| 1. |
- Galar, Diego, et al.
(författare)
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Hybrid models for PHM deployment techniques in railway
- 2013
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Ingår i: 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013. - 9781629939926 ; , s. 1047-1056
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Konferensbidrag (refereegranskat)abstract
- Many railway assets exhibit increasing wear and tear of equipment during operation. Prognostics are viewed as an add-on capability to diagnosis; they assess the current health of a system and predict its remaining life based on features that capture the gradual degradation in the operational capabilities of a system. Prognostics are critical to improve safety, plan successful missions, schedule maintenance, reduce maintenance cost and down time. Unlike fault diagnosis, prognosis is a relatively new area and became an important part of Condition-based Maintenance (CBM) of systems. Currently, there are many prognostic techniques; their usage must be tuned for each application. The prognostic methods can be classified as being associated with one or more of the following two approaches: data-driven and model-based. Each of these approaches has its own advantages and disadvantages, and consequently, they are often used in combination in many applications called hybrid. A hybrid model could combine some or all of model types (data-driven, and phenomenological), so that more complete information allows for more accurate recognition of the fault state. This approach is especially relevant in railway where the maintainer and operator know some of the failure mechanisms, but the complexity of the infrastructure and rolling stock is huge so no way to develop a complete model based approach that is why development of hybrid models becomes necessary to estimate RUL of railway systems in a more accurate way. The paper address this process of data aggregation into the hybrid model in order to get RUL values within logical confidence intervals so railway assets life cycle can be managed and optimized.
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| 2. |
- Galar, Diego, et al.
(författare)
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Hybrid prognosis for railway health assessment : an information fusion approach for PHM deployment
- 2013
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Ingår i: PHM2013: 2013 Prognostic and System Health Management. - : AIDIC Servizi S.r.l.. - 9788895608242 ; , s. 769-774
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Konferensbidrag (refereegranskat)abstract
- Many railway assets suffer increasing wear and tear during operation. Prognostics can assist diagnosis by assessing the current health of a system and predicting its remaining life based on features that capture the gradual degradation in a system's operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Unlike fault diagnosis, prognosis is a relatively new area, but it has become an important part of Condition-based Maintenance (CBM) of systems. As there are many prognostic techniques, usage must be attuned to particular applications. Broadly stated, prognostic methods are either data-driven or model-based. Each has advantages and disadvantages; consequently, they are often combined in hybrid applications. A approach hybrid model can combine some or all model types (data-driven, and phenomenological); thus, more complete information can be gathered, leading to more accurate recognition of the fault state. This approach is especially relevant in railway systems where the maintainer and operator know some of the failure mechanisms, but the complexity of the infrastructure and rolling stock is huge that there is no way to develop a complete model-based approach. Therefore, hybrid models are extremely useful for accurately estimating the Remaining Useful Life (RUL) of railway systems. The paper addresses the process of data aggregation into a hybrid model to get RUL values within logical confidence intervals so that the life cycle of railway assets can be managed and optimised
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| 3. |
- Johansson, Carl-Anders, et al.
(författare)
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Green Condition based Maintenance - an integrated system approach for health assessment and energy optimization of manufacturing machines.
- 2013
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Ingår i: 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013. - 9781629939926 ; , s. 1069-1084
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Konferensbidrag (refereegranskat)abstract
- The normal strategy to keep production systems in good conditions is to apply preventive maintenance practices, with a supportive workforce "reactive" in the case of clearly detected malfunctions. This impact on quality, cost and in general, productivity. Added to this, the uncertainty of machine reliability at any given time, also impacts on product/production delivery times. It is known also that a worn-out mechanism can have higher energy consumption. The use of intelligent predictive technologies could contribute to improve the situation, but these techniques are not widely used in the production environment. Often sensors and monitors required for the production environment are non-standard and require costly implementations. Monitoring and profiling the electric current consumption in combination with operational data is an easy to implement Green Condition based Maintenance (Green CBM) technique to improve the overall business effectiveness, under a triple perspective: • Optimizing maintenance strategies based on the prediction of potential failures and schedule maintenance operations in convenient periods and avoid unexpected breakdowns • Operation: Managing energy as a production resource and reduce its consumption • Product reliability: Providing the machine tool builder with real data about the behaviour of the product and their critical components This also opens for new business models for maintenance and service providers. The described Green CBM technique can be applied in many types of machines. In machine tools, focusing on spindles and linear guides, as responsible for the most common and cost-intensive downtimes
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| 4. |
- Perales, Numan, et al.
(författare)
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A comparison of techniques to determine the nominal life (L10) on railway bearings
- 2014
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Konferensbidrag (refereegranskat)abstract
- Bearings are one of the most important components in a railway vehicle for safety, since a failsafe design is not available. Contact fatigue, internal clearance, corrosion, and contamination of the lubricating oil can cause bearing failures. Generally, failures show up as imperfections in the ball race, in the ball/roller or in the retainer. The more frequent defects are caused by contact fatigue [1]. The analytical methodology (Methodology I) is an alternative approach proposed by the authors [1], inspired by a procedure used to design rail shafts [2]. The purpose is to compare the obtained life using methodology I, with the life estimated by LKAB company. The latter uses a calculation method well-established in manuals of bearing manufacturers such as SKF and Timken. This is to avoid a drawback observed in this type of approach, namely, the lack of rigour in defining terms associated with the mathematical model used to estimate L10. The research goal is to perform an inspection and analysis of the functioning of the bearings most commonly used on railways in order to estimate the bearings' life under real operating conditions.
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| 5. |
- Sinkkonen, Tiina, et al.
(författare)
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Using the life-cycle model with value thinking for managing an industrial maintenance network
- 2016
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Ingår i: International Journal of Industrial and Systems Engineering. - 1748-5037. ; 23:1, s. 19-35
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Tidskriftsartikel (refereegranskat)abstract
- The objective of this article is to create a general life-cycle model for maintenance decision making in different industries at the item level. The need for network-level tools will increase, as inter-organisational collaboration is emphasised more and more. Previous life-cycle models have mostly viewed the matter from the perspective of just one company, but our model takes the different members of maintenance networks into account. We have also integrated value thinking with life-cycle accounting, as it is crucial for companies to perceive which elements increase the value of each member in their network. The value-based life-cycle model introduced in this article has been mainly developed to support the future planning of maintenance operations. In addition, it can be designed how additional value can be reached through future maintenance and how this value can be equitably shared between the network partners
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| 6. |
- Villarejo, Roberto, et al.
(författare)
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Bottom to Top Approach for Railway KPI Generation
- 2017
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Ingår i: Management Systems in Production Engineering. - : Walter de Gruyter. - 2299-0461 .- 2450-5781. ; 25:3, s. 191-198
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Tidskriftsartikel (refereegranskat)abstract
- Railway maintenance especially on infrastructure produces a vast amount of data. However, having data is not synonymous with having information; rather, data must be processed to extract information. In railway maintenance, the development of key performance indicators (KPIs) linked to punctuality or capacity can help planned and scheduled maintenance, thus aligning the maintenance department with corporate objectives. There is a need for an improved method to analyse railway data to find the relevant KPIs. The system should support maintainers, answering such questions as what maintenance should be done, where and when. The system should equip the user with the knowledge of the infrastructure's condition and configuration, and the traffic situation so maintenance resources can be targeted to only those areas needing work. The amount of information is vast, so it must be hierarchized and aggregated; users must filter out the useless indicators. Data are fused by compiling several individual indicators into a single index; the resulting composite indicators measure multidimensional concepts which cannot be captured by a single index. The paper describes a method of monitoring a complex entity. In this scenario, a plurality of use indices and weighting values are used to create a composite and aggregated use index from a combination of lower level use indices and weighting values. The resulting composite and aggregated indicators can be a decision-making tool for asset managers at different hierarchical levels.
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| 7. |
- Villarejo, Roberto, et al.
(författare)
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Context Awareness And Railway Maintenance
- 2014
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Ingår i: Proceedings of the 3rd international workshop and congress on eMaintenance. - Luleå : Luleå tekniska universitet. - 9789174399721 - 9789174399738 ; , s. 17-24
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Konferensbidrag (refereegranskat)abstract
- A railway is an extremely complex system requiring maintenancedecision support systems to gather data from many disparatesources. These sources include traditional maintenanceinformation like condition monitoring or work records, as well astraffic information, given the criticality of maintenance inavoiding traffic disruptions and the need to minimise the trackpossession time for maintenance.A methodology is required if maintainers are to understand thedata as a whole. Context engines try to link the various dataconstellations and to define interactions within the railwaysystem. This is not easy since data have different natures, originsand granularity. But if all information surrounding the railwayasset can be considered, decisions will be more accurate andproblems like false alarms or outlying anomalies will be detected.The contextualisation of the data seems to be a feasible way toallow condition monitoring data i.e physical measurements andother variables, to be understood under certain conditions(weather, regulations etc.) and as a consequence of certain actions(maintenance interventions, overhauls, outsourcing warrantiesetc.).This paper proposes the use of context engines to providemeaningful information out of the overwhelming amount ofcollected and recorded data so that proper maintenance decisionscan be made. In this scenario, fluffy information coming fromwork orders and expertise of maintainers is a big issue since suchinformation must be converted to numerical values. The fuzzylogic approach seems a promising way to integrate suchinformation sources for diagnosis.
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| 8. |
- Villarejo, Roberto, et al.
(författare)
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Context-driven decisions for railway maintenance
- 2016
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Ingår i: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit. - : SAGE Publications. - 0954-4097 .- 2041-3017. ; 230:5, s. 1469-1483
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Tidskriftsartikel (refereegranskat)abstract
- Railway assets suffer wear and tear during operation. Prognostics can be used to assess the current health of a system and predict its remaining life, based on features that capture the gradual degradation of its operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Prognosis is a relatively new area; however, it has become an important part of condition-based maintenance of systems. As there are many prognostic techniques, usage must be tuned to particular applications. Broadly stated, prognostic methods are either data driven, or rule or model based. Each approach has advantages and disadvantages, depending on the hierarchical level of the analysed item; consequently, they are often combined in hybrid applications. A hybrid model can combine some or all model types; thus, more-complete information can be gathered, leading to more-accurate recognition of the impending fault state. However, the amount of information collected from disparate data sources is increasing exponentially and has different natures and granularity; therefore, there is a real need for context engines to establish meaningful data links for further exploration. This approach is especially relevant in railway systems where the maintainer and operator know some of the failure mechanisms, but the sheer complexity of the infrastructure and rolling stock precludes the development of a complete model-based approach. Hybrid models are extremely useful for accurately estimating the remaining useful life (RUL) of railway systems. This paper addresses the process of data aggregation into a contextual awareness hybrid model to obtain RUL values within logical confidence intervals so that the life cycle of railway assets can be managed and optimized.
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