Abstract
Excellent service is a key component of making industrial plants work without unexpected shutdowns and safety hazards to workers. Especially, up-to-date information regarding installed base is crucial to support the entire life cycle of systems and products as well as to provide tailored service offerings. However, the myriad and variety of industrial equipment and systems manufactured throughout various periods increase the effort related to the collection of corresponding installed base data. Moreover, organizational changes, such as corporate mergers or company take-overs can introduce additional complexities, such as intersecting serial numbers or the existence of heterogeneous identification plates. In addition, the time related to collecting installed base data is critical since it is often done during customer visits by well trained service engineers that have to focus on solving time-critical problems. Thus, the corresponding data is often processed slowly because of time consuming media conversion and paper work before and after the actual service work.
In this paper, we present an approach to collect installed base data utilizing wearable and mobile devices. Here, we use different interaction styles at the same time. The proposed approach falls back on using existing hardware components, voice commands, and QR codes in parallel.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Borchers, H.W., Karandikar, H.: A data warehouse approach for estimating and characterizing the installed base of industrial products. In: 2006 International Conference on Service Systems and Service Management (ICSSSM 2006), Troyes, France, October 2006. IEEE (2006)
Henderson, S., Feiner, S.: Exploring the benefits of augmented reality documentation for maintenance and repair. IEEE Trans. Vis. Comput. Graph. 17(10) 1355–1368
Naya, F., Ohmura, R., Takayanagi, F., Noma, H., Kogure, K.: Workers’ routine activity recognition using body movement and location information. In: 10th IEEE International Symposium on Wearable Computers (ISWC 2006), Montreux, Switzerland, October 2006. IEEE (2006)
Oliva, R., Kallenberg, R.: Managing the transition from products to services. Int. J. Service Industry Management 14(2), 160–172 (2003)
Roggen, D., Tröster, G., Lukowicz, P., Ferscha, A., Millan, D.R.J., Chavarrigiara, R.: Opportunistic human activity and context recognition. IEEE Comput. 46(2) 36–45
Stich, C.M., Petersen, H.: ServIS managing the installed base. ABB Research Center Germany - Annual Report, ABB (2007)
Acknowledgments
This research was supported by the German Federal Ministry of Education and Research (BMBF) under grant number 16KIS0244. The responsibility for this publication lies with the authors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Aleksy, M., Fantana, N. (2016). Utilizing Multiple Interaction Styles to Collect Installed Base Information Using Wearable and Mobile Devices. In: Younas, M., Awan, I., Kryvinska, N., Strauss, C., Thanh, D. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2016. Lecture Notes in Computer Science(), vol 9847. Springer, Cham. https://doi.org/10.1007/978-3-319-44215-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-44215-0_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44214-3
Online ISBN: 978-3-319-44215-0
eBook Packages: Computer ScienceComputer Science (R0)