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Publications (10 of 85) Show all publications
Jägare, V., Karim, R., Söderholm, P., Larsson-Kråik, P.-O. & Juntti, U. (2019). Change management in digitalised operation and maintenance of railway. In: PROCEEDINGS: International Heavy Haul Association Conference June 2019: . Paper presented at International Heavy Haul Association (IHHA) STS 2019 Conference (pp. 904-911).
Open this publication in new window or tab >>Change management in digitalised operation and maintenance of railway
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2019 (English)In: PROCEEDINGS: International Heavy Haul Association Conference June 2019, 2019, p. 904-911Conference paper, Published paper (Refereed)
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:ltu:diva-75966 (URN)9780911382716 (ISBN)9780911382709 (ISBN)
Conference
International Heavy Haul Association (IHHA) STS 2019 Conference
Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-11
Thaduri, A., Al-Jumaili, M., Kour, R. & Karim, R. (2019). Cybersecurity for eMaintenance in Railway Infrastructure: Risks and Consequences. International Journal of Systems Assurance Engineering and Management, 10(2), 149-159
Open this publication in new window or tab >>Cybersecurity for eMaintenance in Railway Infrastructure: Risks and Consequences
2019 (English)In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 10, no 2, p. 149-159Article in journal (Refereed) Published
Abstract [en]

Recently, due to the advancements in the ICT (Information and Communication Technology), there has been lot of emphasis on digitization of the existing and newly developed infrastructure. In transportation infrastructure, in general, 80% of the assets are already in place and there has been tremendous push to move to the digital era. For efficient and effective design, construction, operation and maintenance of the infrastructure, due to this digitization, there is increasing research trend in data-driven decision-making algorithms that are proved to be effective because of several advantages. Since railway is the backbone of the society, the data-driven approaches will ensure the continuous operation, efficient maintenance, planning and potential future investments. The breach and leak of this potential data to the wrong hands might result in havoc, risk, trust, hazards and serious consequences. Hence, the main purpose of this paper is to stress the potential challenges, consequences, threats, vulnerabilities and risk management of data security in the railway infrastructure in context of eMaintenance. In addition, this paper also identifies the research methods to obtain and secure this data for potential possible research.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
eMaintenance, Cybersecurity, Risks, consequences, Railways
National Category
Reliability and Maintenance Computer Systems Other Civil Engineering
Research subject
Centre - Luleå Railway Research Center (JVTC); Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-73186 (URN)10.1007/s13198-019-00778-w (DOI)000464861200001 ()
Note

Validerad;2019;Nivå 2;2019-04-23 (marisr)

Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-05-02Bibliographically approved
Kour, R., Tretten, P., Karim, R. & Singh, S. (2019). Cybersecurity Workforce in Railway: A Case Study. In: Proceedings of the 5th International Workshop & Congress on eMaintenance 2019: . Paper presented at Proceedings of the 5th International Workshop & Congress on eMaintenance 2019.
Open this publication in new window or tab >>Cybersecurity Workforce in Railway: A Case Study
2019 (English)In: Proceedings of the 5th International Workshop & Congress on eMaintenance 2019, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Railway will continue to adapt new digital solutions which are necessary and vulnerable to cyber threats. The history of cyber-attacks on critical infrastructures including railway suggests that there is a need for cybersecurity awareness. Both for employees and the general public. The very first step in cyber hygiene is cybersecurity training and awareness for the workforce. A well-educated workforce plays a vital role in building more cyber resiliency across the organization's operation and maintenance. The objective of this research is to evaluate the cybersecurity maturity level for workforce management in three railway organizations. The results show that there is a cybersecurity workforce gap and there is a need to eliminate this gap by enhancing cybersecurity workforce culture. Henceforth, this gap can be improved by developing cybersecurity culture, including cybersecurity training and awareness and by following recommendations provided in this paper.

National Category
Engineering and Technology
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75936 (URN)
Conference
Proceedings of the 5th International Workshop & Congress on eMaintenance 2019
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-10
Kour, R., Al-Jumaili, M., Karim, R. & Tretten, P. (2019). eMaintenance in railways: Issues and challenges in cybersecurity. Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, 233(10), 1012-1022
Open this publication in new window or tab >>eMaintenance in railways: Issues and challenges in cybersecurity
2019 (English)In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017, Vol. 233, no 10, p. 1012-1022Article in journal (Refereed) Published
Abstract [en]

The convergence of information technology and operation technology and the associated paradigm shift toward Industry 4.0 in complex systems, such as railways has brought significant benefits in reliability, maintainability, operational efficiency, capacity, as well as improvements in passenger experience. However, with the adoption of information and communications technologies in railway maintenance, vulnerability to cyber threats has increased. It is essential that organizations move toward security analytics and automation to improve and prevent security breaches and to quickly identify and respond to security events. This paper provides a statistical review of cybersecurity incidents in the transportation sector with a focus on railways. It uses a web-based search for data collection in popular databases. The overall objective is to identify cybersecurity challenges in the railway sector.

Place, publisher, year, edition, pages
Sage Publications, 2019
Keywords
Cybersecurity, railway, eMaintenance, challenges
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-72536 (URN)10.1177/0954409718822915 (DOI)
Note

Validerad;2019;Nivå 2;2019-09-11 (johcin)

Available from: 2019-01-14 Created: 2019-01-14 Last updated: 2019-09-11Bibliographically approved
Saari, E., Lin, J., Liu, B., Zhang, L. & Karim, R. (2019). Novel Bayesian Approach to Assess System Availability using a Threshold to Censor Data. International Journal of Performability Engineering, 15(5), 1314-1325
Open this publication in new window or tab >>Novel Bayesian Approach to Assess System Availability using a Threshold to Censor Data
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2019 (English)In: International Journal of Performability Engineering, ISSN 0973-1318, Vol. 15, no 5, p. 1314-1325Article in journal (Refereed) Published
Abstract [en]

Assessment of system availability has been studied from the design stage to the operational stage in various system configurations using either analytic or simulation techniques. However, the former cannot handle complicated state changes, and the latter is computationally expensive. This study proposes a Bayesian approach to evaluate system availability. In this approach: 1) Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) are treated as distributions instead of being "averaged" to better describe real scenarios and overcome the limitations of data sample size; 2) Markov Chain Monte Carlo (MCMC) simulations are applied to take advantage of the analytical and simulation methods; and 3) a threshold is set up for Time to Failure (TTR) data and Time to Repair (TTR) data, and new datasets with right-censored data are created to reveal the connections between technical and "Soft" KPIs. To demonstrate the approach, the paper considers a case study of a balling drum system in a mining company. In this system, MTTF and MTTR are determined by a Bayesian Weibull model and a Bayesian lognormal model, respectively. The results show that the proposed approach can integrate the analytical and simulation methods to assess system availability and could be applied to other technical problems in asset management (e.g., other industries, other systems). By comparing the results with and without considering the threshold for censoring data, we show the threshold can be used as a monitoring line for continuous improvement in the investigated mining company.

Place, publisher, year, edition, pages
Totem Publisher, Inc., 2019
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75095 (URN)10.23940/ijpe.19.05.p7.13141325 (DOI)2-s2.0-85067024398 (Scopus ID)
Note

Validerad;2019;Nivå 1;2019-06-27 (johcin)

Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-06-27Bibliographically approved
Arranz, M. C. & Karim, R. (Eds.). (2019). Proceedings of the 5th International Workshop and Congress on eMaintenance: eMaintenance: Trends in Technologies & methodologies, challenges, possibilites and applications. Paper presented at 5th International Workshop and Congress on eMaintenance, Stockholm, Sweden, 14-15 May 2019. Luleå: Luleå University of Technology
Open this publication in new window or tab >>Proceedings of the 5th International Workshop and Congress on eMaintenance: eMaintenance: Trends in Technologies & methodologies, challenges, possibilites and applications
2019 (English)Conference proceedings (editor) (Refereed)
Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2019
Keywords
eMaintenance, AI, Industrial AI, artificial intelligence
National Category
Reliability and Maintenance
Identifiers
urn:nbn:se:ltu:diva-76013 (URN)
Conference
5th International Workshop and Congress on eMaintenance, Stockholm, Sweden, 14-15 May 2019
Note

We need ISBN for pdf

Available from: 2019-09-16 Created: 2019-09-16 Last updated: 2019-09-16
Kour, R., Thaduri, A. & Karim, R. (2019). Railway Defender Kill Chain for Cybersecurity. In: Proceedings of the 5th International Workshop & Congress on eMaintenance 2019: . Paper presented at Proceedings of the 5th International Workshop & Congress on eMaintenance 2019.
Open this publication in new window or tab >>Railway Defender Kill Chain for Cybersecurity
2019 (English)In: Proceedings of the 5th International Workshop & Congress on eMaintenance 2019, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The railway is one of the most important infrastructures and its security is as important as other critical infrastructures. Due to the increase in cyber-attacks, there is an increasing trend in the field of cybersecurity. The history of cyber incidents suggested that the railway needs immediate security measures or defensive controls for forthcoming advanced persistent threats (APT). Cyber Kill Chain (CKC) is one of the most widely used models for the identification, detection, and prevention of advanced persistent threats. CKC model was introduced by Lockheed Martin that consists of seven stages as Reconnaissance, Weaponize, Delivery, Exploitation, Installation, Command & Control, and Act on Objective. Breaking the chain as early as possible in the CKC model will help the defender to stop adversary’s malicious actions. As the railway is adapting digital technologies and, therefore, there is a risk that adversary can penetrate into the system following the steps of CKC. The objective of this research is to reduce the risk of cyber-attacks by proposing Railway Defender Kill Chain (RDKC) that provides security controls at each phase of Cyber Kill Chain to predict, prevent, detect and respond to cyber threats.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:ltu:diva-75935 (URN)
Conference
Proceedings of the 5th International Workshop & Congress on eMaintenance 2019
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-10
Zhang, L., Lin, J. & Karim, R. (2018). Adaptive Kernel Density-based Anomaly Detection for Nonlinear Systems. Knowledge-Based Systems, 139(1), 50-63
Open this publication in new window or tab >>Adaptive Kernel Density-based Anomaly Detection for Nonlinear Systems
2018 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 139, no 1, p. 50-63Article in journal (Refereed) Published
Abstract [en]

This paper presents an unsupervised, density-based approach to anomaly detection. The purpose is to define a smooth yet effective measure of outlierness that can be used to detect anomalies in nonlinear systems. The approach assigns each sample a local outlier score indicating how much one sample deviates from others in its locality. Specifically, the local outlier score is defined as a relative measure of local density between a sample and a set of its neighboring samples. To achieve smoothness in the measure, we adopt the Gaussian kernel function. Further, to enhance its discriminating power, we use adaptive kernel width: in high-density regions, we apply wide kernel widths to smooth out the discrepancy between normal samples; in low-density regions, we use narrow kernel widths to intensify the abnormality of potentially anomalous samples. The approach is extended to an online mode with the purpose of detecting anomalies in stationary data streams. To validate the proposed approach, we compare it with several alternatives using synthetic datasets; the approach is found superior in terms of smoothness, effectiveness and robustness. A further experiment on a real-world dataset demonstrated the applicability of the proposed approach in fault detection tasks.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
maintenance modelling, fault detection, unsupervised learning, nonlinear data, kernel density
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-60425 (URN)10.1016/j.knosys.2017.10.009 (DOI)000417773400005 ()2-s2.0-85031504017 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-11-21 (andbra)

Available from: 2016-11-15 Created: 2016-11-15 Last updated: 2017-12-28Bibliographically approved
Al-Jumaili, M., Karim, R. & Tretten, P. (2018). Data quality assessment using multi-attribute: maintenance perspective. International Journal of Information and Decision Sciences, 10(2), 147-161
Open this publication in new window or tab >>Data quality assessment using multi-attribute: maintenance perspective
2018 (English)In: International Journal of Information and Decision Sciences, ISSN 1756-7017, E-ISSN 1756-7025, Vol. 10, no 2, p. 147-161Article in journal (Refereed) Published
Abstract [en]

The paper proposes a model for data quality (DQ) assessment in maintenance. Data has become an increasingly important since most of the maintenance planning and implementations are based on data analysis. Poor DQ reduces customer satisfaction, leading to poor decision making, and has negative impacts on strategy execution. To improve DQ as well as to evaluate the current status, DQ needs to be measured. A measure for DQ could be an important support for decision makers. Multi-criteria decision-making (MCDM) methods can provide a framework for DQ assessment, however, they are not used in literature for DQ assessment. In order to assess DQ, the attributes or KPIs need to be defined, their hierarchy should be designed and the assessment model is proposed to evaluate these attributes. A case study is also presented in this paper. The study shows that MCDM methods could provide qualitative estimation for the quality of DQ attributes.

Place, publisher, year, edition, pages
InderScience Publishers, 2018
Keywords
Data quality, data science, eMaintenance
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-69215 (URN)10.1504/IJIDS.2018.092423 (DOI)2-s2.0-85048767333 (Scopus ID)
Note

Validerad;2018;Nivå 1;2018-06-21 (svasva)

Available from: 2018-06-08 Created: 2018-06-08 Last updated: 2018-06-29Bibliographically approved
Karim, R. & Jägare, V. (2017). ePilot: Slutrapport : ett samverkansprojekt inom järnväg. Luleå: Luleå University of Technology
Open this publication in new window or tab >>ePilot: Slutrapport : ett samverkansprojekt inom järnväg
2017 (Swedish)Report (Other (popular science, discussion, etc.))
Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2017. p. 58
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-61660 (URN)978-91-7583-798-7 (ISBN)978-91-7583-799-4 (ISBN)
Available from: 2017-01-26 Created: 2017-01-26 Last updated: 2017-11-24Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0055-2740

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