Plausible petri nets as self-adaptive expert systems: a tool for infrastructure asset monitoring

Abstract

This paper provides a computational framework to model self-adaptive expert systems using the Petri net (PN) formalism. Self-adaptive expert systems are understood here as expert systems with the ability to autonomously learn from external inputs, like monitoring data. To this end, the Bayesian learning principles are investigated and also combined with the Plausible Petri nets methodology. Plausible Petri nets (PPNs) are a variant within the PN paradigm which are efficient to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about a state variable. The manuscript shows the mathematical conditions and computational procedure where the Bayesian updating becomes a particular case of a more general basic operation within the PPN execution semantics, which enables the uncertain knowledge being updated from monitoring data. The approach is general but here it is demonstrated in a novel computational model acting as expert system for railway track inspection management taken as case study using published data from a laboratory simulation of train loading on ballast. The results reveal self-adaptability and uncertainty management as key enabling aspects to optimise inspection actions in railway track, only being adaptively and autonomously triggered based on the actual learnt state of track and other contextual issues, like resource availability, as opposed to scheduled periodic maintenance activities.

Citation

Chiachío, M., Chiachío, J., Prescott, D., & Andrews, J. (2019). Plausible petri nets as self-adaptive expert systems: a tool for infrastructure asset monitoring. Computer-Aided Civil and Infrastructure Engineering, 34(4), 281-298. https://doi.org/10.1111/mice.12427

• Journal Article Type: Article
• Acceptance Date: Nov 5, 2018
• Online Publication Date: Dec 12, 2018
• Publication Date: Apr 1, 2019
• Deposit Date: Nov 16, 2018
• Publicly Available Date: Dec 13, 2019
• Journal: Computer-Aided Civil and Infrastructure Engineering
• Print ISSN: 1093-9687
• Electronic ISSN: 1467-8667
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 34
• Issue: 4
• Pages: 281-298
• DOI: https://doi.org/10.1111/mice.12427
• Public URL: https://nottingham-repository.worktribe.com/output/1273317
• Publisher URL: https://onlinelibrary.wiley.com/doi/full/10.1111/mice.12427