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Application of a hidden Markov Model for consistency checking of process plant facility tag numbers: A case study

Sivaramakrishnan, J. and Lee, G. (2018) Application of a hidden Markov Model for consistency checking of process plant facility tag numbers: A case study. In: 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), 26 - 28 April 2018, Singapore

Link to Published Version: https://doi.org/10.1109/IEA.2018.8387117
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Abstract

This paper proposes a novel method for validating process plant design data using a self-organising machine-learning approach. The method, based on a Hidden Markov Model (HMM), is ideal for embedding within a decision support system for use by engineers that validates tag numbering conventions during the design of a large process facility. Results are presented drawn from a set of 541 artificial tag numbers and show that the HMM's performance is comparable to that of a custom-made design rule checking algorithm. The approach benefits from the increased interoperability resulting from widespread adoption of the ISO 15926 standard in industry.

Item Type: Conference Paper
Murdoch Affiliation: School of Engineering and Information Technology
URI: http://researchrepository.murdoch.edu.au/id/eprint/41561
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