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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.
Öppna denna publikation i ny flik eller fönster >>A Survey on Underground Pipelines and Railway Infrastructure at Cross-Sections
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2019 (Engelska)Ingår i: ESREL-2019 / [ed] Michael beer, Enrico Zio, 2019Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Nyckelord
Underground Pipelines, Transportation Infrastructure, Railway, Maintenance, FMEA
Nationell ämneskategori
Tillförlitlighets- och kvalitetsteknik Vattenteknik Geoteknik
Forskningsämne
Byggkonstruktion; Trafikteknik
Identifikatorer
urn:nbn:se:ltu:diva-76471 (URN)10.3850/978-981-11-2724-3_0037-cd (DOI)978-981-11-2724-3 (ISBN)
Konferens
ESREL 2019 | European Safety and Reliability Conference
Projekt
PipeXrail
Forskningsfinansiär
Vinnova, 2016-033113
Anmärkning

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

Tillgänglig från: 2019-10-22 Skapad: 2019-10-22 Senast uppdaterad: 2019-11-22
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.
Öppna denna publikation i ny flik eller fönster >>Train-based differential eddy current sensor system for rail fastener detection
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2019 (Engelska)Ingår i: Measurement science and technology, ISSN 0957-0233, E-ISSN 1361-6501, Vol. 30, nr 12, artikel-id 125105Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Institute of Physics Publishing (IOPP), 2019
Nationell ämneskategori
Annan samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik; Drift och underhållsteknik
Identifikatorer
urn:nbn:se:ltu:diva-76340 (URN)10.1088/1361-6501/ab2b24 (DOI)000487122500002 ()2-s2.0-85075694567 (Scopus ID)
Anmärkning

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

Tillgänglig från: 2019-10-10 Skapad: 2019-10-10 Senast uppdaterad: 2019-12-10Bibliografiskt granskad
Saari, J. & Odelius, J. (2018). Detecting operation regimes using unsupervised clustering with infected group labelling to improve machine diagnostics and prognostics. Operations Research Perspectives, 5, 232-244
Öppna denna publikation i ny flik eller fönster >>Detecting operation regimes using unsupervised clustering with infected group labelling to improve machine diagnostics and prognostics
2018 (Engelska)Ingår i: Operations Research Perspectives, ISSN 2214-7160, Vol. 5, s. 232-244Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Estimating the stress level of components while operation modes are varying is a key issue for many prognostic models in condition monitoring. The identification of operation profiles during production is therefore important. Clustering condition monitoring data with regard to operation regimes will provide more detailed information about the variation of stress levels during production. The distribution of the operation regimes can then support prognostics by revealing the cause-and-effect relationship between the operation regimes and the wear level of components.

In this study unsupervised clustering technique was used for detecting operation regimes for an underground LHD (load-haul-dump machine) by using features extracted from vibration signals measured on the front axle and the speed of the Cardan axle. The clusters were also infected with a small portion of the data to obtain the corresponding labels for each cluster. Promising results were obtained where each sought-for operation regime was detected in a sensible manner using vibration RMS values together with speed.

Ort, förlag, år, upplaga, sidor
Elsevier, 2018
Nationell ämneskategori
Annan samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik
Identifikatorer
urn:nbn:se:ltu:diva-70391 (URN)10.1016/j.orp.2018.08.002 (DOI)000452765000023 ()2-s2.0-85051647575 (Scopus ID)
Anmärkning

Validerad;2018;Nivå 2;2018-08-27 (andbra)

Tillgänglig från: 2018-08-15 Skapad: 2018-08-15 Senast uppdaterad: 2019-04-23Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>INFRALERT: improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques
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2018 (Engelska)Ingår i: Proceedings of 7th Transport Research Arena TRA, Vienna, Austria, 2018, Vienna, Austria, 2018Konferensbidrag, Publicerat paper (Refereegranskat)
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

Ort, förlag, år, upplaga, sidor
Vienna, Austria: , 2018
Nyckelord
intelligent maintenance, linear transport infrastructure, condition nowcasting & forecasting, alert management, RAMS & LCC, decision support, maintenance & interventions planning
Nationell ämneskategori
Tillförlitlighets- och kvalitetsteknik Annan samhällsbyggnadsteknik
Forskningsämne
Hållbara transporter (FOI); Drift och underhållsteknik
Identifikatorer
urn:nbn:se:ltu:diva-65940 (URN)
Konferens
7th Transport Research Arena TRA 2018, Vienna, 16 – 19 April 2018
Forskningsfinansiär
EU, Horisont 2020, SEP-210181906
Tillgänglig från: 2017-10-03 Skapad: 2017-10-03 Senast uppdaterad: 2018-06-25Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Selection of features for fault diagnosis on rotating machines using random forest and wavelet analysis
2018 (Engelska)Ingår i: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 60, nr 8, s. 434-442Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
British Institute of Non-Destructive Testing, 2018
Nationell ämneskategori
Annan samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik; Centrumbildning - SKF-LTU University Technology Cooperation
Identifikatorer
urn:nbn:se:ltu:diva-70433 (URN)10.1784/insi.2018.60.8.434. (DOI)000441327800006 ()2-s2.0-85051538361 (Scopus ID)
Anmärkning

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

Tillgänglig från: 2018-08-16 Skapad: 2018-08-16 Senast uppdaterad: 2019-03-26Bibliografiskt granskad
Jiménez-Redondo, N., Calle-Cordón, Á., Kandler, U., Simroth, A., Morales, F. J., Reyes, A., . . . Duarte, E. (2017). Improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques (INFRALERT). Paper presented at BESTInfra 2017, Czech Technical University, Prague, Czech Republic, September 21-22 2017. IOP Conference Series: Materials Science and Engineering, 236
Öppna denna publikation i ny flik eller fönster >>Improving linear transport infrastructure efficiency by automated learning and optimised predictive maintenance techniques (INFRALERT)
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2017 (Engelska)Ingår i: IOP Conference Series: Materials Science and Engineering, ISSN 1757-8981, E-ISSN 1757-899X, Vol. 236Artikel i tidskrift (Refereegranskat) Published
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. It includes two real pilots for road and railways infrastructure. 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 methodologies and preliminary results of the toolkits for i) nowcasting and forecasting of asset condition, ii) alert generation, iii) RAMS & LCC analysis and iv) decision support. The results of these toolkits in a meshed road network in Portugal under the jurisdiction of Infraestruturas de Portugal (IP) are presented showing the capabilities of the approaches.

Ort, förlag, år, upplaga, sidor
Prague, Czech Republic: Institute of Physics (IOP), 2017
Nationell ämneskategori
Annan samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik
Identifikatorer
urn:nbn:se:ltu:diva-67204 (URN)10.1088/1757-899X/236/1/012105 (DOI)000417428700105 ()
Konferens
BESTInfra 2017, Czech Technical University, Prague, Czech Republic, September 21-22 2017
Projekt
INFRALERT
Anmärkning

Konferensartikel i tidskrift

Tillgänglig från: 2018-01-09 Skapad: 2018-01-09 Senast uppdaterad: 2018-11-16Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Industrial internet applications for efficient road winter maintenance
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2017 (Engelska)Ingår i: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, nr 3, s. 355-367Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Emerald Group Publishing Limited, 2017
Nationell ämneskategori
Teknik och teknologier Annan samhällsbyggnadsteknik Teknisk mekanik
Forskningsämne
Drift och underhållsteknik; Experimentell mekanik
Identifikatorer
urn:nbn:se:ltu:diva-65114 (URN)10.1108/JQME-11-2016-0071 (DOI)2-s2.0-85027974857 (Scopus ID)
Anmärkning

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

Tillgänglig från: 2017-08-15 Skapad: 2017-08-15 Senast uppdaterad: 2018-06-25Bibliografiskt granskad
Calle-Cordón, Á., Jiménez-Redondo, N., Morales-Gámiz, F. J., García-Villena, F., Garmabaki, A. & Odelius, J. (2017). Integration of RAMS in LCC analysis for linear transportinfrastructures: A case study for railways. Paper presented at BESTInfra 2017, Czech Technical University, Prague, Czech Republic, September 21-22 2017. IOP Conference Series: Materials Science and Engineering, 236
Öppna denna publikation i ny flik eller fönster >>Integration of RAMS in LCC analysis for linear transportinfrastructures: A case study for railways
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2017 (Engelska)Ingår i: IOP Conference Series: Materials Science and Engineering, ISSN 1757-8981, E-ISSN 1757-899X, Vol. 236Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Life-cycle cost (LCC) analysis is an economic technique used to assess the totalcosts associated with the lifetime of a system in order to support decision making in long termstrategic planning. For complex systems, such as railway and road infrastructures, the cost ofmaintenance plays an important role in the LCC analysis. Costs associated with maintenanceinterventions can be more reliably estimated by integrating the probabilistic nature of thefailures associated to these interventions in the LCC models. Reliability, Maintainability,Availability and Safety (RAMS) parameters describe the maintenance needs of an asset in aquantitative way by using probabilistic information extracted from registered maintenanceactivities. Therefore, the integration of RAMS in the LCC analysis allows obtaining reliablepredictions of system maintenance costs and the dependencies of these costs with specific costdrivers through sensitivity analyses. This paper presents an innovative approach for acombined RAMS & LCC methodology for railway and road transport infrastructures beingdeveloped under the on-going H2020 project INFRALERT. Such RAMS & LCC analysisprovides relevant probabilistic information to be used for condition and risk-based planning ofmaintenance activities as well as for decision support in long term strategic investmentplanning.

Ort, förlag, år, upplaga, sidor
Institute of Physics (IOP), 2017
Nationell ämneskategori
Tillförlitlighets- och kvalitetsteknik Annan samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik; Trafikteknik; Hållbara transporter (FOI)
Identifikatorer
urn:nbn:se:ltu:diva-65561 (URN)10.1088/1757-899X/236/1/012106 (DOI)000417428700106 ()
Konferens
BESTInfra 2017, Czech Technical University, Prague, Czech Republic, September 21-22 2017
Projekt
INFRALERT
Forskningsfinansiär
EU, Horisont 2020, 636496
Anmärkning

2018-01-09 (andbra);Konferensartikel i tidskrift;Bibliografisk uppgift: This research was carried out within the INFRALERT project. This project has received funding fromthe EU Horizon 2020 research and innovation programme under grant agreement No 636496. Theauthors also thank Trafikverket for providing the data in the case study analysis.

Tillgänglig från: 2017-09-11 Skapad: 2017-09-11 Senast uppdaterad: 2018-11-16Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Particle filter-based prognostic approach for railway track geometry
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2017 (Engelska)Ingår i: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 96, s. 226-238Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Elsevier, 2017
Nationell ämneskategori
Annan samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik
Identifikatorer
urn:nbn:se:ltu:diva-63143 (URN)10.1016/j.ymssp.2017.04.010 (DOI)000401886800015 ()2-s2.0-85019145932 (Scopus ID)
Anmärkning

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

Tillgänglig från: 2017-04-25 Skapad: 2017-04-25 Senast uppdaterad: 2018-09-13Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>A Model-based Prognostic Approach to Predict Remaining Useful Life of Components
2016 (Engelska)Ingår i: Proceedings of 1st International Conference on Maintenance Engineering, IncoME-I, 2016 / [ed] Jyoti K. Sinha, Akilu Yunusa-Kaltungo, Wolfgang Hahn, 2016, artikel-id ME2016_1147Konferensbidrag, Muntlig presentation med publicerat abstract (Refereegranskat)
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. 

Nyckelord
Prognostics, Degradation, FEM, Modelling, Particle Filter, CBM, RUL, SKF L10
Nationell ämneskategori
Teknik och teknologier Annan samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik
Identifikatorer
urn:nbn:se:ltu:diva-66625 (URN)
Konferens
1st International Conference on Maintenance Engineering, IncoME-I, The University of Manchester, UK, August 30-31, 2016
Projekt
SKF-UTC
Tillgänglig från: 2017-11-17 Skapad: 2017-11-17 Senast uppdaterad: 2018-05-07Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-0216-5058

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