Assessing the impact of operational context variables on rolling stock reliability. A real case study

Izquierdo, Juan Santiago Murgui; Crespo, Adolfo; Uribetxebarria, Jone; Erguido, Asier

doi:10.1201/9781351174664-70

Cited by 4 publications

(6 citation statements)

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“…Whilst reviewing relevant related works, it is inevitable to encounter with research making use of the Proportional Hazards Model to relate the reliability of the components with operational parameters, e.g. [53,54]. Another recurrent approach, which has attracted considerable attention lately (see [55]), is the application of Articial Neural Networks (ANN) due to their capabilities to represent non-linear relationships [56], and also due to the fact that no "a priori" assumption of the model is required [57].…”

Section: Related Workmentioning

confidence: 99%

On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects

et al. 2020

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Section: Related Workmentioning

confidence: 99%

On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects

et al. 2020

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“…A more realistic estimation of reliability, through a model integrating operating environment information, will enable more effective and better-customized maintenance policies [11]. The benefits of such models gain special interest in the early stages of the asset life-cycle, where it is relevant to reflect the context into which they will perform [12].…”

Section: Motivation and Researchmentioning

confidence: 99%

“…Afterward, several applications of the model can be found in many different industries and systems. The model have been proposed for the rail industry to predict rolling stock reliability, for instance in the door system [27] or the Heating Ventilating and Air Conditioning (HVAC) system [11]. It is also proposed for the wind energy sector, to model failure rate in wind turbines [28] and to assess the turbines stress condition [29].…”

Section: Motivation and Researchmentioning

confidence: 99%

Dynamic artificial neural network-based reliability considering operational context of assets.

Izquierdo

Márquez

Uribetxebarria

2019

Reliability Engineering & System Safety

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Assets reliability is a key issue to consider in the maintenance management policy and given its importance several estimation methods and models have been proposed within the reliability engineering discipline. However, these models involve certain assumptions which are the source of different uncertainties inherent to the estimations. An important source of uncertainty is the operational context in which the assets operate and how it affects the different failures. Therefore, this paper contributes to the reduction of the uncertainty coming from the operational context with the proposal of a novel method and its validation through a case study. The proposed model specifically addresses changes in the operational context by implementing dynamic capabilities in a new conception of the Proportional Hazards Model. It also allows to model interactions among working environment variables as well as hidden phenomena thanks to the integration within the model of artificial neural network methods.

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“…However, aiming at lowering the LCoE by reducing O&M costs is a two-fold challenge since it also entails minimizing the lost energy production [8] for the entire lifecycle, which oscillates around 20 years perspective [9]. According to the International Renewable Agency, the costs associated with O&M In the context of maintenance, several studies have proven the utility of reliability approaches to optimize it [5,16,17]. If seeking to take forward a reliability study, the information needed as an input to the decision should be at hand, in the right format, and on time [18].…”

Section: Background and Introductionmentioning

confidence: 99%

“…The heterogeneity in the failure behavior among WTs may be caused by the presence of different models of WTs, which entail different technical solutions [23]. Moreover, this multiplicity in the failure frequencies may also be the result of the different operational conditions of the assets [16,17,24,25].…”

Section: Background and Introductionmentioning

confidence: 99%

Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance

et al. 2019

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Energies 2019, 12, 2036 2 of 17 account for up to one quarter of the proportion of the LCoE, with 80% of this expenses directly attributed to maintenance [10][11][12].In view of the maintenance role in the LCoE and thus in the profitability of WFs, it is important to consider models for optimization of O&M plans and decisions [2]. The evolution of maintenance models and methodologies have kept pace with the constant technological evolution of Wind Turbines (WTs) [3]. According to [3], the goal of all the approaches and methodologies is determining the most adequate maintenance plan, the management of the resources, and the aspects related to Reliability, Availability, and Maintainability (RAM) of the WTs.Within such context, the WF operators are bound to develop new techniques and decision-support tools for optimal maintenance strategies, if they strive to maximize the profitability of the investment [8]. Accordingly, maintenance management discipline acquires a highly significant position since it provides a comprehensive perspective for the management of WTs, allowing for optimal maintenance strategies which reduce maintenance costs while maximizing availability [3,5].The decision-making process in the asset management field has been divided into strategic (long-term), tactic (medium-term), and operational (short-term) to achieve excellence in maintenance [13][14][15]. It is the purpose of the research in this paper to address the different aspects of the maintenance decision-making process. This is done by answering the research question of whether it is possible to achieve maintenance excellence for the lifecycle of WFs by a maintenance strategy which considers the different behaviors of the WTs and integrated business-related objectives.Aiming at providing an answer for the aforementioned research question, a technical framework for managing maintenance is proposed in the paper. The framework is a comprehensive proposal that considers different aspects regarding the maintenance of a WF. Within the possibilities offered by the current trend for big data, certain key aspects to create a failure database are integrated in the framework, which enables a clustering approach based on the failure behaviors of the different failure modes of each WT. This approach supports an opportunistic maintenance policy with dynamic thresholds that regard not only to the reliability of the assets but also business considerations. Besides, the framework also incorporates the strategic view through a Life-Cycle Cost (LCC) perspective integrated by means of a multi-objective optimization and supported by simulation techniques that provide valuable information to find an attractive trade-off between cost and performance.

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Assessing the impact of operational context variables on rolling stock reliability. A real case study

Cited by 4 publications

References 1 publication

On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects

On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects

Dynamic artificial neural network-based reliability considering operational context of assets.

Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance

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