Railway Assets: A Potential Domain for Big Data Analytics

Thaduri, Adithya; Galar, Diego; Kumar, Uday

doi:10.1016/j.procs.2015.07.323

Cited by 119 publications

(53 citation statements)

References 20 publications

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“…Future research steps in the context of this specific application include the consideration of other subsystems within the expert system model, such as signaling, electrification, switchings and crossings, etc. Building on this work, one desirable future research direction to enhance PPNs as self‐adaptive expert systems for complex civil infrastructures is to explore PPN architectures that allow the modelng of foreseen intervention scenarios. Also, an additional challenge would be how to incorporate massive and heterogenous data (Thaduri, Galar, & Kumar, ; Thöns, ) from infrastructure monitoring within the PPN methodology.…”

Section: Discussionmentioning

confidence: 99%

“…• Building on this work, one desirable future research direction to enhance PPNs as self-adaptive expert systems for complex civil infrastructures is to explore PPN architectures that allow the modelng of foreseen intervention scenarios. Also, an additional challenge would be how to incorporate massive and heterogenous data (Thaduri, Galar, & Kumar, 2015;Thöns, 2018) from infrastructure monitoring within the PPN methodology.…”

Section: Discussionmentioning

confidence: 99%

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Plausible Petri nets as self‐adaptive expert systems: A tool for infrastructure asset monitoring

Chiachío

Prescott

et al. 2018

Computer aided Civil Eng

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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 self‐adaptability 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Plausible Petri nets as self‐adaptive expert systems: A tool for infrastructure asset monitoring

Chiachío

Prescott

et al. 2018

Computer aided Civil Eng

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“…Big data analytics is a vast field that has found applications in diverse domains and studies. Some of the most impactful researches that have merged big data analytics with other fields of study include business analytics, multi-scale climate data analytics [11], banking customer analytics [14], smart cities [16], recommender systems for ecommerce [13], social media analytics [12], healthcare data analytics [15], intelligent transport management systems [18] and railway assets management system [17].…”

Section: Background and Methodologymentioning

confidence: 99%

A survey on scholarly data: From big data perspective

Khan

Liu

Shakil

et al. 2017

Information Processing & Management

117

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With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

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“…Moreover, in the railway industry, WSNs can play a substantial role in the renovation of old systems with the state-of-the-art electrical systems. Despite the many purposes of wireless sensor network applications in railway infrastructure in railway tracks, WSNs need to be explored for rational application in railway stations environment [9]. The use of WSNs in railway stations is expected to aid growing consumption and high-energy costs by curbing energy demand and CO 2 emissions, and boosting energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Networks: Toward Smarter Railway Stations

Alawad

Kaewunruen

2018

Infrastructures

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Railway industry plays a critical role in transportation and transit systems attributed to the ever-growing demand for catering to both freight and passengers. However, owing to many challenges faced by railway stations such as harsh environments, traffic flow, safety and security risks, new and adaptive systems employing new technology are recommended. In this review, several wireless sensor networks (WSNs) applications are proposed for use in railway station systems, including advanced WSNs, which will enhance security, safety, and decision-making processes to achieve more cost-effective management in railway stations, as well as the development of integrated systems. The size, efficiency, and cost of WSNs are influential factors that attract the railway industry to adopt these devices. This paper presents a review of WSNs that have been designed for uses in monitoring and securing railway stations. This article will first briefly focus on the presence of different WSN applications in diverse applications. In addition, it is important to note that exploitation of the state-of-the-art tools and techniques such as WSNs to gain an enormous amount of data from a railway station is a new and novel concept requiring the development of artificial intelligence methods, such machine learning, which will be vital for the future of the railway industry.

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Railway Assets: A Potential Domain for Big Data Analytics

Cited by 119 publications

References 20 publications

Plausible Petri nets as self‐adaptive expert systems: A tool for infrastructure asset monitoring

Plausible Petri nets as self‐adaptive expert systems: A tool for infrastructure asset monitoring

A survey on scholarly data: From big data perspective

Wireless Sensor Networks: Toward Smarter Railway Stations

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