2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI) (2014)
Nov. 10, 2014 to Nov. 12, 2014
Engineering projects are often highly complex, unique and safety critical, which can lead to the complex engineering processes and activity. To ensure the success of engineering projects, the projects often have to comply with stringent regulations and company processes. In addition, the increasing in-service lifespan of products has led to an increase in the number of re-design and maintenance projects. These are often run concurrently in a highly time-constrained and high-pressured environment, which has led to the monitoring of the sequence of engineering activity becoming difficult. This is because, the sequence of engineering activity is typically achieved through the ability of the project managers to use their knowledge, experience and constant contact with the engineers. However, the viability of the current method to manually generate and evaluate the activity plan is becoming an issue due to the increasing number and distributed nature of these projects. As regulatory and/or company process demands, the data relating to the project is often archived and thus, provides a wealth of potentially useful information that could be utilised in the management of current projects. Therefore, this research investigates the potential value provided by the automatic construction of past project activity sequences, and proposes analytical methods to represent the normality of project activity based on the extracted patterns from their sequences. The evaluation applies industrial data, and shows that the results generated by the proposed approach can accurately reflect the similarity and normality of the projects.
Companies, Data mining, IEEE Potentials, Knowledge engineering, Equations, Mathematical model, Maintenance engineering
L. Shi, J. A. Gopsill, L. Newnes and S. Culley, "A Sequence-Based Approach to Analysing and Representing Engineering Project Normality," 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), Limassol, Cyprus, 2014, pp. 967-973.