Hostname: page-component-848d4c4894-sjtt6 Total loading time: 0 Render date: 2024-07-03T21:09:54.753Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning to predict characteristics for engineering service projects

Published online by Cambridge University Press:  01 December 2016

Lei Shi ,
Linda Newnes ,
Steve Culley  and
Bruce Allen
Lei Shi*
Affiliation:
Department of Mechanical Engineering, University of Bath, Bath, United Kingdom
Linda Newnes
Affiliation:
Department of Mechanical Engineering, University of Bath, Bath, United Kingdom
Steve Culley
Affiliation:
Department of Mechanical Engineering, University of Bath, Bath, United Kingdom
Bruce Allen
Affiliation:
AIRBUS Operations Ltd, Filton, United Kingdom
*
Reprint requests to: Lei Shi, Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK. E-mail: l.shi@bath.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

An engineering service project can be highly interactive, collaborative, and distributed. The implementation of such projects needs to generate, utilize, and share large amounts of data and heterogeneous digital objects. The information overload prevents the effective reuse of project data and knowledge, and makes the understanding of project characteristics difficult. Toward solving these issues, this paper emphasized the using of data mining and machine learning techniques to improve the project characteristic understanding process. The work presented in this paper proposed an automatic model and some analytical approaches for learning and predicting the characteristics of engineering service projects. To evaluate the model and demonstrate its functionalities, an industrial data set from the aerospace sector is considered as a the case study. This work shows that the proposed model could enable the project members to gain comprehensive understanding of project characteristics from a multidimensional perspective, and it has the potential to support them in implementing evidence-based design and decision making.

Keywords

Characteristics LearningCharacteristics PredictionEngineering Service ProjectKnowledge Discovery
Type
Regular Articles
Information
AI EDAM , Volume 31 , Special Issue 3: Uncertainty Quantification for Engineering Design , August 2017 , pp. 313 - 326
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ahmad, S., Mallick, D.N., & Schroeder, R.G. (2013). New product development: impact of project characteristics and development practices on performance. Journal of Product Innovation Management 30(2), 331–348.Google Scholar
Baines, T.S., Lightfoot, H.W., Evans, S., Neely, A., Greenough, R., Peppard, J., et al. (2007). State-of-the-art in product-service systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 221(10), 1543–1552.CrossRefGoogle Scholar
Chandrasegaran, S.K., Ramani, K., Sriram, R.D., Horváth, I., Bernard, A., Harik, R.F., & Gao, W. (2013). The evolution, challenges, and future of knowledge representation in product design systems. Computer-Aided Design 45(2), 204–228.CrossRefGoogle Scholar
Chen, H., Chiang, R.H., & Storey, V.C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly 36(4), 1165–1188.Google Scholar
Cho, K., Hong, T., & Hyun, C. (2009). Effect of project characteristics on project performance in construction projects based on structural equation model. Expert Systems With Applications 36(7), 10461–10470.CrossRefGoogle Scholar
Choudhary, A.K., Harding, J.A., & Tiwari, M.K. (2009). Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing 20(5), 501–521.CrossRefGoogle Scholar
Chuang, P.-T. (2007). Combining service blueprint and FMEA for service design. Service Industries Journal 27(2), 91–104.CrossRefGoogle Scholar
Chungoora, N., Young, R.I., Gunendran, G., Palmer, C., Usman, Z., Anjum, N.A., et al. (2013). A model-driven ontology approach for manufacturing system interoperability and knowledge sharing. Computers in Industry 64(4), 392–401.CrossRefGoogle Scholar
Doultsinou, A., Roy, R., Baxter, D., Gao, J., & Mann, A. (2009). Developing a service knowledge reuse framework for engineering design. Journal of Engineering Design 20(4), 389–411.Google Scholar
Dudoit, S., & Fridlyand, J. (2002). A prediction-based resampling method for estimating the number of clusters in a data set. Genome Biology 3(7), 1–21.CrossRefGoogle Scholar
Engwall, M., & Jerbrant, A. (2003). The resource allocation syndrome: the prime challenge of multi-project management? International Journal of Project Management 21(6), 403–409.CrossRefGoogle Scholar
Feng, G., Cui, D., Wang, C., & Yu, J. (2009). Integrated data management in complex product collaborative design. Computers in Industry 60(1), 48–63.Google Scholar
Gheyas, I.A., & Smith, L.S. (2010). Feature subset selection in large dimensionality domains. Pattern Recognition 43(1), 5–13.Google Scholar
Goh, Y.M., Newnes, L., Settanni, E., Thenent, N., & Parry, G. (2015). Addressing uncertainty in estimating the cost for a product-service-system delivering availability: epistemology and ontology. In Ontology Modeling in Physical Asset Integrity Management (Ebrahimipour, V., & Yacout, S., Eds.), pp. 199–219. Cham, Switzerland: Springer.Google Scholar
Gopsill, J., Jones, S., Snider, C., Shi, L., McMahon, C., & Hicks, B. (2014). Understanding the engineering design process through the evolution of engineering digital objects. Proc. 13th Int. Design Conf. (DESIGN 2014), Dubrovnik, Croatia, May 19–May 22.Google Scholar
Griffin, A. (1997). The effect of project and process characteristics on product development cycle time. Journal of Marketing Research 34(1), 24–35.Google Scholar
Harding, J., Shahbaz, M., & Kusiak, A. (2006). Data mining in manufacturing: a review. Journal of Manufacturing Science and Engineering 128(4), 969–976.CrossRefGoogle Scholar
Jones, S., Payne, S., Hicks, B., Gopsill, J., Snider, C., & Shi, L. (2015). Subject lines as sensors: co-word analysis of email to support the management of collaborative engineering work. Proc. Int. Conf. Engineering Design 2015 (ICED 2015). Milan, Italy, July 27–30.Google Scholar
Kamsu-Foguem, B., Rigal, F., & Mauget, F. (2013). Mining association rules for the quality improvement of the production process. Expert Systems With Applications 40(4), 1034–1045.Google Scholar
Kankar, P.K., Sharma, S.C., & Harsha, S.P. (2011). Fault diagnosis of ball bearings using machine learning methods. Expert Systems With Applications 38(3), 1876–1886.Google Scholar
Köksal, G., Batmaz, İ., & Testik, M.C. (2011). A review of data mining applications for quality improvement in manufacturing industry. Expert Systems With Applications 38(10), 13448–13467.Google Scholar
Li, C., McMahon, C., & Newnes, L. (2009). Annotation in product lifecycle management: a review of approaches. Proc. ASME 2009 Int. Design Engineering Technical Conf./Computers and Information in Engineering Conf., pp. 797–806. New York: American Society of Mechanical Engineers.Google Scholar
Li, Y.-F., Xie, M., & Goh, T.N. (2009). A study of project selection and feature weighting for analogy based software cost estimation. Journal of Systems and Software 82(2), 241–252.Google Scholar
Luchs, M., & Swan, K.S. (2011). Perspective: the emergence of product design as a field of marketing inquiry. Journal of Product Innovation Management 28(3), 327–345.Google Scholar
Meredith, J.R., & Mantel, S.J. Jr. (2011). Project Management: A Managerial Approach. Hoboken, NJ: Wiley.Google Scholar
Mesihovic, S., Malmqvist, J., & Pikosz, P. (2004). Product data management system-based support for engineering project management. Journal of Engineering Design 15(4), 389–403.Google Scholar
Paroutis, S., & Al Saleh, A. (2009). Determinants of knowledge sharing using Web 2.0 technologies. Journal of Knowledge Management 13(4), 52–63.Google Scholar
Pascal, A., Thomas, C., & Romme, A.G.L. (2013). Developing a human-centred and science-based approach to design: the knowledge management platform project. British Journal of Management 24(2), 264–280.Google Scholar
Petersen, K.J., Handfield, R.B., & Ragatz, G.L. (2003). A model of supplier integration into new product development. Journal of Product Innovation Management 20(4), 284–299.Google Scholar
Rudin, C., Waltz, D., Anderson, R.N., Boulanger, A., Salleb-Aouissi, A., Chow, M., et al. (2012). Machine learning for the New York City power grid. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(2), 328–345.Google Scholar
Sahin, F., Yavuz, M.Ç., Arnavut, Z., & Uluyol, Ö. (2007). Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization. Parallel Computing 33(2), 124–143.Google Scholar
Settanni, E., Thenent, N.E., Newnes, L.B., Parry, G., & Goh, Y.M. (2015). To cost an elephant: an exploratory survey on cost estimating practice in the light of product-service-systems. Journal of Cost Analysis and Parametrics 8(1), 1–22.Google Scholar
Shi, L., Gopsill, J., Newnes, L., & Culley, S. (2014). A sequence-based approach to analysing and representing engineering project normality. Proc. IEEE 26th Int. Conf. Tools With Artificial Intelligence (ICTAI), pp. 967–973. Washington, DC: IEEE Computer Society.Google Scholar
Shi, L., Gopsill, J., Snider, C., Jones, S., Newnes, L., & Culley, S. (2014). Towards identifying pattern in engineering documents to aid project planning. Proc. 13th Int. Design Conf. (DESIGN 2014), Dubrovnik, Croatia, May 19–May 22.Google Scholar
Shi, L., Newnes, L., Culley, S., & Snide, C. (2015). Process reconstruction and visualisation for collaborative engineering projects. Proc. 13th Int. Conf. Manufacturing Research, Bath, UK, September 8–10.Google Scholar
Shi, L., & Setchi, R. (2013). Enhanced semantic representation for improved ontology-based information retrieval. International Journal of Knowledge-Based and Intelligent Engineering Systems 17(2), 127–136.Google Scholar
Snider, C., Jones, S., Gopsill, J., Shi, L., & Hicks, B. (2014). A framework for the development of characteristic signatures of engineering projects. Proc. 13th Int. Design Conf. (DESIGN 2014), Dubrovnik, Croatia, May 19–May 22.Google Scholar
Wagstaff, K.L. (2012). Machine learning that matters. Proc. 29th Int. Conf. Machine Learning (ICML-12), Edinburgh, Scotland, June 26–July 1.Google Scholar
Walter, G. (2014). Determining the local acceptance of wind energy projects in Switzerland: the importance of general attitudes and project characteristics. Energy Research & Social Science 4, 78–88.Google Scholar
Wang, K. (2007). Applying data mining to manufacturing: the nature and implications. Journal of Intelligent Manufacturing 18(4), 487–495.Google Scholar
Wasiak, J., Hicks, B., Newnes, L., Loftus, C., Dong, A., & Burrow, L. (2011). Managing by e-mail: what e-mail can do for engineering project management. IEEE Transactions on Engineering Management 58(3), 445–456.CrossRefGoogle Scholar
Widodo, A., & Yang, B.-S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing 21(6), 2560–2574.CrossRefGoogle Scholar
Wu, D., Thames, J.L., Rosen, D.W., & Schaefer, D. (2012). Towards a cloud-based design and manufacturing paradigm: looking backward, looking forward. Proc. ASME 2012 Int. Design Engineering Technical Conf./Computers and Information in Engineering Conf., pp. 315–328. New York: American Society of Mechanical Engineers.Google Scholar
Zhang, D., Hu, D., Xu, Y., & Zhang, H. (2012). A framework for design knowledge management and reuse for product-service systems in construction machinery industry. Computers in Industry 63(4), 328–337.CrossRefGoogle Scholar
Zhen, L., Jiang, Z., & Song, H.-T. (2011). Distributed knowledge sharing for collaborative product development. International Journal of Production Research 49(10), 2959–2976.Google Scholar