Deep digital maintenance

doi:10.1007/s40436-017-0202-9

摘要/Abstract

摘要：

With the emergence of Industry 4.0, maintenance is considered to be a specific area of action that is needed to successfully sustain a competitive advantage. For instance, predictive maintenance will be central for asset utilization, service, and after-sales in realizing Industry 4.0. Moreover, artificial intelligence (AI) is also central for Industry 4.0, and offers data-driven methods. The aim of this article is to develop a new maintenance model called deep digital maintenance (DDM). With the support of theoretical foundations in cyber-physical systems (CPS) and maintenance, a concept for DDM is proposed. In this paper, the planning module of DDM is investigated in more detail with realistic industrial data from earlier case studies. It is expected that this planning module will enable integrated planning (IPL) where maintenance and production planning can be more integrated. The result of the testing shows that both the remaining useful life (RUL) and the expected profit loss indicator (PLI) of ignoring the failure can be calculated for the planning module. The article concludes that further research is needed in testing the accuracy of RUL, classifying PLI for different failure modes, and testing of other DDM modules with industrial case studies.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0202-9/fulltext.html

关键词: Maintenance planning, Integrated planning (IPL), Digital maintenance, Predictive maintenance, Industry 4.0

Abstract:

Key words: Maintenance planning, Integrated planning (IPL), Digital maintenance, Predictive maintenance, Industry 4.0

Harald Rødseth, Per Schjølberg, Andreas Marhaug. Deep digital maintenance[J]. Advances in Manufacturing, 2017, 5(4): 299-310.

参考文献

1. Kagermann H, Wahlster W, Helbig J (2013) Recommendations for implementing the strategic initiative Industrie 4.0:securing the future of German manufacturing industry. Forschungsunion, Berlin
2. Rødseth H, Skarlo T, Schjølberg P (2015) Profit loss indicator:a novel maintenance indicator applied for integrated planning. Advances in Manufacturing 3(2):139-150
3. Rødseth H, Schjølberg P (2016) Data-driven predictive maintenance for green manufacturing. In:Proceedings of the 6th international workshop of advanced manufacturing and automation. Advances in Economics, Business and Management Research, Atlantis Press, pp 36-41
4. McKinsey Company (2016) Industry 4.0:how to navigate digitization of the manufacturing sector. World mobility leadership form, September 28-29, 2016, Detroit. Michigan, USA
5. Langedijk E (2017) Smart maintenance management. http://www.maintworld.com/Asset-Management/Smart-MaintenanceManagement. Accessed 29th of March, 2017
6. Kadlec P, Gabrys B, Strandt S (2009) Data-driven soft sensors in the process industry. Computers Chemical Engineering 33(4):795-814
7. European Commision (2016) TOPIC:novel design and predictive maintenance technologies for increased operating life of production systems. http://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/fof-09-2017.html, Accessed 8th of December 2016
8. Samuelson D (2016) AI takeover:Google's ‘DeepMind’ platform can learn and think on it's own without human input. http://glitch.news/2016-11-04-ai-takeover-googles-deepmind-platform-canlearn-and-think-on-its-own-without-human-input.html. Accessed 21st of March 2017
9. Sample I (2017) Google's DeepMind makes AI program that can learn like a human. https://www.theguardian.com/global/2017/mar/14/googles-deepmind-makes-ai-program-that-can-learn-likea-human. Accessed 21st of March 2017
10. Archetti F, Arosio G, Candelieri A et al (2014) Smart data driven maintenance:improving damage detection and assessment on aerospace structures. In:Proceedings of 2014 IEEE international workshop on metrology for aerospace, metroaerospace. Benevento, Italy. http://doi.org/10.1109/MetroAeroSpace.2014.6865902
11. Krueger M, Haghani A, Ding SX et al (2014) A data-driven maintenance support system for wind energy conversion systems. In:The 19th IFAC world congress on international federation of automatic control, IFAC 2014, August 24, 2014, Cape Town, South Africa
12. Kirkpatrick J, Pascanu R, Rabinowitz N et al (2016) Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences of the United States of America 114(13):3521
13. Rødseth H, Schjølberg P, Larsen LT (2016) Industrie 4.0:a new trend in predictive maintenance and maintenance management. In:Proceedings of Euro maintenance artion conferences & events, pp 267-273
14. Vogel-Heuser B, Hess D (2016) Guest editorial Industry 4.0:prerequisites and visions. IEEE Transactions on Automation Science and Engineering 13 (2):411-413
15. Stich V, Hering N, Meißner J (2015) Cyber physical production control:transparency and high resolution in production control. In:Umeda S et al (eds) IFIP Advances in Information and Communication Technology. Springer, Berlin, pp 308-315
16. Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for Industry 4.0:based manufacturing systems. Manufacturing Letters 3:18-23
17. DIN (2016) German standardization roadmap:Industry 4.0. 2nd edn. Berlin
18. Hu F, Lu Y, Vasilakos AV et al (2016) Robust cyber-physical systems:concept, models, and implementation. Future Generation Computer Systems 56:449-475
19. Lee J, Jin C, Bagheri B (2017) Cyber physical systems for predictive production systems. Production Engineering 11(2):155-165
20. Nienke S, Frölian H, Zeller V et al (2017) Energy-management 4.0:roadmap towards the self-optimizing production of the future. In:Proceeding of international conference of ACM. http://doi.org/10.1145/3070617.3070621
21. Schuh G, Anderi R, Gausemeier J et al (2017) Industrie 4.0 maturity index. Managing the Digital Transformation of Companies (acatech STUDY). Hebert Utz Verlag, Munich
22. Deming WE (2000) Out of the crisis. MIT Press, Cambridge, Mass
23. Witten I, Eibe F, Hall M et al (2016) Data mining practical machine learning tools and techniques. Morgan Kaufmann, San Francisco
24. Chappell D (2015) Introduction to Azure machine learning. Chappell & Associates, San Francisco
25. Palmer D (2013) Maintenance planning and scheduling handbook. McGraw-Hill, New York
26. Lachance P (2016) How the transportation industry can keep pace with mobile computerized maintenance management systems. ITE Journal (Institute of Transportation Engineers) 86(7):28-29
27. Labib A (2008) Computerised maintenance management systems. In:Complex system maintenance handbook. Springer, London, pp 417-435. http://doi.org/10.1007/978-1-84800-011-7_17
28. Lopes I, Senra P, Vilarinho S et al (2016) Requirements specification of a computerized maintenance management system:a case study. Procedia CIRP 52:268-273
29. Liebstückel K (2014) Plant maintenance with SAP:practical guide. SAP Press, Bonn & Boston
30. Microsoft (2017) Microsoft azure machine learning. https://azure.microsoft.com/nb-no/services/machine-learning/. Accessed 18th of February 2017
31. NASA (2008) Prognostics center of excellence PCoE datasets. https://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/#turbofan. Accessed 18th of February 2017

[1]	Yi Wang, Hai-Shu Ma, Jing-Hui Yang, Ke-Sheng Wang. Industry 4.0: a way from mass customization to mass personalization production[J]. Advances in Manufacturing, 2017, 5(4): 311-320.
[2]	C. K. M. Lee, S. Z. Zhang, K. K. H. Ng. Development of an industrial Internet of things suite for smart factory towards re-industrialization[J]. Advances in Manufacturing, 2017, 5(4): 335-343.
[3]	Jo Wessel Strandhagen, Erlend Alfnes, Jan Ola Strandhagen, Logan Reed Vallandingham. The fit of Industry 4.0 applications in manufacturing logistics: a multiple case study[J]. Advances in Manufacturing, 2017, 5(4): 344-358.
[4]	Jan Ola Strandhagen, Logan Reed Vallandingham, Giuseppe Fragapane, Jo Wessel Strandhagen, Aili Biriita Hætta Stangeland, Nakul Sharma. Logistics 4.0 and emerging sustainable business models[J]. Advances in Manufacturing, 2017, 5(4): 359-369.
[5]	Zhe Li, Yi Wang, Ke-Sheng Wang. Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario[J]. Advances in Manufacturing, 2017, 5(4): 377-387.
[6]	Wladimir Bodrow. Impact of Industry 4.0 in service oriented firm[J]. Advances in Manufacturing, 2017, 5(4): 394-400.
[7]	Ke-Sheng Wang, Zhe Li, Jørgen Braaten, Quan Yu. Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs[J]. Advances in Manufacturing, 2015, 3(2): 97-104.
[8]	Harald Rødseth, Terje Skarlo, Per Schjølberg. Profit loss indicator: a novel maintenance indicator applied for integrated planning[J]. Advances in Manufacturing, 2015, 3(2): 139-150.