19 research outputs found
Plausible Petri nets as self-adaptive expert systems: A tool for infrastructure asset monitoring
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.Lloyd'sRegister Foundation, Grant/Award
Number: RB4539; Engineering and Physical
SciencesResearch Council, Grant/Award
Number:EP/M023028/
Adapting Railway Maintenance to Climate Change
Railway infrastructure is vulnerable to extreme weather events such as elevated temperature, flooding, storms, intense winds, sea level rise, poor visibility, etc. These events have extreme consequences for the dependability of railway infrastructure and the acceptable level of services by infrastructure managers and other stakeholders. It is quite complex and difficult to quantify the consequences of climate change on railway infrastructure because of the inherent nature of the railway itself. Hence, the main aim of this work is to qualitatively identify and assess the impact of climate change on railway infrastructure with associated risks and consequences. A qualitative research methodology is employed in the study using a questionnaire as a tool for information gathering from experts from several municipalities in Sweden, Swedish transport infrastructure managers, maintenance organizations, and train operators. The outcome of this questionnaire revealed that there was a lower level of awareness about the impact of climate change on the various facets of railway infrastructure. Furthermore, the work identifies the challenges and barriers for climate adaptation of railway infrastructure and suggests recommended actions to improve the resilience towards climate change. It also provides recommendations, including adaptation options to ensure an effective and efficient railway transport service
Maintenance of Railway Infrastructure Using Cyber-Physical Systems
Cyber-physical systems (CPS) facilitate the recent advancements in manufacturing to make it a comprehensive system that incorporates computational intelligence, communication technologies, context-awareness and data analytics. The potential of CPS is not only confined to manufacturing but are also applicable to other complex infrastructure systems that necessitate improved life cycle and asset management system. The maintenance of such a system of systems necessitates a holistic view of the infrastructure for effective decision support methodologies. This paper discusses infrastructure maintenance from the viewpoint of life cycle management within the structure of cyber-physical systems. This paper also discusses several assisting technologies to support the development of CP. In addition, some use cases are provided from the literature and based on their experience, a CPS framework is developed for Swedish Railway Infrastructure. This CPS system also enables the development of Digital Twin for Railways.ISBN för värdpublikation: 978-981-15-3642-7, 978-981-15-3643-4</p