Skip to main content
    • Aa
    • Aa

Learning to predict characteristics for engineering service projects

  • Lei Shi (a1), Linda Newnes (a1), Steve Culley (a1) and Bruce Allen (a2)

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.

Corresponding author
Reprint requests to: Lei Shi, Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK. E-mail:
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

S. Ahmad , D.N. Mallick , & R.G. Schroeder (2013). New product development: impact of project characteristics and development practices on performance. Journal of Product Innovation Management 30(2), 331348.

T.S. Baines , H.W. Lightfoot , S. Evans , A. Neely , R. Greenough , J. Peppard , (2007). State-of-the-art in product-service systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 221(10), 15431552.

S.K. Chandrasegaran , K. Ramani , R.D. Sriram , I. Horváth , A. Bernard , R.F. Harik , & W. Gao (2013). The evolution, challenges, and future of knowledge representation in product design systems. Computer-Aided Design 45(2), 204228.

K. Cho , T. Hong , & C. Hyun (2009). Effect of project characteristics on project performance in construction projects based on structural equation model. Expert Systems With Applications 36(7), 1046110470.

A.K. Choudhary , J.A. Harding , & M.K. Tiwari (2009). Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing 20(5), 501521.

P.-T. Chuang (2007). Combining service blueprint and FMEA for service design. Service Industries Journal 27(2), 91104.

N. Chungoora , R.I. Young , G. Gunendran , C. Palmer , Z. Usman , N.A. Anjum , (2013). A model-driven ontology approach for manufacturing system interoperability and knowledge sharing. Computers in Industry 64(4), 392401.

A. Doultsinou , R. Roy , D. Baxter , J. Gao , & A. Mann (2009). Developing a service knowledge reuse framework for engineering design. Journal of Engineering Design 20(4), 389411.

S. Dudoit , & J. Fridlyand (2002). A prediction-based resampling method for estimating the number of clusters in a data set. Genome Biology 3(7), 121.

M. Engwall , & A. Jerbrant (2003). The resource allocation syndrome: the prime challenge of multi-project management? International Journal of Project Management 21(6), 403409.

G. Feng , D. Cui , C. Wang , & J. Yu (2009). Integrated data management in complex product collaborative design. Computers in Industry 60(1), 4863.

J. Harding , M. Shahbaz , & A. Kusiak (2006). Data mining in manufacturing: a review. Journal of Manufacturing Science and Engineering 128(4), 969976.

B. Kamsu-Foguem , F. Rigal , & F. Mauget (2013). Mining association rules for the quality improvement of the production process. Expert Systems With Applications 40(4), 10341045.

P.K. Kankar , S.C. Sharma , & S.P. Harsha (2011). Fault diagnosis of ball bearings using machine learning methods. Expert Systems With Applications 38(3), 18761886.

Y.-F. Li , M. Xie , & T.N. Goh (2009). A study of project selection and feature weighting for analogy based software cost estimation. Journal of Systems and Software 82(2), 241252.

S. Mesihovic , J. Malmqvist , & P. Pikosz (2004). Product data management system-based support for engineering project management. Journal of Engineering Design 15(4), 389403.

S. Paroutis , & A. Al Saleh (2009). Determinants of knowledge sharing using Web 2.0 technologies. Journal of Knowledge Management 13(4), 5263.

A. Pascal , C. Thomas , & A.G.L. Romme (2013). Developing a human-centred and science-based approach to design: the knowledge management platform project. British Journal of Management 24(2), 264280.

C. Rudin , D. Waltz , R.N. Anderson , A. Boulanger , A. Salleb-Aouissi , M. Chow , (2012). Machine learning for the New York City power grid. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(2), 328345.

F. Sahin , M.Ç. Yavuz , Z. Arnavut , & Ö. Uluyol (2007). Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization. Parallel Computing 33(2), 124143.

L. Shi , & R. Setchi (2013). Enhanced semantic representation for improved ontology-based information retrieval. International Journal of Knowledge-Based and Intelligent Engineering Systems 17(2), 127136.

G. Walter (2014). Determining the local acceptance of wind energy projects in Switzerland: the importance of general attitudes and project characteristics. Energy Research & Social Science 4, 7888.

J. Wasiak , B. Hicks , L. Newnes , C. Loftus , A. Dong , & L. Burrow (2011). Managing by e-mail: what e-mail can do for engineering project management. IEEE Transactions on Engineering Management 58(3), 445456.

A. Widodo , & B.-S. Yang (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing 21(6), 25602574.

D. Zhang , D. Hu , Y. Xu , & H. Zhang (2012). A framework for design knowledge management and reuse for product-service systems in construction machinery industry. Computers in Industry 63(4), 328337.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 13
Total number of PDF views: 40 *
Loading metrics...

Abstract views

Total abstract views: 336 *
Loading metrics...

* Views captured on Cambridge Core between 1st December 2016 - 25th September 2017. This data will be updated every 24 hours.