After-sales services optimisation through dynamic opportunistic maintenance: a wind energy case study

After-sales maintenance services can be a very profitable source of incomes for original equipment manufacturers (OEM) due to the increasing interest of assets’ users on performance-based contracts. However, when it concerns the product value-adding process, OEM have traditionally been more focused on improving their production processes, rather than on complementing their products by offering after-sales services; consequently leading to difficulties in offering them efficiently. Furthermore, both due to the high uncertainty of the assets’ behaviour and the inherent challenges of managing the maintenance process (e.g. maintenance strategy to be followed or resources to be deployed), it is complex to make business out of the provision of after-sales services. With the aim of helping the business and maintenance decision makers at this point, this paper proposes a framework for optimising the incomes of after-sales maintenance services through: 1) implementing advanced multi-objective opportunistic maintenance strategies that sistematically consider the assets’ operational context in order to perform preventive maintenance during most favourable conditions, 2) considering the specific OEMs’ and users’ needs, and 3) assessing both internal and external uncertainties that might condition the after-sales services’ success. The developed case study for the wind energy sector demonstrates the suitability of the presented framework for optimising the after-sales services.EU Framework Programme Horizon 2020, MSCA-RISE-2014: Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) (grant agreement number 645733- Sustain-Owner-H2020-MSCA-RISE-2014) and the EmaitekPlus 2016-2017 Program of the Basque Government

Principles of Periodontology

Periodontal diseases are among the most common diseases affecting humans. Dental biofilm is a contributor to the etiology of most periodontal diseases. It is also widely accepted that immunological and inflammatory responses to biofilm components are manifested by signs and symptoms of periodontal disease. The outcome of such interaction is modulated by risk factors (modifiers), either inherent (genetic) or acquired (environmental), significantly affecting the initiation and progression of different periodontal disease phenotypes. While definitive genetic determinants responsible for either susceptibility or resistance to periodontal disease have yet to be identified, many factors affecting the pathogenesis have been described, including smoking, diabetes, obesity, medications, and nutrition. Currently, periodontal diseases are classified based upon clinical disease traits using radiographs and clinical examination. Advances in genomics, molecular biology, and personalized medicine may result in new guidelines for unambiguous disease definition and diagnosis in the future. Recent studies have implied relationships between periodontal diseases and systemic conditions. Answering critical questions regarding host‐parasite interactions in periodontal diseases may provide new insight in the pathogenesis of other biomedical disorders. Therapeutic efforts have focused on the microbial nature of the infection, as active treatment centers on biofilm disruption by non‐surgical mechanical debridement with antimicrobial and sometimes anti‐inflammatory adjuncts. The surgical treatment aims at gaining access to periodontal lesions and correcting unfavorable gingival/osseous contours to achieve a periodontal architecture that will provide for more effective oral hygiene and periodontal maintenance. In addition, advances in tissue engineering have provided innovative means to regenerate/repair periodontal defects, based upon principles of guided tissue regeneration and utilization of growth factors/biologic mediators. To maintain periodontal stability, these treatments need to be supplemented with long‐term maintenance (supportive periodontal therapy) programs

System design and maintenance modelling for safety in extended life operation

It is frequently the most cost effective option to operate systems and infrastructure over an extended life period rather than enter a new build programme. The condition and performance of existing systems operated beyond their originally intended design life are controlled through maintenance. For new systems there is the option to simultaneously develop the design and the maintenance processes for best effect when a longer life expectancy is planned. This paper reports a combined Petri net and Bayesian network approach to investigate the effects of design and maintenance features on the system performance. The method has a number of features which overcome limitations in traditionally used system performance modelling techniques, such as fault tree analysis, and also enhances the modelling capabilities. Significantly, for the assessment of aging systems, the new method avoids the need to assume a constant failure rate over the lifetime duration. In addition the assumption of independence between component failures events is no longer required. In comparison with the commonly applied system modelling techniques, this new methodology also has the capability to represent the maintenance process in far greater detail and as such options for: inspection and testing, servicing, reactive repair and component replacement based on condition, age or use can all be included. In considering system design options, levels of redundancy and diversity along with the component types selected can be investigated. All of the options for the design and maintenance can be incorporated into a single integrated Petri net and Bayesian network model and turned on and off as required to predict the effects of any combination of options selected. In addition this model has the ability to evaluate different system failure modes. The integrated Petri-net and Bayesian network approach is demonstrated through application to a remote un-manned wellhead platform from the oil and gas industry

Maintenance Optimization and Inspection Planning of Wind Energy Assets: Models, Methods and Strategies

Designing cost-effective inspection and maintenance programmes for wind energy farms is a complex task involving a high degree of uncertainty due to diversity of assets and their corresponding damage mechanisms and failure modes, weather-dependent transport conditions, unpredictable spare parts demand, insufficient space or poor accessibility for maintenance and repair, limited availability of resources in terms of equipment and skilled manpower, etc. In recent years, maintenance optimization has attracted the attention of many researchers and practitioners from various sectors of the wind energy industry, including manufacturers, component suppliers, maintenance contractors and others. In this paper, we propose a conceptual classification framework for the available literature on maintenance policy optimization and inspection planning of wind energy systems and structures (turbines, foundations, power cables and electrical substations). The developed framework addresses a wide range of theoretical and practical issues, including the models, methods, and the strategies employed to optimise maintenance decisions and inspection procedures in wind farms. The literature published to date on the subject of this article is critically reviewed and several research gaps are identified. Moreover, the available studies are systematically classified using different criteria and some research directions of potential interest to operational researchers are highlighted

An Intelligent Fleet Condition-Based Maintenance Decision Making Method Based on Multi-Agent

According to the demand for condition-based maintenance online decision making among a mission oriented fleet, an intelligent maintenance decision making method based on Multi-agent and heuristic rules is proposed. The process of condition-based maintenance within an aircraft fleet (each containing one or more Line Replaceable Modules) based on multiple maintenance thresholds is analyzed. Then the process is abstracted into a Multi-Agent Model, a 2-layer model structure containing host negotiation and independent negotiation is established, and the heuristic rules applied to global and local maintenance decision making is proposed. Based on Contract Net Protocol and the heuristic rules, the maintenance decision making algorithm is put forward. Finally, a fleet consisting of 10 aircrafts on a 3-wave continuous mission is illustrated to verify this method. Simulation results indicate that this method can improve the availability of the fleet, meet mission demands, rationalize the utilization of support resources and provide support for online maintenance decision making among a mission oriented fleet

Designing as Construction of Representations: A Dynamic Viewpoint in Cognitive Design Research

This article presents a cognitively oriented viewpoint on design. It focuses on cognitive, dynamic aspects of real design, i.e., the actual cognitive activity implemented by designers during their work on professional design projects. Rather than conceiving de-signing as problem solving - Simon's symbolic information processing (SIP) approach - or as a reflective practice or some other form of situated activity - the situativity (SIT) approach - we consider that, from a cognitive viewpoint, designing is most appropriately characterised as a construction of representations. After a critical discussion of the SIP and SIT approaches to design, we present our view-point. This presentation concerns the evolving nature of representations regarding levels of abstraction and degrees of precision, the function of external representations, and specific qualities of representation in collective design. Designing is described at three levels: the organisation of the activity, its strategies, and its design-representation construction activities (different ways to generate, trans-form, and evaluate representations). Even if we adopt a "generic design" stance, we claim that design can take different forms depending on the nature of the artefact, and we propose some candidates for dimensions that allow a distinction to be made between these forms of design. We discuss the potential specificity of HCI design, and the lack of cognitive design research occupied with the quality of design. We close our discussion of representational structures and activities by an outline of some directions regarding their functional linkages

Condition-based maintenance of wind turbine blades

The blades of offshore wind farms (OWTs) are susceptible to a wide variety of diverse sources of damage. Internal impacts are caused primarily by structure deterioration, so even though outer consequences are the consequence of harsh marine ecosystems. We examine condition-based maintenance (CBM) for a multiblade OWT system that is exposed to environmental shocks in this work. In recent years, there has been a significant rise in the number of wind turbines operating offshore that make use of CBMs. The gearbox, generator, and drive train all have their own vibration-based monitoring systems, which form most of their foundation. For the blades, drive train, tower, and foundation, a cost analysis of the various widely viable CBM systems as well as their individual prices has been done. The purpose of this article is to investigate the potential benefits that may result from using these supplementary systems in the maintenance strategy. Along with providing a theoretical foundation, this article reviews the previous research that has been conducted on CBM of OWT blades. Utilizing the data collected from condition monitoring, an artificial neural network is employed to provide predictions on the remaining life. For the purpose of assessing and forecasting the cost and efficacy of CBM, a simple tool that is based on artificial neural networks (ANN) has been developed. A CBM technique that is well-established and is based on data from condition monitoring is used to reduce cost of maintenance. This can be accomplished by reducing malfunctions, cutting down on service interruption, and reducing the number of unnecessary maintenance works. In MATLAB, an ANN is used to research both the failure replacement cost and the preventative maintenance cost. In addition to this, a technique for optimization is carried out to gain the optimal threshold values. There is a significant opportunity to save costs by improving how choices are made on maintenance to make the operations more cost-effective. In this research, a technique to optimizing CBM program for elements whose deterioration may be characterized according to the level of damage that it has sustained is presented. The strategy may be used for maintenance that is based on inspections as well as maintenance that is based on online condition monitoring systems

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security