Conference paper
Application of a hidden Markov Model for consistency checking of process plant facility tag numbers: A case study
2018 5th International Conference on Industrial Engineering and Applications (ICIEA)
2018 5th International Conference on Industrial Engineering and Applications (ICIEA) (Singapore, 26/04/2018–28/04/2018)
2018
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.
Details
- Title
- Application of a hidden Markov Model for consistency checking of process plant facility tag numbers: A case study
- Authors/Creators
- J. Sivaramakrishnan (Author/Creator) - Murdoch UniversityG. Lee (Author/Creator) - Murdoch University
- Publication Details
- 2018 5th International Conference on Industrial Engineering and Applications (ICIEA)
- Conference
- 2018 5th International Conference on Industrial Engineering and Applications (ICIEA) (Singapore, 26/04/2018–28/04/2018)
- Identifiers
- 991005542027807891
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Conference paper
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