@misc{10481/61537,
year = {2019},
url = {http://hdl.handle.net/10481/61537},
abstract = {This article 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 PNs (PPNs) methodology. 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 a case study using published
data from a laboratory simulation of train loading on ballast. The results reveal selfadaptability
and uncertainty management as key enabling aspects to optimize 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.},
organization = {Lloyd'sRegister Foundation, Grant/Award
Number: RB4539; Engineering and Physical
SciencesResearch Council, Grant/Award
Number:EP/M023028/1},
publisher = {Wiley},
title = {Plausible Petri nets as self-adaptive expert systems: A tool for infrastructure asset monitoring},
doi = {10.1111/mice.12427},
author = {Chiachío Ruano, Manuel and Chiachío Ruano, Juan and Prescott, Darren and Andrews, John},
}