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Famurewa, Stephen MayowaORCID iD iconorcid.org/0000-0001-9843-5819
Publications (10 of 44) Show all publications
Thaduri, A., Famurewa, S. M., Verma, A. K. & Kumar, U. (2019). Process Mining for Maintenance Decision Support. In: P. K. Kapur, Yury Klochkov, Ajit Kumar Verma, Gurinder Singh (Ed.), System Performance and Management Analytics: (pp. 279-293). Springer
Open this publication in new window or tab >>Process Mining for Maintenance Decision Support
2019 (English)In: System Performance and Management Analytics / [ed] P. K. Kapur, Yury Klochkov, Ajit Kumar Verma, Gurinder Singh, Springer, 2019, p. 279-293Chapter in book (Refereed)
Abstract [en]

In carrying out maintenance actions, there are several processes running simultaneously among different assets, stakeholders, and resources. Due to the complexity of maintenance process in general, there will be several bottlenecks for carrying out actions that lead to reduction in maintenance efficiency, increase in unnecessary costs and a hindrance to operations. One of the tools that is emerging to solve the above issues is the use Process Mining tools and models. Process mining is attaining significance for solving specific problems related to process such as classification, clustering, discovery of process, prediction of bottlenecks, developing of process workflow, etc. The main objective of this paper is to utilize the concept of process mining to map and comprehend a set of maintenance reports mainly repair or replacement from some lines on the Swedish railway network. To attain the above objective, the reports were processed to extract out time related maintenance parameters such as  administrative, logistic and repair times. Bottlenecks are identified in the maintenance process and this information will be useful for maintenance service providers, infrastructure managers, asset owners and other stakeholders for improvement and maintenance effectiveness.

Place, publisher, year, edition, pages
Springer, 2019
Series
Asset Analytics, ISSN 2522-5162
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-70280 (URN)10.1007/978-981-10-7323-6_23 (DOI)978-981-10-7322-9 (ISBN)978-981-10-7323-6 (ISBN)
Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2018-08-09Bibliographically approved
Calle Cordón, Á., Jiménez-Redondo, N., Morales-Gámiz, J., García-Villena, F. A., Peralta-Escalante, J., Garmabaki, A., . . . Morgado, J. (2018). Combined RAMS and LCC analysis in railway and road transport infrastructures. In: Proceedings of 7th Transport Research Arena TRA: . Paper presented at 7th Transport Research Arena TRA 2018, Vienna, 16 – 19 April 2018. Vienna, Austria
Open this publication in new window or tab >>Combined RAMS and LCC analysis in railway and road transport infrastructures
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2018 (English)In: Proceedings of 7th Transport Research Arena TRA, Vienna, Austria, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Life-cycle cost (LCC) analysis is an assessment technique used to evaluate costs incurred during the life-cycle of a system to help in long term decision making. In railway and road transport infrastructures, costs are subject to numerous uncertainties associated to the operation and maintenance phase. By integrating in the LCC the stochastic nature of failure using Reliability, Maintainability, Availability and Safety (RAMS) analyses, maintenance costs can be more reliably estimated. This paper presents an innovative approach for a combined RAMS&LCC methodology for linear transport infrastructures which has been developed under the H2020 project INFRALERT. Results of the application of such methodology in two real use cases are shown, one for rail and another one for road. The use cases show how the approach is implemented in practice.

Place, publisher, year, edition, pages
Vienna, Austria: , 2018
Keywords
intelligent maintenance, linear transport infrastructure, RAMS, Life-Cycle Cost, maintenance
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Sustainable transportation (AERI); Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65941 (URN)
Conference
7th Transport Research Arena TRA 2018, Vienna, 16 – 19 April 2018
Funder
EU, Horizon 2020, SEP-210181906
Note

Life-cycle cost (LCC) analysis is an assessment technique used to evaluate costs incurred during the life-cycle of a system to help in long term decision making. In railway and road transport infrastructures, costs are subject to numerous uncertainties associated to the operation and maintenance phase. By integrating in the LCC the stochastic nature of failure using Reliability, Maintainability, Availability and Safety (RAMS) analyses, maintenance costs can be more reliably estimated. This paper presents an innovative approach for a combined RAMS&LCC methodology for linear transport infrastructures which has been developed under the H2020 project INFRALERT. Results of the application of such methodology in two real use cases are shown, one for rail and another one for road. The use cases show how the approach is implemented in practice.

Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2018-06-25Bibliographically approved
Jiménez-Redondo, N., Calle Cordón, Á., Kandler, U., Simroth, A., Reyes, A., Morales, F., . . . Juszt, A. (2018). INFRALERT: improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques. In: Proceedings of 7th Transport Research Arena TRA, Vienna, Austria, 2018: . Paper presented at 7th Transport Research Arena TRA 2018, Vienna, 16 – 19 April 2018. Vienna, Austria
Open this publication in new window or tab >>INFRALERT: improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques
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2018 (English)In: Proceedings of 7th Transport Research Arena TRA, Vienna, Austria, 2018, Vienna, Austria, 2018Conference paper, Published paper (Refereed)
Abstract [en]

The on-going H2020 project INFRALERT aims to increase rail and road infrastructure capacity in the current framework of increased transportation demand by developing and deploying solutions to optimise maintenance interventions planning. INFRALERT develops an ICT platform - the expert-based Infrastructure Management System eIMS - which follows a modular approach including several expert-based toolkits. This paper presents the architecture of the eIMS as well as the functionalities, methodologies and exemplary results of the toolkits for i) nowcasting and forecasting of asset condition, ii) alert generation, iii)  RAMS & LCC analysis and iv) decision support. The applicability and effectiveness of the eIMS and its toolkits will be demonstrated in two real-world pilot scenarios, which are described in the paper: a meshed road network in Portugal under the jurisdiction of Infraestruturas de Portugal (IP) and a freight railway line in Northern Europe managed by Trafikverket

Place, publisher, year, edition, pages
Vienna, Austria: , 2018
Keywords
intelligent maintenance, linear transport infrastructure, condition nowcasting & forecasting, alert management, RAMS & LCC, decision support, maintenance & interventions planning
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Sustainable transportation (AERI); Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65940 (URN)
Conference
7th Transport Research Arena TRA 2018, Vienna, 16 – 19 April 2018
Funder
EU, Horizon 2020, SEP-210181906
Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2018-06-25Bibliographically approved
Asplund, M., Famurewa, S. M. & Schoech, W. (2017). A Nordic heavy haul experience and best practices. Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, 231(7), 794-804
Open this publication in new window or tab >>A Nordic heavy haul experience and best practices
2017 (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. 231, no 7, p. 794-804Article in journal (Refereed) Published
Abstract [en]

This article summarizes the experiences gained at the Nordic heavy haul line “Malmbanan” located in Northern Sweden and Norway during the years 2007 to 2015 and the resulting best practice. Unique long-term information of field trials and monitoring from the on-going development for maintenance of rail and wheel has been described. The reported results come from the rail profile measurements using MiniProf and HC-recordings with Eddy-current devices and visual inspection on 43 test sections. The monitoring has been continuous since the project started, to reveal a deep insight into the complex wheel–rail interaction and provide understanding of the effect of applying optimized specifications. This was particularly important in view of the increasing traffic load that contributed to doubling of the yearly grinding campaigns. This article presents in particular the new MB5 profile, the wear rate behaviour between two different curves, impacts of gauge widening on rail rolling contact fatigue and the speed of gauge widening as well as the seasonal impact on the crack propagation. The presently applied maintenance strategy is discussed together with other experiences. The article finishes with some conclusions and an outlook into further work.

Place, publisher, year, edition, pages
Sage Publications, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-62807 (URN)10.1177/0954409717699468 (DOI)000407122100005 ()2-s2.0-85026881475 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-08-14 (rokbeg)

Available from: 2017-03-30 Created: 2017-03-30 Last updated: 2018-07-10Bibliographically approved
Famurewa, S. M., Zhang, L. & Asplund, M. (2017). Data analytics for condition based wheel maintenance. In: : . Paper presented at 11th International Heavy Haul Association Conference, Cape Town, South Africa. 2–6 September 2017. Cape Town, South Africa
Open this publication in new window or tab >>Data analytics for condition based wheel maintenance
2017 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Cape Town, South Africa: , 2017
Keywords
maintenance wheel health analytics monitoring system classification scheme
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Sustainable transportation (AERI); Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65938 (URN)
Conference
11th International Heavy Haul Association Conference, Cape Town, South Africa. 2–6 September 2017
Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2018-06-25Bibliographically approved
Odelius, J., Famurewa, S. M., Forslöf, L., Casselgren, J. & Konttaniemi, H. (2017). Industrial internet applications for efficient road winter maintenance. Journal of Quality in Maintenance Engineering, 23(3), 355-367
Open this publication in new window or tab >>Industrial internet applications for efficient road winter maintenance
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2017 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 355-367Article in journal (Refereed) Published
Abstract [en]

Purpose: For the expected increase in the capacity of existing transportation systems and efficient energy utilisation, smart maintenance solutions that are supported by online and integrated condition monitoring systems are required. Industrial Internet is one of the smart maintenance solutions, which enables real-time acquisition and analysis of asset condition by linking intelligent devices with different stakeholdersᅵ applications and databases. This paper presents some aspects of Industrial Internet application as required for integrating weather information and floating road condition data from vehicle mounted sensors to enhance effective and efficient winter maintenance.

Design/methodology/approach: The concept of real-time road condition assessment using in-vehicle sensors is demonstrated in a case study of a 3.5 km road section located in northern Sweden. The main floating data sources were acceleration and position sensors from a smartphone positioned on the dash board of a truck. Features extracted from the acceleration signal were two road roughness estimations. To extract targeted information and knowledge, the floating data were further processed to produce time series data of the road condition using Kalman filtering. The time series data were thereafter combined with weather data to assess the condition of the road.

Findings: In the case study, examples of visualisation and analytics to support winter maintenance planning, execution, and resource allocation were presented. Reasonable correlation was shown between estimated road roughness and annual road survey data to validate and prove the presented results wider applicability.

Originality/value: The paper describes a concept of floating data for an industrial internet application for efficient road maintenance. The resulting improvement in winter maintenance will promote dependable, safe and sustainable transportation of goods and people, especially in northern Nordic region with harsh and sometimes unpredictable weather conditions.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2017
National Category
Engineering and Technology Other Civil Engineering Applied Mechanics
Research subject
Operation and Maintenance; Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-65114 (URN)10.1108/JQME-11-2016-0071 (DOI)2-s2.0-85027974857 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-08-28 (andbra)

Available from: 2017-08-15 Created: 2017-08-15 Last updated: 2018-06-25Bibliographically approved
Famurewa, S. M., Zhang, L. & Asplund, M. (2017). Maintenance analytics for railway infrastructure decision support. Journal of Quality in Maintenance Engineering, 23(3), 310-325
Open this publication in new window or tab >>Maintenance analytics for railway infrastructure decision support
2017 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 310-325Article in journal (Refereed) Published
Abstract [en]

Purpose

This purpose of this article is to present a framework for maintenance analytics that is useful for the assessment of rail condition and for maintenance decision support. The framework covers three essential maintenance aspects: diagnostic, prediction and prescription. The article also presents principal component analysis (PCA) and local outlier factor (LOF) methods for detecting anomalous rail wear occurrences using field measurement data.

Design/methodology/approach

The approach used in this paper includes a review of the concept of analytics and appropriate adaptation to railway infrastructure maintenance. The diagnotics aspect of the proposed framework is demonstrated with a case study using historical rail profile data collected between 2007 and 2016 for 9 sharp curves on the heavy haul line in Sweden.

Findings

The framework presented for maintenance analytics is suitable for extracting useful information from condition data as required for effective rail maintenance decision support. The findings of the case study include: combination of the two statistics from PCA model (T2 and Q) can help to identify systematic and random variations in rail wear pattern that are beyond normal: the visualisation approach is a better tool for anomaly detection as it categorises wear observations into normal, suspicious and anomalous observations.

Practical implications

A practical implication of this article is that the framework and the diagnostic tool can be considered as an integral part of eMaintenance solution. It can be easily adapted as online or onboard maintenance analytic tool with data from automated vehicle based measurement system.

Originality/value

This research adapts the concept of analytics to railway infrastructure maintenance for enhanced decision making. It proposes a graphical method for combining and visualising different outlier statistics as a reliable anomaly detection tool.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2017
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65099 (URN)10.1108/JQME-11-2016-0059 (DOI)2-s2.0-85027972757 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-08-28 (andbra)

Available from: 2017-08-15 Created: 2017-08-15 Last updated: 2018-06-25Bibliographically approved
Thaduri, A. & Famurewa, S. M. (2017). Processing mining for maintenance decision support. In: Diego Galar, Dammika Seneviratne (Ed.), Proceedings of MPMM 2016: 6th International Conference on Maintenance Performance Measurement and Management, 28 November 2016, Luleå, Sweden. Paper presented at Maintenance Performance and Measurement and Management 2016(MPMM 2016). November 28, Luleå, Sweden (pp. 179-). Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>Processing mining for maintenance decision support
2017 (English)In: Proceedings of MPMM 2016: 6th International Conference on Maintenance Performance Measurement and Management, 28 November 2016, Luleå, Sweden / [ed] Diego Galar, Dammika Seneviratne, Luleå: Luleå tekniska universitet, 2017, p. 179-Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Process mining is gaining importance for the classification, clustering, workflow models, process discovery, predictions and planning and scheduling in a process or events in especially business oriented fields. On the other hand, there are several events that are required to perform a maintenance action in various industries. There is a need to understand the process flow of events to reduce the delays to increase the performance of the maintenance action. This paper applies the concept of process mining to understand the events in a typical maintenance action (repair or replacement,). We implemented the process mining for administrative, logistic and repair delays for one section in Swedish Railway. We identified the bottlenecks in this process fordifferent subsystems for productive feedback to the railway industry.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-63905 (URN)978-91-7583-841-0 (ISBN)
Conference
Maintenance Performance and Measurement and Management 2016(MPMM 2016). November 28, Luleå, Sweden
Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2018-06-25Bibliographically approved
Sundgren, A., Junnti, U. A., Famurewa, S. M. & Asplund, M. (2017). ReRail: Rail capping system for improved performance and LCC. In: : . Paper presented at 11th International Heavy Haul Association Conference, Cape Town, South Africa. 2–6 September 2017. Cape Town, South Africa
Open this publication in new window or tab >>ReRail: Rail capping system for improved performance and LCC
2017 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Cape Town, South Africa: , 2017
Keywords
LCC, rerail, rail performance wear fatigue resistance
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65939 (URN)
Conference
11th International Heavy Haul Association Conference, Cape Town, South Africa. 2–6 September 2017
Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2018-06-25Bibliographically approved
Asplund, M., Palo, M., Famurewa, S. M. & Rantatalo, M. (2016). A study of railway wheel profile parameters used as indicators of an increased risk of wheel defects (ed.). Paper presented at . Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, 230(2), 323-334
Open this publication in new window or tab >>A study of railway wheel profile parameters used as indicators of an increased risk of wheel defects
2016 (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. 230, no 2, p. 323-334Article in journal (Refereed) Published
Abstract [en]

The capacity demands on the railways will increase in the future, as well as the demands for a robust and available system. The availability of the railway system is dependent on the condition of the infrastructure and the rolling stock. To inspect the rolling stock and to prevent damage to the track due to faulty wheels, infrastructure managers normally install wayside monitoring systems along the track. Such systems indicate, for example, wheels that fall outside the defined safety limits and have to be removed from service to prevent further damage to the track. Due to the nature of many wayside monitoring systems, which only monitor vehicles at definite points along the track, damage may be induced on the track prior to fault detection at the location of the system. Such damage can entail capacity-consuming speed reductions and manual track inspections before the track can be opened for traffic again. The number of wheel defects must therefore be kept to a minimum. In this paper wheel profile parameters measured by a wayside wheel profile measurement system, installed along the Swedish Iron Ore Line, are examined and related to warning and alarm indications from a wheel defect detector installed on the same line. The study shows that an increased wheel wear, detectable by changes in the wheel profile parameters could be used to reduce the risk of capacity-consuming wheel defect failure events and its reactive measures.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-11827 (URN)10.1177/0954409714541953 (DOI)000368600500001 ()2-s2.0-84954348350 (Scopus ID)ad81ec6f-eb97-4226-a5fd-9b81eb39b186 (Local ID)ad81ec6f-eb97-4226-a5fd-9b81eb39b186 (Archive number)ad81ec6f-eb97-4226-a5fd-9b81eb39b186 (OAI)
Note
Validerad; 2016; Nivå 2; 20131210 (matasp)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9843-5819

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