| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Caretta, Martina Angela, et al.
(författare)
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Water
- 2022
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Ingår i: Climate Change 2022: Impacts, Adaptation and Vulnerability : Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change - Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 3. |
- Kour, Ravdeep, 1981-, et al.
(författare)
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Metaverse for Intelligent Asset Management
- 2022
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Ingår i: 2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM). - : IEEE.
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Konferensbidrag (refereegranskat)
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| 4. |
- Thaduri, Adithya, et al.
(författare)
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Maintenance of Railway Infrastructure Using Cyber-Physical Systems
- 2020
-
Ingår i: Decision Analytics Applications in Industry. - Singapore : Springer. ; , s. 521-540
-
Bokkapitel (refereegranskat)abstract
- 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.
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| 5. |
- Chamkhorami, Khosro Soleimani, et al.
(författare)
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Implications of Climate Change in Life Cycle Cost Analysis of Railway Infrastructure
- 2023
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Ingår i: Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023). - : Research Publishing. ; , s. 2089-2096
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Konferensbidrag (populärvet., debatt m.m.)abstract
- Extreme weather conditions from climate change, including high or low temperatures, snow and ice, flooding,storms, sea level rise, low visibility, etc., can damage railway infrastructure. These incidents severely affect the reliability of the railway infrastructure and the acceptable service level. Due to the inherent complexity of the railway system, quantifying the impacts of climate change on railway infrastructure and associated expenses has been challenging. To address these challenges, railway infrastructure managers must adopt a climate-resilient approach that considers all cost components related to the life cycle of railway assets. This approach involves implementing climate adaptation measures to reduce the life cycle costs (LCC) of railway infrastructure while maintaining the reliability and safety of the network. Therefore, it is critical for infrastructure managers to predict, "How will maintenance costs be affected due to climate change in different RCP's scenarios?"The proposed model integrates operation and maintenance costs with reliability and availability parameters such as mean time to failure (MTTF) and mean time to repair (MTTR). The proportional hazard model (PHM) is used to reflect the dynamic effect of climate change by capturing the trend variation in MTTF and MTTR. A use case from a railway in North Sweden is studied and analyzed to validate the process. Data collected over a 20-year period is analyzed for the chosen use case. As a main result, this study has revealed that climate change may significantly influence the LCC of switch and crossing (S&C) and can help managers predict the required budget.
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| 6. |
-
Decision Analytics Applications in Industry
- 2020. - 1
-
Samlingsverk (redaktörskap) (refereegranskat)abstract
- This book presents a range of qualitative and quantitative analyses in areas such as cybersecurity, sustainability, multivariate analysis, customer satisfaction, parametric programming, software reliability growth modeling, and blockchain technology, to name but a few. It also highlights integrated methods and practices in the areas of machine learning and genetic algorithms. After discussing applications in supply chains and logistics, cloud computing, six sigma, production management, big data analysis, satellite imaging, game theory, biometric systems, quality, and system performance, the book examines the latest developments and breakthroughs in the field of science and technology, and provides novel problem-solving methods.The themes discussed in the book link contributions by researchers and practitioners from different branches of engineering and management, and hailing from around the globe. These contributions provide scholars with a platform to derive maximum utility in the area of analytics by subscribing to the idea of managing business through system sciences, operations, and management. Managers and decision-makers can learn a great deal from the respective chapters, which will help them devise their own business strategies and find real-world solutions to complex industrial problems.
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| 7. |
- Galar, Diego, et al.
(författare)
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Advanced Analytics for Modern Mining
- 2022
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Ingår i: Advanced Analytics in Mining Engineering. - Cham : Springer Nature. ; , s. 23-54
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 8. |
- Galar, Diego, et al.
(författare)
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Big Data in Railway O&M: A Dependability Approach
- 2022
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Ingår i: Research Anthology on Big Data Analytics, Architectures, and Applications. - : IGI Global. ; , s. 391-416
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Railway systems are complex with respect to technology and operations with the involvement of a wide range of human actors, organizations and technical solutions. For the operations and control of such complexity, a viable solution is to apply intelligent computerized systems, for instance, computerized traffic control systems for coordinating airline transportation, or advanced monitoring and diagnostic systems in vehicles. Moreover, transportation assets cannot compromise the safety of the passengers by only applying operation and maintenance activities. Indeed, safety is a more difficult goal to achieve using traditional maintenance strategies and computerized solutions come into the picture as the only option to deal with complex systems interacting among them and trying to balance the growth in technical complexity together with stable and acceptable dependability indexes. Big data analytics are expected to improve the overall performance of the railways supported by smart systems and Internetbased solutions. Operation and Maintenance will be application areas, where benefits will be visible as a consequence of big data policies due to diagnosis and prognosis capabilities provided to the whole network of processes. This chapter shows the possibilities of applying the big data concept in the railway transportation industry and the positive effects on technology and operations from a systems perspective. © 2022 by IGI Global. All rights reserved.
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| 9. |
- Galar, Diego, et al.
(författare)
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Robotics and artificial intelligence (AI) for maintenance
- 2023
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Ingår i: Monitoring and Protection of Critical Infrastructure by Unmanned Systems. - : IOS Press. - 9781643683768 - 9781643683775 ; , s. 206-223
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This paper reviews the application of AI in maintenance and inspections. It gives an overview of the development of AVs and distant inspection operations for industrial assets using unmanned aerial vehicles (UAVs). It discusses the use of AVs in infrastructure inspection and explain the types of sensors used for these applications. It explains how autonomous robots, including drones, are currently used in various industrial settings for inspection and maintenance. The paper concludes by discussing the use of AI in predictive maintenance.
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| 10. |
- Galvez, Antonio
(författare)
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Hybrid digital twins: A co-creation of data science and physics
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Safety is more important than reliability or efficiency in railway, aerospace, oil & gas, and chemical industries. Regulations are very restrictive in sectors where safety is paramount. This makes maintainers replace critical components in initial stages of degradation, which implies a loss of useful life and a lack of information about advanced stages of degradation for those components. Nevertheless, this lack of data can be overcome using hybrid digital twins, also known as hybrid-model based approaches (HyMAs), which combine data-driven models with physics-based models. This fusion minimizes the occurrence of undesirable failures that may interrupt the functionality of critical systems in a safe or cost-efficient manner.HyMAs have been studied at Luleå University of Technology by other Ph.D. students who found promising direction for future research in prognostics and health management (PHM) applications. Thus, this research work continues the direction defined in previous research with the proposal of HyMAs for a heating, ventilation, and air conditioning (HVAC) system installed in a passenger train carriage orientated to diagnostics and prognostics processes. The proposed hybrid modelling consists of the fusion of data obtained from two sources: data obtained from the real system and synthetic data generated by a developed physics-based model of the HVAC.The HVAC system is considered a system of systems (SoS). Therefore, the physics-based model of the HVAC system is divided into four main systems: heating subsystem, cooling subsystem, ventilation subsystem, and cabin thermal networking subsystem. These subsystems are modelled considering the sensors installed in the real system and soft sensors, also known as virtual sensors, which provide crucial information for fault detection, diagnostics, and prognostics. These sensors defined in the physics-based model generate synthetic data which reproduce the behaviour of the system while a failure mode (FM) is simulated. Verification and validation are key processes to synchronise the response of the physics-based model with the signals obtained from the real system. Hence, the physics-based model is synchronised, verified, and validated using data collected by sensors located in the real system. These steps are conducted following guidelines suggested in the literature.Different datasets containing real data and synthetic data while the HVAC system works in faulty and healthy states are used to train data-driven models for fault detection and diagnostics and to train data-driven models for prognostics.Statistical features, such as shape factor, kurtosis, skewness, and sum square error, among others, are calculated from the selected signals. These features are labelled according to the related FMs and are merged with the features calculated from the data obtained from the real system. The data fusion is classified according to the condition indicators of the system in terms of FMs and level of degradation. The merged features are used to train data-driven models for fault detection and diagnostics. In addition, the real data can be loaded to the physics-based model to predict the degradation of the air filter.Then, the prediction data are loaded to an exponential model that provides an estimation of the remaining useful life (RUL) of the air filter. To improve the prognostics model, the physics-based model is used to generate run-to-failure data which are used to train and test a deep convolutional neural network (CNN) which accurately estimates the RUL of the air filter.
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| 11. |
- Garmabaki, Amir Soleimani, et al.
(författare)
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Adapting Railway Maintenance to Climate Change
- 2021
-
Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 13:24
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 12. |
- Garmabaki, Amir Soleimani, et al.
(författare)
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Climate Change Impact Assessment on Railway Maintenance
- 2022
-
Ingår i: Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022). - Singapore : Research Publishing.
-
Konferensbidrag (refereegranskat)abstract
- Modern societies have become more and more complex, interconnected, and heavily dependent ontransport infrastructure. Moreover, most transport infrastructures were conceptualized, designed and built withoutanticipating the future variations of climate change. Climate changes have a negative impact on the railway systemand related costs. Increased temperatures, precipitation, sea levels, and frequency of extremely adverse weatherevents such as floods, heatwaves, and heavy snowfall pose major risks and consequences for railway infrastructureassets, operations and maintenance. Approximately, 5 to 10% of total failures and 60% of delays of trains are dueto various climate change impacts of railway infrastructure in northern Europe. In Sweden, weather-related failureswere responsible for 50% of train delays in switches and crossings (S&C).The paper explores a pathway toward climate resilience in transport networks and assess the climate change impactson railway infrastructure by integrating transport infrastructure health information with meteorological, satellite,and expert knowledge. The paper provides recommendations considering adaptation options to ensure an effectiveand efficient railway transport operation and maintenance.
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| 13. |
- Garmabaki, Amir Soleimani, et al.
(författare)
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Underground pipelines and railway infrastructure : failure consequences and restrictions
- 2020
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Ingår i: Structure and Infrastructure Engineering. - : Taylor & Francis. - 1573-2479 .- 1744-8980. ; 16:3, s. 412-430
-
Tidskriftsartikel (refereegranskat)abstract
- Underground pipelines are an essential part of the transportation infrastructure. The structural deterioration of pipelines crossing railways and their subsequent failures can entail critical consequences for society and industry, resulting in direct and indirect costs for all the stakeholders involved. Therefore, continuous and accurate condition assessment is critical for the effective management and maintenance of pipeline networks within the transportation infrastructure. The aim of this study has been to identify failure modes and consequences related to pipelines crossing railway corridors. Expert opinions have been collected through interviews and two sets of questionnaires have been distributed to the 291 municipalities in Sweden, with 137 responses in total. The failure analysis has revealed that pipe deformation has the highest impact, followed by pipe rupture at locations where pipelines cross railway infrastructure. For underground pipelines under railway infrastructure, ageing and the external load were awarded a higher ranking than other potential causes of pipeline failure.Authors gratefully acknowledge the funding provided by Sweden’sinnovation agency, Vinnova, through the strategic innovation programmeInfraSweden2030. The funding was granted in a competitiveapplication process that assessed replies to an open call for proposalsconcerning “Condition Assessment and Maintenance of TransportInfrastructure (Grant No. 2016-033113)”.Authors gratefully acknowledge the technical support and collaboration(In-kind support) of Arrsleff R€orteknik at Sweden, Luleå RailwayResearch Center (JVTC), Stormwater&Sewers and the SwedishTransport Administration (Trafikverket). In addition, the authors arethankful to the anonymous referees for their constructive commentsand Dr Matthias Asplund and Dr Masoud Naseri for their support andsuggestions.
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| 14. |
- Heydari, Sasan, et al.
(författare)
-
Resilience measurement of longwall machinery
- 2020
-
Ingår i: Rudarsko-Geološko-Naftni Zbornik. - Croatia : Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb. - 0353-4529 .- 1849-0409. ; 35:3, s. 39-44
-
Tidskriftsartikel (refereegranskat)abstract
- This paper attempts to apply the resilience concept to the mining sector, especially to mining machinery and productionsystems. The quantitative analysis method using the linear recovery function has been applied. As the core part of theproposed method, it is assumed that in the mining machinery fleet, the performance function falls to a “zero” value immediatelyafter the occurrence of a failure. Therefore, the resilience calculation process runs through the concept of timeto repair and machine maintainability. As a case study for the proposed concept, the operation and failure data of thedrum shearer machine in Parvadeh longwall mine in Iran is applied. The data pertains to a coal cutting operation in awhole longwall panel over the period of two years. In total, the calculations encompass over 2600 hours of actual operationand 171.8 hours of repair time, which reveals that the studied shearer has a resilience of 96.7 percent. Along with thecase study results, it is confirmed by this paper that resilience as a developing concept could be adequately applied to coalmining systems as a support measure for production assurance and reliability.
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| 15. |
- Illankoon, Prasanna, 1977-
(författare)
-
Soft Issues of Industry 4.0 : A study on human-machine interactions
- 2020
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Autonomous industrial operations are becoming the norm due to advancements in technology, which has led to both advantages and disadvantages for the organisations involved. The use of intelligent systems has resulted in higher system reliability, a higher quality product, and reduced risk for human error. These systems collect large amounts of information, analyse them, make predictions, and take decisions, of which humans cannot do in the same capacity, have led to new and expanded levels of interactions. One key aspect concerns the fact that human interaction has decreased although has become more critical than before. Even if the systems are advanced and automated, human intervention is still necessary: such as maintenance actions, selection of data to train the system, and advanced decision making. Human intervention is especially crucial when dealing with complex and safety critical systems, where and when immediate interventions are required. Moreover, an expert human can improvise and make novel decisions in a capacity that present intelligent systems cannot. The problem is that both humans and machines need assistance to perform well. Autonomous operation is not perfect and when problems arise, humans must react. Although it is common that humans when not actively interacting with the system tend to lose perspective and find it difficult to quickly analyse a situation when it arises. Which means that they “fall out of the loop”. Their ability to gain a good understanding of the situation and make good decisions when the system suddenly needs their interaction is lost. In other words, humans have lost their situation awareness (SA) and a good SA it is needed in dynamic environments if they are to intervene quickly and successfully. If, and when a system can assist a human to quickly assess the situation and get back “into the loop” then the human can make educated decisions in a much quicker fashion. The purpose of this research was to explore and describe the importance of SA in maintenance and to recommend how to develop and provide better SA for intelligent maintenance systems (IMS).This thesis consists of a literature study conducted to develop the theoretical framework and two case studies were used to test the theoretical concepts. The thesis work tested five systematic methodologies to find suitable interventions to fulfil the SA requirements. The first case study focused on SA requirements during maintenance execution in a manufacturing organisation; there a quick return to production was the focus. The second case study was SA requirements in maintenance in the aviation domain, where safety is a top priority. The case study data were collected using interviews, observations, focus groups, and archival records. These qualitative data were analysed using qualitative content analysis, cognitive task analysis, and case taxonomic analysis.This work resulted in the identification of seven key SA requirements for maintenance: consisting of detection of abnormalities; diagnosing and predicting their behaviour; making changes in system configuration; compliance with maintenance standards; conducting effective maintenance judgements; maintenance teams; and for safe maintenance work. Five strategies to maintain SA were identified: explicit knowledge status, sense making, recognition primed decision making, skilled intuition, and heuristics. We also argue why IMS will make it difficult for humans to use most of these strategies to maintain SA in future. Finally, a new theoretical model for decision support (Distributed Collaborative Awareness Model) was developed. The study also shows how to apply these interventions in the railway maintenance sector. In conclusion, this study shows that in the maintenance domain, keeping humans in the loop requires a novel collaborative approach where the integration of the strengths of intelligent systems and human cognition is necessary. We also argue that a better understanding of SA strategies will lead to the further development of SA support for the human operator and maintenance technician.
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| 16. |
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| 17. |
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| 18. |
- Jena, Jajati K., et al.
(författare)
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Aggregated risk of a series of tunnels on Mumbai–Pune expressway
- 2020
-
Ingår i: International Journal of Systems Assurance Engineering and Management. - : Springer. - 0975-6809 .- 0976-4348. ; 11:July, issue 2, s. 357-366
-
Tidskriftsartikel (refereegranskat)abstract
- In the heavily industrialized western India, on the express way between Mumbai and Pune there is a series of five 03-lane one way tunnels. Risks in the tunnels on the hilly express way are sourced from the heavy goods vehicles often carrying dangerous goods. During the design stage, risk assessment is carried out using theoretical risk models such as OECD/PIARC DG-QRAM. However, during operational stage of a tunnel real time accident data are available to estimate the risk or fatality rate. In this paper a methodology has been developed to estimate aggregated risk of the series of 05 tunnels from the accident rate by Monte-Carlo simulation using simple random sampling technique. It has been found that major accident rate (or risk) is better represented by lognormal distribution.
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| 19. |
- Karim, Ramin, 1964-, et al.
(författare)
-
AI Factory -- A Framework for Digital Asset Management
- 2021
-
Ingår i: Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021). - Singapore : Research Publishing Services. ; , s. 1160-1167
-
Konferensbidrag (refereegranskat)abstract
- Advanced analytics empowered by Artificial Intelligence (AI) contributes to the achievement of global sustainability and business goals. It will also contribute to global competitiveness of enterprises through enablement of fact-based decisionmaking and improved insight. The digitalisation process currently ongoing in industry, and the corresponding implementation of AI technologies, requires availability and accessibility of data and models. Data and models are considered as digital assets (ISO55K) that impact a system’s dependability during its whole lifecycle. Digitalisation and implementation of AI in complex technical systems such as found in railway, mining, and aerospace industries is challenging. From a digital asset management perspective, the main challenges can be related to source integration, content processing, and cybersecurity.However, to effectively and efficiently retain the required performance of a complex technical system during its lifecycle, there is a need of appropriate concepts, methodologies, and technologies. With this background, Luleå University of Technology, in cooperation with a number of Swedish railway stakeholders – fleet managers, railway undertakings, infrastructure managers and Original Equipment Manufacturers (OEM), has created a universal platform called ‘the AI Factory’ (AIF). The concept of AIF has further been specialised for railway industry, so called AI Factory for Railway (AIF/R).Hence, this paper aims to provide a description of findings from the development and implementation of ‘AI Factory (AIF)’ in the railway context. Furthermore, the paper provides a case-study description used to verify the developed technologies and methodologies within AIF/R.
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| 20. |
- Karim, Ramin, et al.
(författare)
-
AI Factory: Theories, Applications and Case Studies
- 2023. - 1
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Bok (övrigt vetenskapligt/konstnärligt)abstract
- This book provides insights into how to approach and utilise data science tools, technologies, and methodologies related to artificial intelligence (AI) in industrial contexts. It explains the essence of distributed computing and AI technologies and their interconnections. It includes descriptions of various technology and methodology approaches and their purpose and benefits when developing AI solutions in industrial contexts. In addition, this book summarises experiences from AI technology deployment projects from several industrial sectors. Features:• Presents a compendium of methodologies and technologies in industrial AI and digitalisation.• Illustrates the sensor-to-actuation approach showing the complete cycle, which defines and differentiates AI and digitalisation.• Covers a broad range of academic and industrial issues within the field of asset management.• Discusses the impact of Industry 4.0 in other sectors.• Includes a dedicated chapter on real-time case studies.This book is aimed at researchers and professionals in industrial and software engineering, network security, AI and machine learning (ML), engineering managers, operational and maintenance specialists, asset managers, and digital and AI manufacturing specialists.
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| 21. |
- Karim, Ramin, et al.
(författare)
-
Preface
- 2022
-
Ingår i: International Congress and Workshop on Industrial AI 2021. - : Springer. - 9783030936389 - 9783030936396 ; , s. v-vi
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 22. |
- Kasraei, Ahmad, et al.
(författare)
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Climate Zone Reliability Analysis of Railway Assets
- 2024
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Ingår i: International Congress and Workshop on Industrial AI and eMaintenance 2023. - : Springer Science and Business Media Deutschland GmbH.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 23. |
- Khajehei, Hamid, 1987-, et al.
(författare)
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Investigation of Track Geometry Defects on a Heavy-Haul Railway Line
- 2021
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Ingår i: Journal of Transportation Engineering, Part A: Systems. - : American Society of Civil Engineers (ASCE). - 2473-2907 .- 2473-2893. ; 147:9
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents an in-depth case study of a heavy-haul railway line in Sweden to analyze the twist and longitudinal level geometry defects. A linear model was applied to model the evolution of the amplitude of the longitudinal level defects and twist over time. Despite the effect of the defect shapes on the dynamic track loads, the amplitude of the defects still is the only criterion used for the assessment of geometry defect severity. The application of first- and second-order derivatives to capture information about the shape of defects was investigated in the case study. In addition, the RUSBoost algorithm was used to classify track sections into healthy and unhealthy sections using the imbalance class data set. In this algorithm, the standard deviation and the kurtosis of the geometry parameters were used as explanatory variables. Finally, the abnormal track geometry degradation patterns identified in the case study were explored in detail. The results of the analysis can be used directly in maintenance modeling and used for the purpose of maintenance scheduling.
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| 24. |
- Khosravi, Mahdi, 1988-
(författare)
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Leveraging Data-Driven and Alignment Techniques for Optimal Railway Track Maintenance Scheduling
- 2023
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The railway infrastructure plays a pivotal role in fostering economic growth and poverty alleviation. Over time, these infrastructures deteriorate due to aging and usage, compromising their functionality. Given their crucial socioeconomic significance and vast scale, ensuring their functionality and availability is paramount. Thus, an effective condition-based maintenance program is essential to restore reliability, facilitate cost-effective restoration, and enable continued benefits.Track is a critical railway component susceptible to degradation from traffic loading, resulting in deviations from designated geometry parameters. Such degradation jeopardizes safety, availability, and travel quality. Developing an effective tamping regime emerges as a vital maintenance measure to control degradation and restore track geometry to acceptable standards. An optimal maintenance schedule becomes imperative to minimize costs, enhance track availability and capacity, and ensure safety.Achieving efficient tamping scheduling necessitates accurate prediction of geometry degradation, accounting for tamping effects, and modeling the evolution of single defects. However, uncontrolled shifts in geometry measurements from different inspections—known as positional errors—can misplace defects and distort their evolution analysis. Therefore, precise alignment of geometry measurements is vital to eliminate such positional errors.The purpose of this research was to streamline maintenance scheduling through leveraging track geometry measurements for modeling and prediction. Firstly, a study addressed alignment through evaluating and comparing four methods—Cross-Correlation Function (CCF), Recursive alignment by fast Fourier transform (RAFFT), Correlation optimized warping (COW), and Dynamic Time Warping (DTW). Furthermore, a combined RAFFT-COW method was proposed, overcoming their limitations. Comparison revealed COW aligned datasets satisfactorily without altering their shape but could not align endpoints precisely. The combined method effectively aligned datasets even when the datasets were stretched or compressed. Secondly, a modified COW (MCOW) addressed accurate and efficient alignment. MCOW surpassed COW's restrictions and reduced alignment time. To enhance robustness, MCOW with channel fusion (MFCOW) combined data from different channels, significantly reducing positional errors. Thirdly, a multi-objective approach proposed aimed at reducing positional errors in geometry measurements of track as a linear asset. Accordingly, recursive segment-wise peak alignment (RSPA) and MCOW were evaluated and compared. Furthermore, a novel rule-based approach proposed which prevented data loss during alignment, preserving all the single defects. In addition, the results revealed that RSPA excelled in aligning peaks, while MCOW proved efficient for datasets with equal priority data points.Finally, an optimization model minimized track geometry maintenance costs through tamping scheduling. Key track quality indicators, including the standard deviation of the longitudinal level and single defects and the impact of preventive/corrective tamping on these indicators were integrated. Results showcased the influence of fixed maintenance window costs and maintenance cycle intervals on tamping expenses. The model's validity was confirmed through interactions with experienced practitioners from prominent railway infrastructure and maintenance entities.
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| 25. |
- Kour, Ravdeep, 1981-
(författare)
-
Cybersecurity in Railway : A Framework for Improvement of Digital Asset Security
- 2020
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Digitalisation changes operation and maintenance in railways. Emerging digital technologies facilitate implementation of enhanced eMaintenance solutions through utilisation of distributed computing and artificial intelligence. In railway, the digital technology deployment is expected to improve the railway system’s sustainability, availability, reliability, maintainability, capacity, safety, and security including cybersecurity. In digitalised railway, aspects of cybersecurity are essential in order to achieve overall system dependability. Lack of cybersecurity imposes negative impacts on the railways like reputational damage, heavy costs, service unavailability and risk to the safety of employees and passengers.It has been observed, through open access data, that many railway organizations focus on detective measures of security threats with less emphasis on forecasting of cyber-attacks. In order to prepare in advance for cyberattacks, it is essential that Information and Communication Technology (ICT) and Operational Technology (OT) in railways need to undergo continuous updating towards security analytics approach. This approach will help the railways to produce proactive security measures to cyberattacks. In this work, it has been observed that there exists some standards and guidelines related to cybersecurity in railways (e.g. AS 7770- Rail Cyber Security, APTA SS-CCS-004-16, BS EN 50159:2010+A1:2020). These standards and guidelines are proprietary (i.e. either organization-specific or country-specific) and are followed by most of the railway organizations. These proprietary standards and guidelines lack in providing a holistic approach to enable interoperability, scalability, orchestration, adaptability, and agility for railway’s stakeholders. Therefore, there is a need for a generic cybersecurity framework for digitalized railways to facilitate proactive cybersecurity and threat intelligence sharing within the railways. The proposed framework, i.e., Cybersecurity Information Delivery Framework has been developed by integrating existing models, technologies, and standards to minimize the risks of cyber-attacks in the railways. The framework maps different layers of Open System Architecture for Condition-Based Maintenance (OSA-CBM) in the context of cybersecurity to deliver threat intelligence. The framework implements extended Cyber Kill Chain (CKC) and Industrial Control System (ICS) Kill Chain for detecting cyberattacks. The framework also incorporates proposed Railway Defender Kill Chain (RDKC) that enables proactive cybersecurity. Therefore, the proposed framework enables proactive cybersecurity and shares threat intelligence for improving cybersecurity in railways.
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| 26. |
- Kumar, Uday, et al.
(författare)
-
Editorial
- 2024
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Ingår i: International Congress and Workshop on Industrial AI and eMaintenance 2023. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 - 9783031396199 ; , s. v-vi
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 27. |
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| 28. |
- Merk, Olaf
(författare)
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Data-driven Transport Infrastructure Maintenance
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- What we didThis report assesses the potential of data-driven approaches to improving transport infrastructure maintenance. It looks at trends in maintenance strategies, explores how the targeted use of data could make them more effective for different types of transport infrastructure, and looks into implications for policy. The report builds on discussions held during workshops with members of the International Transport Forum’s Corporate Partnership Board.What we foundMaintenance constitutes an inevitable, albeit often invisible, part of countries’ transport policies. Increased demand for transport infrastructure accelerates infrastructure’s ageing. The effects of climate change further aggravate this. Unsurprisingly, many governments look for transport infrastructure maintenance policies that provide better value for money than current practices offer.Infrastructure maintenance strategies are gradually shifting towards data-driven approaches. They exploit the power of digital technologies, Big Data analytics and advanced forecasting methodologies. Data-driven approaches have gained momentum in transport infrastructure maintenance as a result of four simultaneous technological innovations.First, the development of digital technologies has resulted in the digitalisation of society, industry and transport, which facilitates data sharing. Second, computing technologies have provided the necessary horsepower for running the digital infrastructure. Third, the Internet of Things and sensor technology have increased the potential for automating reporting from sensors that capture and measure new phenomena and provide data sets that flow through digital infrastructures. Fourth, artificial intelligence (AI) has helped to extract information from vast amounts of data, recognising patterns beyond the capacity of individual observation and exploiting digital infrastructure and computing power.Policy makers are beginning to leverage these developments in various ways. Data-driven maintenance is becoming common in many parts of the transport industry.Railroads collect massive amounts of inspection data from different sources using various methods, such as track inspection cars and drones that gather data to model track degradation. However, the rail sector faces numerous challenges for applying Big Data analysis: a lack of specific data analysis tools, high cost of involving stakeholders and heterogeneous data sources. Also, the algorithms currently used to predict the wear of rail infrastructure only work under lab conditions.For road infrastructure, various automated inspection methods exist. These include vision-based methods, laser scanning, ground penetration radar and a combination of these. All are accurate and effective but usually costly. As a result, the coverage and collection frequency can prove insufficient for detectingchanging road conditions. Several pilot studies have tried to use smartphones to collect data on the state of roads to reduce deployment costs for data-driven maintenance.At airports, the demand for accurate real-time data has spawned systems that automatically acquire and process infrastructure data. Advanced technologies now register when deformities develop on runways. They accurately measure moisture levels, temperature, strain and other factors relevant to wear and degradation. Several airports have built, or plan to build, concrete pavements with embedded strain gauges and other sensors to monitor the stress in the material caused by aircraft.Overall, data-driven approaches to infrastructure maintenance promise to enhance fact-based decision making and capabilities to predict the remaining useful life of assets. They can also improve cost efficiency and environmental sustainability. However, some new challenges need to be addressed, notably for the use of AI. AI predicts future behaviour based on historical data. Yet all predictions can prove incorrect where events do not follow past trends.What we recommendScale up and speed up the deployment of data-driven approaches to transport infrastructure maintenanceTransport infrastructure maintenance could benefit from a broader and accelerated roll-out of data-driven approaches. These could improve the quality of assets, enhance the life cycles and save costs - especially when the relevant technologies are well-known, such as sensor technologies. In some cases, more tests and pilot projects will be useful, notably where leveraging data technologies for more effective maintenance policies poses specific challenges, as is the case of artificial intelligence in the railway sector.Update regulation and guidelines for transport infrastructure maintenance to facilitate the introduction of more data-driven approachesCurrent regulations and guidelines apply to condition-based maintenance strategies. These may set requirements that are ill-adapted to data-driven approaches to maintenance and may hamper their roll-out. Policy makers should ensure that the policies applied to data-driven approaches do not stifle their potential benefits.Ensure data-driven infrastructure maintenance approaches follow good practices in data governanceThe use of data in infrastructure maintenance must be in line with privacy protection laws and regulations. All data should be anonymised and encrypted. Location and trajectory data should be covered by the most robust protection methods, as they create the severest vulnerabilities for citizens. Tools to limit privacy risks include non-disclosure agreements between data users and providers, the involvement of trusted third parties to conduct the data collection and the development of “safe answers” approaches, in which only query results are exchanged instead of raw data. Governments could also broker data-sharing partnerships for the purpose of data-driven maintenance, for instance, between data providers and infrastructure managers. However, it may want to limit such partnerships to data of public interest and require purpose specificity and data minimisation.
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| 29. |
- Rantatalo, Matti, et al.
(författare)
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Evaluation of Measurement Strategy for Track Side Monitoring of Railway Wheels
- 2023
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 13:9
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Tidskriftsartikel (refereegranskat)abstract
- Wheelsets form an indispensable part of the railway rolling stock and need to be periodically inspected to ensure stable, safe, reliable, and sustainable rail operation. Wheel profiles are usually inspected and measured in a workshop environment using handheld equipment or by utilizing wayside measuring equipment. A common practice for both methods is to measure the wheel profile at one position along the circumference of the wheel, resulting in a one-slice measurement strategy, based on the assumption that the wheel profile has the same shape independent of the measurement position along the wheel. In this article, the representability of a one-slice measurement strategy with respect to the wheel profile parameters is investigated using handheld measurement equipment. The calculated range of standard deviation of the parameters estimated such as flange height, flange width, flange slope, and hollow wear from the measurements shows a spread in the parameter value along the circumference of the wheel. As an initial validation of the results, measurements from the wayside monitoring systems were also investigated to see if a similar spread was visible. The spread was significantly higher for flange height, flange width, and flange slope estimated from wayside measurement equipment than for the same parameters estimated using the handheld measurement equipment.
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| 30. |
- Shao, Haidong, et al.
(författare)
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A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance
- 2021
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Ingår i: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 74, s. 65-76
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Tidskriftsartikel (refereegranskat)abstract
- Collaborative fault diagnosis can be facilitated by multisensory fusion technologies, as these can give more reliable results with a more complete data set. Although deep learning approaches have been developed to overcome the problem of relying on subjective experience in conventional fault diagnosis, there are two remaining obstacles to collaborative efficiency: integration of multisensory data and fusion of maintenance strategies. To overcome these obstacles, we propose a novel two-part approach: a stacked wavelet auto-encoder structure with a Morlet wavelet function for multisensory data fusion and a flexible weighted assignment of fusion strategies. Taking a planetary gearbox as an example, we use noisy vibration signals from multisensors to test the diagnosis performance of the proposed approach. The results demonstrate that it can provide more accurate and reliable fault diagnosis results than other approaches.
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| 31. |
- Thaduri, Adithya, et al.
(författare)
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Impact of climate change on railway operation and maintenance in Sweden : A State-of-the-art review
- 2021
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Ingår i: Maintenance, Reliability and Condition Monitoring (MRCM). - : JVE International. - 2669-2961. ; 1:2, s. 52-70
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Tidskriftsartikel (refereegranskat)abstract
- Increased intensity and frequency of extreme weather conditions caused by climate change can have a negative impact on rail service performance and also increases total ownership costs. Research has shown that adverse weather conditions are responsible for 5 to 10 % of total failures and 60 % of delays on the railway infrastructure in Sweden. The impact of short-term and long-term effects of climate change and extreme weather events depends on the design characteristics of the railway assets, geographical location, operational profile, maturity of the climate adaptation, etc. These extreme events will have major consequences such as traffic disruption, accidents, and higher maintenance costs during the operation and maintenance (O&M) phase. Therefore, a detailed assessment of the effects of climate change on the O&M phase requires a more comprehensive review of the previous studies reported from different parts of the world. The paper provides a state-of-the-art review of the effects of extreme weather events and their impacts on the operation and maintenance of railway infrastructure. This paper also provides a list of vulnerable railway assets that can have an impact due to extreme weather events.
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| 32. |
- Thaduri, Adithya, et al.
(författare)
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Integrated RAMS, LCC and Risk Assessment for Maintenance Planning for Railways
- 2020
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Ingår i: Advances in RAMS Engineering. - Cham : Springer. ; , s. 261-292
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- As of today, about 70% of the transportation infrastructure has already built for the needs of customers, business and society, where Railways is the major infrastructure. Due to huge investment for renewal and overhaul, there is emergent need to maintain the infrastructure with high availability with minimum cost and risk, being, transportation is the backbone of the economy. These infrastructures normally lead to degradation due to operational loads, environmental factors and frequent interventions. Hence, planning and optimization of the maintenance actions with the constrained resources is implemented properly for the efficient operation. Due to the hierarchical nature of Railways, there is necessary for railway infrastructure managers to design a generic framework for the decision-making process when planning maintenance and interventions, which is an important functional block of asset management in railway infrastructures. This chapter proposes an integrated methodology to perform maintenance decision making using definitive “building blocks” namely Reliability, Availability, Maintainability and Safety (RAMS), Life Cycle Costing (LCC) and Risk assessment. It has to incorporates the “building blocks” at different planning levels in asset hierarchy; namely network, route, line and component and planning hierarchy; namely Strategic Asset Management Plan (SAMP), Route Asset Plan (RAP), Route Delivery Plan (RDP) and Implementation of Asset Maintenance Plan (IAMP) as proposed in IN2SMART which was renamed from ISO 55000 Asset Management Framework.
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| 33. |
- Thaduri, Adithya, et al.
(författare)
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Space weather climate impacts on railway infrastructure
- 2020
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Ingår i: International Journal of Systems Assurance Engineering and Management. - : Springer Nature. - 0975-6809 .- 0976-4348. ; 11:2, s. 267-281
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Tidskriftsartikel (refereegranskat)abstract
- Space weather is a phenomenon in which radioactivity and atomic particles is caused by emission from the Sun and stars. It is one of the extreme climate events that could potentially has short-term and long-term impacts on infrastructure. The effects of this phenomenon are a multi-fold process that include electronic system, equipment and component failures, short-term and long-term hazards and consequences to astronauts and aircraft crews, electrostatic charge variation of satellites, disruptions in telecommunications systems, navigational systems, power transmission failures and disturbances to the rail traffic and power grids. The critical infrastructures are becoming interdependent to each other and these infrastructures are vulnerable if one of them is affected due to space weather. Railway infrastructure could be affected by the extreme space weather events and long-term evolution due to direct and indirect effects on system components, such as track circuits, electronic components in-built in signalling systems or indirectly via interdependencies on power, communications, etc. While several space weather-related studies focus on power grids, Global Navigation Satellite System (GNSS) and aviation sectors, a little attention has focused towards probability of railway infrastructure disruptions. Nevertheless, disruptions due to space weather on signalling and train control systems has documented but other systems that railway infrastructure dependent upon are not very well studied. Due to the advancements in digitalization, cloud storage, Internet of Things (IoT), etc., that are embedded with electronic equipment are also possible to prone to these effects and it is even become more susceptible to the extreme space weather events. This paper gives a review of space weather effects on railways and other transportation systems and provide some of the mitigation measures to the infrastructure and societal point of view.
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| 34. |
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