytics. In: Vogel-Heuser, B., Wimmer, M. (eds) Digital Transformation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-65004-2_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-65004-2_14

  • Published:

  • Publisher Name: Springer Vieweg, Berlin, Heidelberg

  • Print ISBN: 978-3-662-65003-5

  • Online ISBN: 978-3-662-65004-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Navigation