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Rantatalo, Matti
Publications (10 of 65) Show all publications
Garmabaki, A. S., Thaduri, A., Hedström, A., Kumar, U., Laue, J., Marklund, S., . . . Indahl, S. (2019). A Survey on Underground Pipelines and Railway Infrastructure at Cross-Sections. In: Michael beer, Enrico Zio (Ed.), ESREL-2019: . Paper presented at ESREL 2019 | European Safety and Reliability Conference.
Open this publication in new window or tab >>A Survey on Underground Pipelines and Railway Infrastructure at Cross-Sections
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2019 (English)In: ESREL-2019 / [ed] Michael beer, Enrico Zio, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Underground pipelines are an essential part of the transportation infrastructure. The structural deterioration of pipelines crossing railways and their subsequent failures are critical for society and industry resulting in direct and indirect costs for all the related stakeholders. Pipeline failures are complex processes, which are affected by many factors, both static (e.g., pipe material, size, age, and soil type) and dynamic (e.g., traffic load, pressure zone changes, and environmental impacts). These failures have serious impacts on public due to safety, disruption of traffic, inconvenience to society, environmental impacts and shortage of resources. Therefore, continuous and accurate condition assessment is critical for the effective management and maintenance of pipeline networks within transportation infrastructure. The aim of this study is to identify failure modes and consequences related to the crossing of pipelines in railway corridors. Expert opinion have been collected through two set of questionnaires which have been distributed to the 291 municipalities in the whole Sweden. The failure analysis revealed that pipe deformation has higher impact followed by pipe rupture at cross-section with railway infrastructure. For underground pipeline under railway infrastructure, aging and external load gets higher ranks among different potential failure causes to the pipeline.

Keywords
Underground Pipelines, Transportation Infrastructure, Railway, Maintenance, FMEA
National Category
Reliability and Maintenance Water Engineering Geotechnical Engineering
Research subject
Structural Engineering; Traffic Engineering
Identifiers
urn:nbn:se:ltu:diva-76471 (URN)10.3850/978-981-11-2724-3_ 0037-cd (DOI)978-981-11-2724-3 (ISBN)
Conference
ESREL 2019 | European Safety and Reliability Conference
Projects
PipeXrail
Funder
Vinnova, 2016-033113
Note

We gratefully acknowledge the funding provided by Sweden’s Innovation Agency, Vinnova, through the Strategic Innovation Programme InfraSweden2030. The funding was granted in competition within the Open Call “Condition assessment and maintenance of transport infrastructure – Grant No. 2016-033113”. In addition, the technical support and collaboration of, Arrsleff Rörteknik, Luleå Railway Research Center (JVTC) and the Swedish Transport Administration (Trafikverket) are greatly appreciated

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-22
Chandran, P., Rantatalo, M., Odelius, J., Lind, H. & Famurewa, S. M. (2019). Train-based differential eddy current sensor system for rail fastener detection. Measurement science and technology, 30(12), Article ID 125105.
Open this publication in new window or tab >>Train-based differential eddy current sensor system for rail fastener detection
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2019 (English)In: Measurement science and technology, ISSN 0957-0233, E-ISSN 1361-6501, Vol. 30, no 12, article id 125105Article in journal (Refereed) Published
Abstract [en]

One of the crucial components in rail tracks is the rail fastening system, which acts as a means of fixing rails to the sleepers to maintain the track gauge and stability. Manual inspection and 2D visual inspection of fastening systems have predominated over the past two decades. However, both methods have drawbacks when visibility is obscured and are found to be relatively expensive in terms of cost and track possession. The present article presents the concept of a train-based differential eddy current (EC) sensor system for fastener detection. The sensor uses the principle of electromagnetic induction, where an alternating-current-carrying coil is used to create an EC on the rail and other electrically conductive material in the vicinity and a pick-up coil is used to measure the returning field. This paper gives an insight into the theoretical background and application of the proposed differential EC sensor system for the condition monitoring system of rail fasteners and shows experimental results from both laboratory and field measurements. The field measurements were carried out along a heavy-haul railway line in the north of Sweden. Results obtained from both the field measurements and from the lab tests reveal that that the proposed method was able to detect an individual fastening system from a height of 65 mm above the rail. Furthermore, missing clamps within a fastening system are detected by analysing a time domain feature of the measurement signal.

Place, publisher, year, edition, pages
Institute of Physics Publishing (IOPP), 2019
National Category
Other Civil Engineering
Research subject
Operation and Maintenance; Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-76340 (URN)10.1088/1361-6501/ab2b24 (DOI)000487122500002 ()
Note

Validerad;2019;Nivå 2;2019-10-10 (johcin)

Available from: 2019-10-10 Created: 2019-10-10 Last updated: 2019-10-10Bibliographically approved
Mishra, M., Martinsson, J., Rantatalo, M. & Goebel, K. (2018). Bayesian hierarchical model-based prognostics for lithium-ion batteries. Reliability Engineering & System Safety, 172, 25-35
Open this publication in new window or tab >>Bayesian hierarchical model-based prognostics for lithium-ion batteries
2018 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 172, p. 25-35Article in journal (Refereed) Published
Abstract [en]

To optimise operation and maintenance, knowledge of the ability to perform the required functions is vital. The ability is governed by the usage of the system (operational issues) and availability aspects like reliability of different components. This paper proposes a Bayesian hierarchical model (BHM)-based prognostics approach applied to Li-ion batteries, where the goal is to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. A BHM approach where the operational and reliability aspects end of life (EoD) and end of life (EoL) is studied where its shown that predictions of EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g. predicting a new battery, the predictions are based only on the prior distributions describing the similarity within the group of batteries and their dependency on the load cycle. A discharge cycle dependency can also be identified in the result giving the opportunity to predict the battery reliability.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Other Civil Engineering Probability Theory and Statistics
Research subject
Operation and Maintenance; Mathematical Statistics
Identifiers
urn:nbn:se:ltu:diva-66884 (URN)10.1016/j.ress.2017.11.020 (DOI)000424960000003 ()2-s2.0-85037540599 (Scopus ID)
Note

Validerad;2018;Nivå 2;2017-12-14 (andbra)

Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-06-13Bibliographically approved
Asplund, M. & Rantatalo, M. (2018). Evaluation of wheel profile measurements by means of the contact-point function for the wheel-rail interface. Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, 232(4), 1225-1239
Open this publication in new window or tab >>Evaluation of wheel profile measurements by means of the contact-point function for the wheel-rail interface
2018 (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. 232, no 4, p. 1225-1239Article in journal (Refereed) Published
Abstract [en]

This study examines the accuracy of a wayside train wheel profile measurement system. This was accomplished by an evaluation of the contact-point function for the wheel–rail interface. The wheel profile measurement system in question generates data about the wheel profiles of passing trains. These data are used for improving the wheel maintenance procedures for the rolling stock operator. Recent work shows that there are differences between the data from the two different units in the system, but how this influences further use of the data, e.g. in wheel–rail contact analysis, has not been investigated so far. Accordingly, this article shows how two key wheel measures (the wheel flange thickness and the wheel profile) impact on the contact-point function and which of these measures has the largest impact on the contact-point function. The data used in this study were generated by two different measurement units for the same wheel and with the same wheel status. The results show that the different units produce different results and that these differences are more prominent when a difference in the flange thickness is detected, with a resulting shift of the front side of the flange and of the tread. With no difference in the flange thickness, i.e. no shift of the front side of the flange and of the tread, a difference was still detected in the contact conditions. Furthermore, this investigation shows that the shape of the tread has a greater impact on the contact-point conditions compared to a change in the flange thickness of up to 2.5 mm. This difference in the tread shape could have originated in measurement noise or different wheel measurement positions. The results of the study also show the importance of managing the measurement quality before using the data, for example for maintenance decisions.

Place, publisher, year, edition, pages
Sage Publications, 2018
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-60150 (URN)10.1177/0954409717714353 (DOI)000429914700021 ()
Funder
Swedish Transport Administration
Note

Validerad;2018;Nivå 2;2018-04-12 (rokbeg)

Available from: 2016-11-04 Created: 2016-11-04 Last updated: 2018-04-26Bibliographically approved
Mishra, M., Martinsson, J., Rantatalo, M. & Goebel, K. (2018). Hierarchical model-based prognostics for Li-ion batteries. Advances In ENGINEERING
Open this publication in new window or tab >>Hierarchical model-based prognostics for Li-ion batteries
2018 (English)In: Advances In ENGINEERINGArticle in journal (Other (popular science, discussion, etc.)) Published
Abstract [en]

Recent global trend towards a fossil-fuel-free society has yielded the rapid soar in demand of electrically powered systems. Specifically, the demand for battery powered systems has fueled the desire to have better performing batteries with lithium-ion batteries being the most widely used. Presently, for application in unmanned vehicles, exploratory rovers, submarines among others demand a better comprehension of battery performance metrics. Case and point, battery capacity and state of charge have become increasingly vital when it comes to determining the end of discharge. As of now, several techniques have already been established for determining such parameters. Unfortunately, their prognostic capability for determining remaining battery charge is still not optimal. Therefore, there is a need to develop prognostic and health management technology for critical systems (such as Mars rovers) to successfully predict and manage the lifetime of batteries, monitor their health state in real time, evaluate the performance and predict the remaining useful life.

To this note, Luleå University of Technology researchers in Sweden: Dr. Madhav Mishra, Dr. Jesper Martinsson, and Dr. Matti Rantatalo in collaboration with Dr. Kai Goebel at NASA in the United States proposed a study whose main objective was to measure the battery discharge and predict the end of discharge considering the operating conditions for lithium ion batteries. To be precise, they purposed on employing a Bayesian Hierarchical Model (BHM)-based end of discharge prognostic for Li-ion batteries. Their work is currently published in the research journal, Reliability Engineering and System Safety.

The research technique employed entailed the utilization of two batteries with 16 discharge events with a simplified battery circuit model of the battery. Next, the research team examined the detailed discharge voltage profiles during different discharging cycles with variable load profiles. They then proceeded to demonstrate the BHM approach and group-level dependencies by utilizing more than one battery and more than one discharge cycle.

The authors observed that the BHM approach enabled inferences for both individual batteries and groups of batteries. The researchers then recorded the estimates of the hierarchical model parameters and the individual battery parameters after which their dependencies on load cycles were inferred. In addition, they noted that by using the BHM approach the predictions of end of discharge could be made accurately with a variable amount of battery data. Furthermore, this technique was seen to applicable even for new batteries without prior recorded data where the predictions were based only on the prior distributions describing the similarity within the group of batteries and their dependency on the load cycle.

In conclusion, the study presented a Bayesian hierarchical model (BHM)-based prognostics approach for Li-ion batteries, where the goal was to analyze and predict the discharge behavior of such batteries with variable load profiles and variable amounts of available discharge data. The results obtained showed that the technique could address cases with or without data. Altogether, the proposed method can capture additional relationships between parameters and use it to improve prognostics. Lastly, the BHM approach has been seen to permit inference at both the individual battery level and group of battery level.

Place, publisher, year, edition, pages
Ontario, CANADA: , 2018
Keywords
Significance, Prognostics, Hierarchical
National Category
Engineering and Technology Other Civil Engineering Probability Theory and Statistics
Research subject
Operation and Maintenance; Mathematical Statistics
Identifiers
urn:nbn:se:ltu:diva-70654 (URN)
Available from: 2018-08-30 Created: 2018-08-30 Last updated: 2019-04-23Bibliographically approved
Saari, J., Lundberg, J., Odelius, J. & Rantatalo, M. (2018). Selection of features for fault diagnosis on rotating machines using random forest and wavelet analysis. Insight (Northampton), 60(8), 434-442
Open this publication in new window or tab >>Selection of features for fault diagnosis on rotating machines using random forest and wavelet analysis
2018 (English)In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 60, no 8, p. 434-442Article in journal (Refereed) Published
Abstract [en]

Identification of component faults using automated condition monitoring methods has a huge potential to improve the prediction of machine failures. The ongoing development of the Internet of Things (IoT) will support and benefit feature selection and improve preventative maintenance decision making. However, there may be problems with the selection of features that best describe a specific fault and remain valid even when the operation mode is changing (for example different levels of load). In this study, features were extracted from vibration signals using wavelet analysis; a feature subset was selected using the random forest ensemble technique. Three different datasets were created where the load of the system was changing while the rotational speed remained the same. The tests were repeated five times by first recording the nominal condition and then introducing four faults: angular misalignment; offset misalignment; partially broken gear tooth failure; and macro-pitting of the gear. To improve previous feature selection techniques, a method is proposed where, before training a classifier, the most promising features are compared at different degrees of torsional load. The results indicate that the proposed method of using random forests to select top variables can help to choose good features that may not have been considered in manual feature selection or in individual load zones.

Place, publisher, year, edition, pages
British Institute of Non-Destructive Testing, 2018
National Category
Other Civil Engineering
Research subject
Operation and Maintenance; Centre - SKF-LTU University Technology Cooperation
Identifiers
urn:nbn:se:ltu:diva-70433 (URN)10.1784/insi.2018.60.8.434. (DOI)000441327800006 ()2-s2.0-85051538361 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-08-16 (andbra)

Available from: 2018-08-16 Created: 2018-08-16 Last updated: 2019-03-26Bibliographically approved
Petersson, A. M., Lundberg, J. & Rantatalo, M. (2017). Ideation methods applied in a cross-functional inter-organizational group: an exploratory case study from the railway sector. Research in Engineering Design, 28(1), 71-97
Open this publication in new window or tab >>Ideation methods applied in a cross-functional inter-organizational group: an exploratory case study from the railway sector
2017 (English)In: Research in Engineering Design, ISSN 0934-9839, E-ISSN 1435-6066, Vol. 28, no 1, p. 71-97Article in journal (Refereed) Published
Abstract [en]

The conceptual design phase is a critical step, since it influences the subsequent steps during product development with regard to cost, quality and performance. Previous research has focused on cross-functional teams within an organization. However, many product development projects benefit from the participation of members from different organizations, not least during the conceptual design phase of technical products, where it is essential to consider different aspects of the product-to-be. Therefore, we conducted an in-depth case study of a cross-functional inter-organizational group testing ideation methods in a real-life setting within a development project in the railway sector. The group comprised participants from an infrastructure manager, a supplier, a maintenance contractor and research bodies. The tested ideation methods were Method 635, the gallery method and the SIL method. The participants found working in a cross-functional inter-organizational group to be beneficial both during the group-analysis of the topics and during the generation of ideas on how to address the ideation topic. Applying the ideation methods to the ideation topics facilitated the sharing of information between participants, and the diversity of the group manifested itself in several ways during ideation. Overall, the gallery method was most popular, and the SIL method was least popular among the participants. 

Place, publisher, year, edition, pages
Springer, 2017
Keywords
New product development, Concept generation, Cross-functional groups, Inter-organizational groups, Ideation methods, Method 635, Gallery method, SIL method
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-36827 (URN)10.1007/s00163-016-0238-z (DOI)000396359400004 ()2-s2.0-84988701292 (Scopus ID)
Projects
OptiKrea
Note

Validerad; 2017; Nivå 2; 2017-02-20 (andbra)

Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2018-12-14Bibliographically approved
Mishra, M., Odelius, J., Thaduri, A., Nissen, A. & Rantatalo, M. (2017). Particle filter-based prognostic approach for railway track geometry. Mechanical systems and signal processing, 96, 226-238
Open this publication in new window or tab >>Particle filter-based prognostic approach for railway track geometry
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2017 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 96, p. 226-238Article in journal (Refereed) Published
Abstract [en]

Track degradation of ballasted railway track systems has to be measured on a regular basis, and these tracks must be maintained by tamping. Tamping aims to restore the geometry to its original shape to ensure an efficient, comfortable and safe transportation system. To minimize the disturbance introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. This paper proposes a particle filter-based prognostic approach for railway track degradation; this approach is demonstrated by examining different railway switches. The standard deviation of the longitudinal track degradation is studied, and forecasts of the maintenance limit intersection are derived. The particle filter-based prognostic results are compared with the standard regression method results for four railway switches, and the particle filter method shows similar or better result for the four cases. For longer prediction times, the error of the proposed method is equal to or smaller than that of the regression method. The main advantage of the particle filter-based prognostic approach is its ability to generate a probabilistic result based on input parameters with uncertainties. The distributions of the input parameters propagate through the filter, and the remaining useful life is presented using a particle distribution.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-63143 (URN)10.1016/j.ymssp.2017.04.010 (DOI)000401886800015 ()2-s2.0-85019145932 (Scopus ID)
Note

Validerad; 2017; Nivå 2; 2017-04-25 (andbra)

Available from: 2017-04-25 Created: 2017-04-25 Last updated: 2018-09-13Bibliographically approved
Mishra, M., Rantatalo, M. & Odelius, J. (2016). A Model-based Prognostic Approach to Predict Remaining Useful Life of Components. In: Jyoti K. Sinha, Akilu Yunusa-Kaltungo, Wolfgang Hahn (Ed.), Proceedings of 1st International Conference on Maintenance Engineering, IncoME-I, 2016: . Paper presented at 1st International Conference on Maintenance Engineering, IncoME-I, The University of Manchester, UK, August 30-31, 2016. , Article ID ME2016_1147.
Open this publication in new window or tab >>A Model-based Prognostic Approach to Predict Remaining Useful Life of Components
2016 (English)In: Proceedings of 1st International Conference on Maintenance Engineering, IncoME-I, 2016 / [ed] Jyoti K. Sinha, Akilu Yunusa-Kaltungo, Wolfgang Hahn, 2016, article id ME2016_1147Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

One of the major problems in the industry is the extension of the useful life of high-performance systems. Proper maintenance plays an important role by extending the useful life, reducing the lifecycle costs and improving the reliability and availability. Health management using a proper condition-based maintenance (CBM) deployment is a worldwide accepted strategy and has grown very popular in many industries over the past decades. A case of CBM is when the maintenance decision is taken based on a forecast of the asset state. This strategy is called predictive maintenance or prognostic health management (PHM). PHM is an engineering discipline that aims to maintain the system behaviour and function, and assure the mission success, safety and effectiveness. This strategy is relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase the profit and safety of the facilities concerned. Prognosis is the most critical part of this process and is nowadays recognized as a key feature in maintenance strategies since estimation of the remaining useful life (RUL) is essential.

PHM can provide a state assessment of the future health of systems or components, e.g. when a degraded state has been found. The aim of using PHM is to estimate how long it will take before the equipment will reach a failure threshold, in future operating conditions and future environmental conditions.

The aim of the paper is to improve the estimation of bearing RUL by dynamically updating the SKF L10 bearing life length calculation. Using a physics-based prognostic approach, the behaviour of a roller in a paper machine was simulated using the finite element method (FEM). A transfer function representing the relation between bearing acceleration and bearing forces was generated and used to convert the acceleration signal into an estimation of the dynamically changing bearing force. The estimated force is then used as input to the bearing life length calculation generating an updated L10 calculation for each time step. 

Keywords
Prognostics, Degradation, FEM, Modelling, Particle Filter, CBM, RUL, SKF L10
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-66625 (URN)
Conference
1st International Conference on Maintenance Engineering, IncoME-I, The University of Manchester, UK, August 30-31, 2016
Projects
SKF-UTC
Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2018-05-07Bibliographically 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
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