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Publications (10 of 37) Show all publications
Vanhatalo, E., Bergquist, B., Arasteh-Khouy, I. & Larsson, D. (2024). Causal Effects of Railway Track Maintenance—An Experimental Case Study of Tamping. In: International Congress and Workshop on Industrial AI and eMaintenance 2023: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, June 13-15, 2023. Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Causal Effects of Railway Track Maintenance—An Experimental Case Study of Tamping
2024 (English)In: International Congress and Workshop on Industrial AI and eMaintenance 2023, Springer Science and Business Media Deutschland GmbH , 2024Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
National Category
Reliability and Maintenance Infrastructure Engineering
Research subject
Quality Technology and Logistics; Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-103886 (URN)10.1007/978-3-031-39619-9_6 (DOI)2-s2.0-85181981977 (Scopus ID)
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, June 13-15, 2023
Funder
Swedish Transport Administration
Available from: 2024-01-23 Created: 2024-01-23 Last updated: 2024-01-23Bibliographically approved
Sedghi, M., Bergquist, B., Vanhatalo, E. & Migdalas, A. (2022). Data‐driven maintenance planning and scheduling based on predicted railway track condition. Quality and Reliability Engineering International, 38(7), 3689-3709
Open this publication in new window or tab >>Data‐driven maintenance planning and scheduling based on predicted railway track condition
2022 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 38, no 7, p. 3689-3709Article in journal (Refereed) Published
Abstract [en]

Timely planning and scheduling of railway infrastructure maintenance interventions are crucial for increased safety, improved availability, and reduced cost. We propose a data-driven decision-support framework integrating track condition predictions with tactical maintenance planning and operational scheduling. The framework acknowledges prediction uncertainties by using a Wiener process-based prediction model at the tactical level. We also develop planning and scheduling algorithms at the operational level. One algorithm focuses on cost-optimisation, and one algorithm considers the multi-component characteristics of the railway track by grouping track segments near each other for one maintenance activity. The proposed framework's performance is evaluated using track geometry measurement data from a 34 km railway section in northern Sweden, focusing on the tamping maintenance action. We analyse maintenance costs and demonstrate potential efficiency increases by applying the decision-support framework.

Place, publisher, year, edition, pages
John Wiley & Sons, 2022
Keywords
decision-making framework, multi-component system, planning and scheduling, predictive maintenance, railway track, Wiener process
National Category
Computer Engineering Production Engineering, Human Work Science and Ergonomics Other Mechanical Engineering
Research subject
Quality technology and logistics
Identifiers
urn:nbn:se:ltu:diva-92182 (URN)10.1002/qre.3166 (DOI)000826112600001 ()2-s2.0-85134349665 (Scopus ID)
Funder
Swedish Transport Administration
Note

Validerad;2022;Nivå 2;2022-11-28 (joosat);

Funder: Swedish Strategic Innovation Programme InfraSweden2030 (2016–04757); Luleå Railway Research Centre (JVTC); Predge AB

Available from: 2022-07-18 Created: 2022-07-18 Last updated: 2023-09-05Bibliographically approved
Larsson Turtola, S., Rönnbäck, A. & Vanhatalo, E. (2022). Integrating mixture experiments and six sigma methodology to improve fibre‐reinforced polymer composites. Quality and Reliability Engineering International, 38(4), 2233-2254
Open this publication in new window or tab >>Integrating mixture experiments and six sigma methodology to improve fibre‐reinforced polymer composites
2022 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 38, no 4, p. 2233-2254Article in journal (Refereed) Published
Abstract [en]

This article illustrates a Six Sigma project aimed at reducing manufacturing-induced visual deviations for fibre-reinforced polymer (FRP) composites. For a European composites manufacturer, such visual deviations lead to scrapping of cylindrical composite bodies and subsequent environmental impact. The composite bodies are manufactured through vacuum infusion, where a resin mixture impregnates a fibreglass preform and cures, transforming from liquid to solid state. We illustrate the define-measure-analyse-improve-control (DMAIC) steps of the Six Sigma project. Specific emphasis is placed on the measure and analyse steps featuring a 36-run computer-generated mixture experiment with six resin mixture components and six responses. Experimental analysis establishes causal relationships between mixture components and correlated resin characteristics, which can be used to control resin characteristics. Two new resin mixtures were developed and tested in the improve step using the understanding developed in previous steps. Manufacturing-induced visual deviations were greatly reduced by adjusting the resin mixture to induce a slower curing process. Further refinement of the mixture was made in the control step. A production scrap rate of 5% due to visual deviations was measured during a monitoring period of 5 months after the resin mixture change. The scrap rate was substantially improved compared to the historical level (60%). The successful experimental investigation integrated in this Six Sigma project is expected to generate increased quality, competitiveness, and substantial savings.

Place, publisher, year, edition, pages
John Wiley & Sons, 2022
Keywords
curing, define-measure-improve-control (DMAIC), experimental design and analysis (DOE), mixture design, resin mixture
National Category
Composite Science and Engineering
Research subject
Quality technology and logistics
Identifiers
urn:nbn:se:ltu:diva-88910 (URN)10.1002/qre.3067 (DOI)000744792200001 ()2-s2.0-85122811960 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-05-09 (sofila)

Available from: 2022-01-25 Created: 2022-01-25 Last updated: 2022-05-31Bibliographically approved
Sedghi, M., Kauppila, O., Bergquist, B., Vanhatalo, E. & Kulahci, M. (2021). A Taxonomy of Railway Track Maintenance Planning and Scheduling: A Review and Research Trends. Reliability Engineering & System Safety, 215, Article ID 107827.
Open this publication in new window or tab >>A Taxonomy of Railway Track Maintenance Planning and Scheduling: A Review and Research Trends
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2021 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 215, article id 107827Article, review/survey (Refereed) Published
Abstract [en]

Railway track maintenance and renewal are vital for railway safety, train punctuality, and travel comfort. Therefore, having cost-effective maintenance is critical in managing railway infrastructure assets. There has been a considerable amount of research performed on mathematical and decision support models for improving the application of railway track maintenance planning and scheduling. This article reviews the literature in decision support models for railway track maintenance planning and scheduling and transforms the results into a problem taxonomy. Furthermore, the article discusses current approaches in optimising maintenance planning and scheduling, research trends, and possible gaps in the related decision-making models.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Maintenance management, planning, scheduling, decision support models, railway track
National Category
Reliability and Maintenance
Research subject
Quality technology and logistics
Identifiers
urn:nbn:se:ltu:diva-85191 (URN)10.1016/j.ress.2021.107827 (DOI)000690283800033 ()2-s2.0-85107785564 (Scopus ID)
Funder
Swedish Transport AdministrationLuleå Railway Research Centre (JVTC)
Note

Validerad;2021;Nivå 2;2021-06-22 (beamah)

Available from: 2021-06-10 Created: 2021-06-10 Last updated: 2023-09-05Bibliographically approved
Bergquist, B. & Vanhatalo, E. (2020). In-situ measurement in the iron ore pellet distribution chain using active RFID technology. Powder Technology, 361, 791-802
Open this publication in new window or tab >>In-situ measurement in the iron ore pellet distribution chain using active RFID technology
2020 (English)In: Powder Technology, ISSN 0032-5910, E-ISSN 1873-328X, Vol. 361, p. 791-802Article in journal (Refereed) Published
Abstract [en]

The active radio frequency identification (RFID) technique is used for in-situ measurement of acceleration and temperature in the distribution chain of iron ore pellets. The results of this paper are based on two experiments, in which active RFID transponders were released into train wagons or product bins. RFID exciters and readers were installed downstream in a harbour storage silo to retrieve data from the active transponders. Acceleration peaks and temperatures were recorded. The results imply that in-situ data can aid the understanding of induced stresses along the distribution chain to, for example, reduce pellet breakage and dusting. In-situ data can also increase understanding of product mixing behaviour and product residence times in silos. Better knowledge of stresses, product mixing and residence times are beneficial to process and product quality improvement, to better understand the transportation process, and to reduce environmental impacts due to dusting.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Mining industry, RFID tags, Temperature sensors, Accelerometers, Flow production systems, Supply chain management
National Category
Reliability and Maintenance
Research subject
Quality technology and logistics
Identifiers
urn:nbn:se:ltu:diva-76886 (URN)10.1016/j.powtec.2019.11.042 (DOI)000518704900080 ()2-s2.0-85076579100 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-02-27 (alebob)

Available from: 2019-11-27 Created: 2019-11-27 Last updated: 2022-02-24Bibliographically approved
Capaci, F., Vanhatalo, E., Palazoglu, A., Bergquist, B. & Kulahci, M. (2020). On Monitoring Industrial Processes under Feedback Control. Quality and Reliability Engineering International, 36(8), 2720-2737
Open this publication in new window or tab >>On Monitoring Industrial Processes under Feedback Control
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2020 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 36, no 8, p. 2720-2737Article in journal (Refereed) Published
Abstract [en]

The concurrent use of statistical process control and engineering process con-trol involves monitoring manipulated and controlled variables. One multivari-ate control chart may handle the statistical monitoring of all variables, butobserving the manipulated and controlled variables in separate control chartsmay improve understanding of how disturbances and the controller perfor-mance affect the process. In this article, we illustrate how step and ramp dis-turbances manifest themselves in a single-input–single-output system bystudying their resulting signatures in the controlled and manipulated variables.The system is controlled by variations of the widely used proportional-integral-derivative(PID) control scheme. Implications for applying control charts forthese scenarios are discussed.

Place, publisher, year, edition, pages
John Wiley & Sons, 2020
Keywords
control charts, disturbance signatures, engineering process control (EPC), proportional-integral-derivative (PID), statistical process control (SPC)
National Category
Reliability and Maintenance
Research subject
Quality technology and logistics
Identifiers
urn:nbn:se:ltu:diva-74657 (URN)10.1002/qre.2676 (DOI)000544343400001 ()2-s2.0-85087166819 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-11-09 (johcin)

Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2020-11-09Bibliographically approved
Lundkvist, P., Bergquist, B. & Vanhatalo, E. (2020). Statistical methods – still ignored? The testimony of Swedish alumni. Total Quality Management and Business Excellence, 31(3-4), 245-262
Open this publication in new window or tab >>Statistical methods – still ignored? The testimony of Swedish alumni
2020 (English)In: Total Quality Management and Business Excellence, ISSN 1478-3363, E-ISSN 1478-3371, Vol. 31, no 3-4, p. 245-262Article in journal (Refereed) Published
Abstract [en]

Researchers have promoted statistical improvement methods as essential for product and process improvement for decades. However, studies show that their use has been moderate at best. This study aims to assess the use of statistical process control (SPC), process capability analysis, and design of experiments (DoE) over time. The study also highlights important barriers for the wider use of these methods in Sweden as a follow-up study of a similar Swedish study performed in 2005 and of two Basque-based studies performed in 2009 and 2010. While the survey includes open-ended questions, the results are mainly descriptive and confirm results of previous studies. This study shows that the use of the methods has become more frequent compared to the 2005 study. Larger organisations (>250 employees) use the methods more frequently than smaller organisations, and the methods are more widely utilised in the industry than in the service sector. SPC is the most commonly used of the three methods while DoE is least used. Finally, the greatest barriers to increasing the use of statistical methods were: insufficient resources regarding time and money, low commitment of middle and senior managers, inadequate statistical knowledge, and lack of methods to guide the user through experimentations.

Place, publisher, year, edition, pages
Taylor & Francis, 2020
Keywords
statistical process control, capability analysis, design of experiments, implementation barriers, statistical thinking, longitudinal study, Swedish organizations
National Category
Reliability and Maintenance
Research subject
Quality technology and logistics
Identifiers
urn:nbn:se:ltu:diva-67189 (URN)10.1080/14783363.2018.1426449 (DOI)000505886200002 ()2-s2.0-85041139894 (Scopus ID)
Funder
Swedish Research Council, 340-2013-5108
Note

Validerad;2020;Nivå 2;2020-01-27 (johcin)

Available from: 2018-01-08 Created: 2018-01-08 Last updated: 2023-01-20Bibliographically approved
Bergquist, B., Söderholm, P., Kauppila, O. & Vanhatalo, E. (2019). Cleaning of Railway Track Measurement Data forBetter Maintenance Decisions. In: Miguel Castano Arranz; Ramin Karim (Ed.), 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, May 14-15, 2019 (pp. 9-15). Luleå University of Technology
Open this publication in new window or tab >>Cleaning of Railway Track Measurement Data forBetter Maintenance Decisions
2019 (English)In: Proceedings of the 5th International Workshop and Congress on eMaintenance: eMaintenance: Trends in Technologies & methodologies, challenges, possibilites and applications / [ed] Miguel Castano Arranz; Ramin Karim, Luleå University of Technology, 2019, p. 9-15Conference paper, Published paper (Refereed)
Abstract [en]

Data of sufficient quality, quantity and validity constitute a sometimes overlooked basis for eMaintenance. Missing data, heterogeneous data types, calibration problems, or non-standard distributions are common issues of operation and maintenance data. Railway track geometry data used for maintenance planning exhibit all the above issues. They also have unique features stemming from their collection by measurement cars running along the railway network. As the track is a linear asset, measured geometry data need to be precisely located to be useful. However, since the sensors on the measurement car are moving along the track, the observations’ geographical sampling positions come with uncertainty. Another issue is that different seasons and othertime restrictions (e.g. related to the timetable) prohibit regular sampling. Hence, prognostics related to remaining useful life (RUL) are challenging since most forecasting methods require a fixed sampling frequency.

This paper discusses methods for data cleaning, data condensation and data extraction from large datasets collected by measurement cars. We discuss missing data replacement, dealing with autocorrelation or cross-correlation, and consequences of not fulfilling methodological pre-conditions such as estimating probabilities of failures using data that do not follow the assumed distributions or data that are dependent. We also discuss outlier detection, dealing with data coming from multiple distributions, of unknown calibrations and other issues seen in railway track geometry data. We also discuss the consequences of not addressing or mishandling quality issues of such data. 

Place, publisher, year, edition, pages
Luleå University of Technology, 2019
Keywords
Track geometry, big data, railway, data quality, diagnostics, prognostics, maintenance, Sweden
National Category
Reliability and Maintenance
Research subject
Quality Technology and Logistics
Identifiers
urn:nbn:se:ltu:diva-75427 (URN)
Conference
5th International Workshop and Congress on eMaintenance, Stockholm, Sweden, May 14-15, 2019
Funder
VinnovaSwedish Transport Administration
Note

ISBN för värdpublikation: 978-91-7790-475-5

Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2024-01-12Bibliographically approved
Sedghi, M., Vanhatalo, E., Migdalas, A., Kulahci, M. & Kauppila, O. (2019). Data-driven railway maintenance scheduling based on track condition prediction. In: : . Paper presented at International Heavy Haul STS Conference (IHHA 2019), Narvik Norway, June 10-14, 2019.
Open this publication in new window or tab >>Data-driven railway maintenance scheduling based on track condition prediction
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2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Reliability and Maintenance
Research subject
Quality technology and logistics
Identifiers
urn:nbn:se:ltu:diva-75424 (URN)
Conference
International Heavy Haul STS Conference (IHHA 2019), Narvik Norway, June 10-14, 2019
Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2023-09-05Bibliographically approved
Capaci, F., Vanhatalo, E., Kulahci, M. & Bergquist, B. (2019). The Revised Tennessee Eastman Process Simulator as Testbed for SPC and DoE Methods. Quality Engineering, 31(2), 212-229
Open this publication in new window or tab >>The Revised Tennessee Eastman Process Simulator as Testbed for SPC and DoE Methods
2019 (English)In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 31, no 2, p. 212-229Article in journal (Refereed) Published
Abstract [en]

Engineering process control and high-dimensional, time-dependent data present great methodological challenges when applying statistical process control (SPC) and design of experiments (DoE) in continuous industrial processes. Process simulators with an ability to mimic these challenges are instrumental in research and education. This article focuses on the revised Tennessee Eastman process simulator providing guidelines for its use as a testbed for SPC and DoE methods. We provide flowcharts that can support new users to get started in the Simulink/Matlab framework, and illustrate how to run stochastic simulations for SPC and DoE applications using the Tennessee Eastman process.

Place, publisher, year, edition, pages
Taylor & Francis, 2019
Keywords
Simulation, Tutorial, Statistical process control, Design of experiments, Engineering process control, Closed-loop
National Category
Reliability and Maintenance
Research subject
Quality technology and logistics
Identifiers
urn:nbn:se:ltu:diva-66255 (URN)10.1080/08982112.2018.1461905 (DOI)000468617000002 ()2-s2.0-85066129240 (Scopus ID)
Projects
Statistical Methods for Improving Continuous Production
Note

Validerad;2019;Nivå 2;2019-06-11 (johcin)

Available from: 2017-10-25 Created: 2017-10-25 Last updated: 2019-06-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1473-3670

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