Artificial Intelligence based Asset Management

Mattioli, Juliette; Perico, Paolo; Robic, Pierre-Olivier

doi:10.1109/sose50414.2020.9130505

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…30 Gap analysis -The need for data-driven decision support in multiple application areas within railways can be realized from the substantial amount of research on digitalization of AM within railways. [31][32][33][34][35] AM for railways focusses mainly on railway infrastructure such as railway track, 36 interdependencies between railway assets 37 and performance optimization of assets in heavy haul rail. 38 AM for railway infrastructure is based on adoption of various AI techniques like maintenance optimization modelling for railway track by Andrews, [39][40][41][42] Computerized Maintenance Managemtent Systems (CMMS), DSSs, reliability analysis and lifecycle costing for infrastructure, 43 the use of clustering techniques and petri net modelling for intelligent AM for rail earthworks, 44 intelligent AM with decision support for railway signaling system, 45 and intelligent transport system for the European Railway Traffic Management System (ERTMS).…”

Section: Introductionmentioning

confidence: 99%

Augmented asset management in railways – Issues and challenges in rolling stock

Kumari

Karim

Thaduri

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part F:

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Managing assets in railway, including infrastructure and rolling stock, efficiently and effectively is challenging. The emerging digital technologies and Artificial Intelligence (AI) are expected to augment the decision making in Asset Management (AM) and Fleet Management (FM). The AI technologies need to be adapted to the specific needs of any industrial domain, e.g. railways, to facilitate the implementation and achievement of the overall business goals. This adaptation is denoted as ‘Industrial AI’(IAI). IAI for railways infrastructure and rolling stock, is dependent on an appropriate technology roadmap reflecting necessary know-hows. The IAI roadmap aims to provide a strategic and executive plan to augment managing railway assets i.e. ‘Augmented Asset Management (AAM)’. AAM can be applied through an end-to-end secure platform for e.g. data sharing among stakeholders, the development of analytics, and model sharing through distributed computing. AAM in railways can be enhanced through implementation of a generic fleet management (FM) approach. In the FM approach, any population of assets with common characteristics and also the relationship of the asset to the fleet is considered. This paper aims to develop and propose a concept for AAM enabled through IAI and digital technologies to provide augmented decision support through a secure platform, for AM in railways. A FM approach towards a holistic operation and maintenance of assets, based on a System of Systems thinking, for AAM in railways is applied for population of infrastructure assets and rolling stock assets with common characteristics. Finally, a taxonomy of issues and challenges, in the application of AAM to FM in railways is provided. The data for this taxonomy has been collected from railway organizations through iterative rounds of interviews. This taxonomy can be used for research and development of frameworks, approaches, technologies, and methodologies for AAM in railways.

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Section: Introductionmentioning

confidence: 99%

Augmented asset management in railways – Issues and challenges in rolling stock

Kumari

Karim

Thaduri

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part F:

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“…The development of artificial intelligence (AI) for process monitoring has been ongoing since the mid‐1980s 1, 2, but AI has not been widely adopted until very recently. With the rapid evolution of computational capacity, AI has reached a considerable level of maturity and is increasingly applied in many areas of process analysis and optimization as well as asset management 3–5. However, until recently, the application of AI‐based methods was largely limited to the awareness level of the Gartner AI maturity model 6; 75 % of the companies included in a 2018 Gartner survey were at Level 1: awareness – being interested in AI but applying it only to select use cases or pioneering AI techniques.…”

Section: Introductionmentioning

confidence: 99%

Implementation Guideline for an Artificial Intelligence‐Based Process Analysis and Optimization Application

Kisslinger

Hildinger

2021

Chemie Ingenieur Technik

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Using predictive and proactive maintenance methods for critical equipment and processes in industry can help reduce unplanned downtimes and improve process availability and stability. Artificial intelligence (AI) methods for process data analysis can provide plant operators with much-needed insights for decision support and predictive plant maintenance strategies. In this article, a guideline for implementing AI methods in the process industry is provided using the AI-based application Siemens Predictive Analytics (SiePA) and the results of a SiePA application in a milling process are presented.

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