This paper discusses a lean instrumentation framework for guiding the introduction of the lean concept in condition monitoring in order to enhance the organizational capability (i.e. human, technical and management trichotomy) and reduce the complexity in the maintenance management systems of industrial companies. Additionally, decision-making, based on severity diagnosis and prognosis in condition monitoring, is a complex maintenance function which is based on large data-set of sensors measurements.

Yet, the entirety of such decision-making is not dependent on only the sensors measurements, but also on other important indices, such as the human factors, organizational aspects and knowledge management. This is because, the ability to identify significant features from large amount of measured data is a major challenge for automated defect diagnosis, a situation that necessitate the need to identify signal transformations and features in new domains.

The need for the lean instrumentation framework is justified by the desire to have a modern condition monitoring system with the capability of pruning to the optimal level the number of sensors required for efficient and effective serviceability of the maintenance process. It is concluded that there are methodologies that can be developed to enable more efficient condition monitoring systems, with benefits for many processes along the value chain.

The assessment and analysis of railway infrastructure capacity is an essential task in railway infrastructure management carried out to meet the required quality and capacity demand of railway transport. For sustainable and dependable infrastructure management, it is important to assess railway capacity limitation from the point of view of infrastructure performance. However, the existence of numerous performance indicators often leads to diffused information that is not in a format suitable to support decision making. In this paper, we demonstrated the use of fuzzy inference system for aggregating selected railway infrastructure performance indicators to relate maintenance function to capacity situation.

The selected indicators consider the safety, comfort, punctuality and reliability aspects of railway infrastructure performance. The resulting composite indicator gives a reliable quantification of the health condition or integrity of railway lines. A case study of the assessment of overall infrastructure performance which is an indication of capacity limitation is presented using indicator data between 2010 and 2012 for five lines on the network of Trafikverket (Swedish Transport Administration).

The results are presented using customised performance dashboard for enhanced visualisation, quick understanding and relevant comparison of infrastructure conditions for strategic management. This gives additional information on capacity status and limitation from maintenance management perspective.

Fault detection is a crucial step in condition based maintenance requiring. The importance of fault diagnosis necessitates an efficient and effective failure pattern identification method. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) emerging as prospective pattern recognition techniques in fault diagnosis have been showing its adaptability, flexibility and efficiency. Regardless of variants of the two techniques, this paper discusses the principle of the two techniques, and discusses their theoretical similarity and difference. Eventually using the commonest ANN, SVM, a case study is presented for fault diagnosis using a wide used bearing data. Their performances are compared in terms of accuracy, computational cost and stability

Over the last decade, system integration is applied more as it allows organizations to streamline business processes. A recent development in the asset engineering management is to leverage the investment already made in process control systems. This allows the operations, maintenance, and process control teams to monitor and determine new alarm level based on the physical condition data of the critical machines. Condition-based maintenance (CBM) is a maintenance philosophy based on this massive data collection, wherein equipment repair or replacement decisions depend on the current and projected future health of the equipment.

Since, past research has been dominated by condition monitoring techniques for specific applications; the maintenance community lacks a generic CBM implementation method based on data mining of such vast amount of collected data. The methodology would be relevant across different domains. It is necessary to integrate Condition Monitoring (CM) data with management data from CMMS (Computer Maintenance Management Systems) which contains information, such as: component failures, failure information related data, servicing or repairs, and inventory control and so on. These systems are the core of traditional scheduled maintenance practices and rely on bulk observations from historical data to make modifications to regulated maintenance actions.

The most obvious obstacle in the integration of CMMS, process and CM data is the disparate nature of the data types involved, and there have benn several attempts to remedy this problem. Although, there have been many recent efforts to collect and maintain large repositories of these types of data, there have been relatively few studies to identify the ways these to datasets could be related.

This paper attempts to fulfill that need by proposing a combined data mining-based methodology for CBM considering CM data and Historical Maintenance Management data. It shows a system integration of physical and management data that also supports business intelligence and data mining where data sets can be combined in non-traditional ways.

With increasing amounts of data being generated by businesses and researchers, there is a need for fast, accurate and robust algorithms for data analysis. Improvements in database's technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. The primary aim of data mining is knowledge discovery, i.e. patterns in the data that lead to better understanding of the data generating process and to useful predictions.

The knowledge that becomes available through data mining enables an asset owner to make important decisions about life cycle costs in advance. In maintenance field, CMMS (Computer maintenance management system) and CM (Condition Monitoring) are the most popular software available in the industries. Since first one stores all historical data, maintenance actions, events and ma nufacturer recommendations, second one collects and stores all critical physical parameters (vibration, temperature.) to be monitored in a regular time basis. However, converting these data into useful information is a challenge.

The degradation process of a system may be affected by many unknown factors, such as unidentified fault modes, unmeasured operational conditions, engineering variance, environmental conditions, etc. These unknown factors not only complicate the degradation behaviors of the system, but also make it difficult to collect quality data. Due to lack of knowledge and incomplete measurements, certain important con text information (e.g. fault modes, operational conditions) of the collected data will be missing. Therefore, historical data of the system with a large variety of degradation patterns will be mixed together. With such data, learning a global model for Remaining Useful Life (RUL) prediction becomes extremely hard since the end user does not have enough and good-quality data to model properly the system.

This has led us to look for advanced RUL prediction techniques beyond the traditional RUL prediction models. The degradation process for many engineering systems, especially mechanical systems, is irreversible unless the condition is recovered by effective maintenance actions. The irreversible degradation process does not necessarily imply that the observed features will exhibit a monotonic progression pattern during degradation. Such progression pattern is sometimes hard to model using parametric methods. Considering a degradation process involving no or limited maintenance, the process may compose of a sequence of irreversible stages (either discrete or continuous) from new to be worn out, which can be implicitly expressed by the trajectory of the measured condition data or features. Therefore, the RUL of the system can be estimated if its future degradation trend can be projected from those historical instances. In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed.

The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. For a test instance of the same system, whose RUL is going to be estimated, degradation speed is evaluated by computing the minimal distance defined based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data at a different time horizon. Therefore, the final RUL of a specific component can be estimated, and global RUL information can then be obtained by aggregating the multiple RUL estimations using a density estimation method.

Proposed model develops an effective RUL prediction method that addresses multiple challenges in complex system prognostics, where many parameters are unknown. Similarities between degradation trajectories can be checked in order to enrich existing methodologies in prognostic's applications. Existing CM data for bearings will be used to verify the model.

In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed. The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. For a test instance of the same system, whose RUL is to be estimated, degradation speed is evaluated by computing the minimal distance defined based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data at different time horizon. Therefore, the final RUL of a specific component can be estimated and global RUL information can then be obtained by aggregating the multiple RUL estimations using a density estimation method.

The occurrence of equipment failures is one of the main causes of inefficiency. These events increase the operational costs and give rise to a loss of revenues or in the worst case they can even produce an accident with significant damages to people and the environment. The efficiency of the operation of an industrial installation in a given period of time has been defined use the ratio of the Dependability level achieved by the installation in a specified period of time and the sum of the corresponding Dependability and Undependability costs.

The aim of this paper is the development of a methodology for the calculation of operating costs in industrial facilities that addresses the difficulty of performing a simple, homogeneous and objective evaluation of the operational efficiency of the industrial facility. The main obstacle to this evaluation is the lack of a method for quantifying Dependability, which to date has always been considered a purely qualitative characteristic.

The measurement of the performance in the maintenance function has produced large sets of indicators that due to their nature and disparity in criteria and objectives have been grouped in different subsets lately, emphasizing the set of financial indicators. The generation of these indicators demands data collection of high reliability that is only made possible through a model of costs adapted to the special casuistry of the maintenance function, characterized by the occultism of these costs.

The system of performance measurement of maintenance function should cover all processes related to it within the organization. There must be an interconnection between the different indicators, so the numbers can be interpreted in order to reach a good conclusion for decision making. This premise implies a hierarchy of indicators needed in a dual way. First, it will require maintenance indicators to be segmented according to the areas of influence for the rest of the organization, posed by interactions with finance department, human resources, purchasing, and, of course, with production in the seeking of compliance with corporate objectives. Simultaneously, these indicators correspond to different levels in the organization and therefore they will be segmented according to the hierarchical position of end users.

The maintenance performance measurement is often faced with a lack in knowledge about the real function of the maintenance department within organizations, and consequently the absence of appropriate targets emanating from the global mission and vision. These facts bring about metrics not adapted to the real needs, which has a strong load of human factor and without a roadmap of the amount of data to be collected, their processing and use in decision making. This article proposes a model where qualitative and quantitative methods are combined in order to complement advantages and disadvantages of them both.

All assets necessarily suffer wear and tear during operation. Prognostics can assess the current health of a system and predict its remaining life based on features capturing 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 but has become an important part of Condition-based Maintenance (CBM) of systems. Broadly stated, prognostic methods are either data-driven, rule based, or model-based. Each approach has advantages and disadvantages; 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 fault state. In this context, it is important to evaluate the consistency and reliability of the measurement data obtained during laboratory testing and the prognostic/diagnostic monitoring of the system under examination.This approach is especially relevant in systems where the maintainer and operator know some of the failure mechanisms with a sufficient amount of data, but the sheer complexity of the assets precludes the development of a complete model-based approach. This paper addresses the process of data aggregation into a contextual awareness hybrid model to get Residual Useful Life (RUL) values within logical confidence intervals so that the life cycle of assets can be managed and optimised.

iMaintenance stands for integrated, intelligent and immediate maintenance. It integrates various maintenance functions and connects these to all devices, using advanced communication technologies. The main challenge is to integrate the disparate systems and capabilities developed under current eMaintenance models and to make them immediately accessible through intelligent computing technologies. iMaintenance systems are computer-based, able to evolve with the system that they monitor and control, and they can be embedded in the system’s components, providing the ability to integrate new functionality with no downtime. This article will show how iMaintenance systems can provide decision-making support, thereby going beyond merely connecting various maintenance systems.

The measurement of the performance in the maintenance function has produced large sets of indicators that due to their nature and disparity in criteria and objectives have been grouped in different subsets lately, emphasizing the set of financial indicators. To generate these indicators properly is necessary to have accurate input data. Hence in this paper we propose a comprehensive model of consensus between the different stakeholders involved in the maintenance function. This will bring about the accurate determination of the maintenance costs of an organization.

It is reported that switches and crossings (S&C) are one of the subsystems that cause the most delays on Swedish Railways while accounting for at least 13% of maintenance costs [6]. It is the main reason why we chose to base our study on this subsystem.Intelligent data processing allows understanding the real reliability characteristics of the assets to be maintained. The first objective of this research is to determine the S&C reliability characteristics based on field data collection. Because field failure data are typically strongly censored, an especial statistics software package was developed to process field failure data, as commercial packages have not been found satisfactory in that respect. The resulting software, named RDAT® (Reliability Data Analysis Tool) has been relied upon for this study: it is especially adapted to statistical failure data analysis.In the next step the availability of studied switches and crossings is estimated based on the reliability characteristics founded in the first step.

There is increasing interest in the use of maintenance performance measurement (MPM) and the possibility of using the maintenance audits for benchmarking metrics. This article proposes a methodology for simple measurement, one that accepts the indicators used on a scorecard with four perspectives and is hierarchized according to organizational level. The maintenance audit will evaluate the degree of fulfillment of objectives and the degree of satisfaction obtained from each of those perspectives. It will provide a clear picture of the current status of maintenance organization and the success of implemented policies taking into account the maintenance maturity model, i.e, the logical evolution of the maintenance function in the company.

This paper provides an overview of research and developments in the measurement of maintenance performance. It considers the problems of various measuring parameters and comments on the lack of structure in, and references for, the measurement of maintenance performance.

The main focus is to determine how value can be created for organizations by measuring maintenance performance, looking at such maintenance strategies as condition based maintenance, reliability centered maintenance, e-maintenance etc. In other words, the objectives are to find frameworks or models that can be used to evaluate different maintenance strategies and determine the value of these frameworks for an organization.

The paper asks the following research questions:

- What approaches and techniques are used for Maintenance Performance Measurement (MPM) and which MPM techniques are optimal for evaluating maintenance strategies?

- In general, how can MPM create value for organizations, and more specifically, which system of measurement is best for which maintenance strategy?

The body of knowledge on maintenance performance is both quantitative and qualitative based. Quantitative approaches include economic and technical ratios, value-based and balanced scorecards, system audits, composite formulations, and statistical and partial maintenance productivity indices. Qualitative approaches include human factors, amongst others. Qualitative-based approaches are adopted because of the inherent limitations of effectively measuring a complex function such as maintenance through quantitative models. Maintenance decision makers often come to the best conclusion using heuristics, backed up by qualitative assessment, supported by quantitative measures. Both maintenance performance perspectives are included in this overview.

Diagnosis and prognosis processes are necessary to optimise the dependability of systems and ensure their safe operation. If there is a lack of information, faulty conditions cannot be identified and undesired events cannot be predicted. It is essential to predict such events and mitigate risks, but this is difficult in complex systems.Abnormal or unknown faults cause problems for maintenance decision makers. We therefore propose a methodology that fuses data-driven and model-based approaches. Real data acquired from a real system and synthetic data generated from a physical model can be used together to perform diagnosis and prognosis.As systems have time-varying conditions related to both the operating condi- tions and the healthy or faulty state of systems, the idea behind the proposed methodology is to generate synthetic data in the whole range of conditions in which a system can work. Thus, data related to the context in which the system is operating can be generated.We also take a first step towards implementing this methodology in the field of rolling element bearings. Synthetic data are generated using a physical model that reproduces the dynamics of these machine elements. Condition indicators such as root mean square, kurtosis and shape factor, among others, are calculated from the vibrational response of a bearing and merged with the real features obtained from the data collected from the functioning systemFinally, the merged indicators are used to train SVM classifiers (support vector machines), so that a classification according to the condition of the bearing is made independently of the applied loading conditions even though some of the scenarios have not yet occurred.

Rolling element bearings have been studied for decades, but more research is required into their dynamics, especially failure due to different kinds of damage in the context of condition monitoring. The appearance of a failure in an element of a bearing, as well as its degradation, can entail not only a malfunction in the system in which it is located, but also a catastrophic failure. This work presents a multi-body model of a rolling element bearing with the objective of analysing the dynamics of the bearing and emphasising the effect of defects in any of its element. The study models the metal-metal contacts between the bearing’s elements using the Hertz contact and the elastohydrodynamic lubricationtheories, both of which are theories of nonlinearity. It also considers the non-stationary regime of bearings and local geometric damage. Its results are compared with results in the literature. Finally, it includes a set of additional results showing different aspects of the response of the bearing.

As rolling element bearings are key parts of rotating machinery, the estimation of their reliability is very important. In this context, different standards and research articles propose how to estimate fatigue life for different levels of reliability. However, when trying to do calculations based on data from a real system, there are many difficulties because of economic and safety reasons. Consequently, the use of physical models to simulate the cases that are difficult to reproduce in a real system allows us to generate synthetic data related to them. Thus, in this paper a synthetic failure rate of rolling element bearings is calculated using a physical modelling approach. A multi-body model of a bearing is used in order to obtain its dynamic response in non-stationary conditions and in different degradation levels. Thus, synthetic data are generated to cover a range of degradation related to geometric changes in the surface of the parts of the bearing. Some of the output variables of these synthetic data, such as vibration, are used as covariates of a proportional hazard model, which is then trained to estimate the reliability of the bearing. In this way, a synthetic failure rate is obtained in such a way that it can improve the failure rate given by the manufacturers.

The estimation of the life of rolling element bearings (REBs) is crucial to determine when maintenance is required. This paper presents a methodology to calculate the fatigue life of REBs considering non-stationary conditions. Instead of taking a constant value, the paper considers cyclic loading and unloading processes, as well as increasing and decreasing values of the speed of rotation. It employs a model-based approach to calculate contact loads between the different elements of the bearing, with a finite element model (FEM) used to calculate the contact stresses. Using this information, it then performs a fatigue analysis to study overloading in faulty bearings.

Using condition-based maintenance (CBM) to assess machinery health is a popular technique in many industries, especially those using rotating machines. CBM is relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase both profit and safety. Prognosis is the most critical part of this process and the estimation of Remaining Useful Life (RUL) is essential once failure is identified. This paper presents a method of synthetic data generation for hybrid model-based prognosis. In this approach, physical and data-driven models are combined to relate process features to damage accumulation in time-varying service equipment. It uses parametric models and observer-based approaches to Fault Detection and Identification (FDI). A nominal set of parameters is chosen for the simulated system, and a sensitivity analysis is performed using a general-purpose simulation package. Synthetic data sets are then generated to compensate for information missing in the acquired data sets. Information fusion techniques areproposed to merge real and synthetic data to create training data sets which reproduce all identified failure modes, even those that do not occur in the asset, such as Reliability Centered Maintenance (RCM), Failure Mode and Effect Analysis(FMEA). This new technology can lead to better prediction of remaining useful life of rotating machinery and minimizing and mitigating the costly effects of unplanned maintenance actions.

Safety in transport is a key. Railway and aerospace sectors have a need for ways to predict the behaviour of trains and aircraft, respectively. With this information, maintenance tasks for the correct operation of the assets can be carried out, reducing the number of failures that can cause an accident. However, the lack of enough data of the faulty state of those systems makes this to be difficult. Because of that either hidden faults or unknown faults can occur. As regulations in transport are very restrictive, components are usually substituted in early states of their degradation, which implies a loss of useful life of those components.In this article a methodology to overcome this limitation is presented. This methodology consists in the fusion of data obtained from two sources: data acquired from the real system, and synthetic data generated using physical models of the system. These physical models should be constructed in such a way that they can reproduce the main failure modes that can occur in the modelled system. This data fusion, that creates a hybrid model, not only allows to classify the condition of the system according to the aforementioned failure modes, but also to define new data that do not belong to any of those failure modes as a new failure mode, improving diagnosis and prognosis processes.

Signalling systems in railway allow the control, supervision and protection of railway traffic. These systems play an important part in a railway’s capacity and availability. Thus, their reliability and maintenance are key concerns. A number of signalling systems are on the market today; these work to guarantee safety while meeting the required capacity of the network. In order to keep the railway network in an optimal state, it is critical for the signalling systems to have tools that can make data mining and analysis easier and faster. The solution described herein allows data mining and posterior analysis without depending on the elements that provide the data. This is a key factor for signalling systems, due to their complexity and continuous development. For integration purposes, a data collection and distribution system based on the concept of cloud computing is proposed to collect data or information pertaining to the assets of the railway signalling systems. From a maintenance point of view, a benefit is that information or data may be collected pertaining to the health, variability, performance or utilization of an asset.

The railway network composed by different systems, such assignalling, power supply, rolling stock and others. The signalling system is used for control, supervision and protection of railway traffic. Its reliability, maintainability and related maintenance support affect the availability of the railway network. Often,signalling system itself is considered as a system-of-systems spread over a wide geographical area and consists of a large number of items with different lifecycle. However, managing a complex technical system with large number of items in different lifecycle phases to achieve required availability is challenging. One major challenge is to define and describe the structure and relation between the system’s inherent items at any specific period of time, i.e. the configuration of the system. The system configuration is important during the design and manufacturing of systems, but it is also essential during the utilisation and retirement of a system. Furthermore, system configuration can be considered as an essential data container to which other data sources can be related, e.g. design data, reliability data, maintainability data, operation data, and maintenance data. Aproper management of configuration data is highly important during a system’s whole lifecycle, e.g. the railway signalling system. Hence, the purpose of this paper is investigate how the process of configuration management related to railway signalling system can be improved through utilisation of Capability Maturity Model Integrated (CMMI).

Several studies in Sweden have looked into railway electromagnetic interference (EMI) either to discover the source of the interference or to determine if the equipment in the system is performing properly. The movement of rolling stock along an electrified track produces certain EMI events. Transient electromagnetic fields are produced in the signalling system when the train leaves the neutral section of the overhead power line and enters the powered section. These transient EM fields are mainly produced by the engine. The track’s infrastructure system has been tested for EMI events, but this phenomenon affects the surrounding environment as well, up to at least 10 meters from the track. The infrastructure is designed so that the return current from locomotives should go through the running rails, but occasionally the ground acts as a conductor, transmitting current to areas that are distant from the rail. The paper reviews the status of Swedish railways with respect to electromagnetic compatibility. This TREND project is a joint project with 7 FP EU.

Many railway assets, such as wheels, suffer from increasing deterioration during operation. Good condition monitoring based on good decision-making techniques can lead to accurate assessment of the current health of the wheels. This, in turn, will improve safety, facilitate maintenance planning and scheduling, and reduce maintenance costs and down-time. In this paper, wheel/rail forces are selected as a parameter (feature) for the condition monitoring of wheel health. Once wheels are properly thresholded, determining their condition can help operators to define maintenance limits for their rolling stock. In addition, if rail forces are used as condition indicators of wheel wear, it is possible to use measurement stations that cost less than ordinary profile stations. These stations are located on ordinary tracks and can provide the condition of wheelsets without causing shutdowns or slowdowns of the railway system and without interfering with railway traffic. The paper uses the iron-ore transport line in northern Sweden as a test scenario to validate the use of wheel/rail forces as indicators of wagon and wheel health. The iron-ore transport line has several monitoring systems, but in this paper only two of these systems will be used. Wheel/rail force measurements are performed on curves to see how the vehicle negotiates the curve, and wheel profile measurements are done on tangent track not far away. The vehicles investigated are iron-ore wagons with an axle load of 30 tonnes and a loaded top speed of 60 km/h. The measurements are non-intrusive, since trains are moving and assets are not damaged during the testing process

Purpose:The purpose of this paper is to provide a literature review of the performance measurement in maintenance. The authors aim to discuss the background and development of the performance measurement for maintenance, besides defining the concept of performance measures for maintenance and the frameworks developed.Design/methodology/approach:A detailed and extensive literature search and study was undertaken by the authors on the concept and definition of performance measurement, performance indicators, maintenance performance indicators and various performance frameworks. The history and theory of performance measurement over different phases of business and technological developments have been critically examined and analysed in this review paper. Findings:This paper reviews and presents the different performance indicators (PIs) and performance measurement (PM) frameworks like; balanced scorecard, performance prism, performance pyramid and performance matrix etc, and identifies their characteristics and shortcomings. After considering related issues and challenges, frameworks and approaches for the maintenance performance measurement (MPM) are also presented, where the emerging techniques like; e-maintenance have also been discussed amongst others. More and more industries are applying the balanced and integrated MPM frameworks for their competitive survivability and sustainability.Practical implications:The concept, issues and approaches considered for the MPM frameworks can be adapted by the practicing managers, while trying to define and develop an MPM framework for the operation and maintenance activities. The considerations of the advantages and limitations of different frameworks can provide insights to the managers for implementation. Originality/value:Some literature reviews on MPM and MPM frameworks are available today. This paper makes an attempt to provide a detailed and relevant literature review, besides adding value in this new and emerging area.

The objective of the present work is to carry out an analysis of the performance and life of bearings used in railways transporting ore. The project determined the nominal life (L10) for a single type of bearing commonly used in railways (SKF CTBU Class K and Timken AP-2TM). To do so, it considered two methodologies. Methodology I is an alternative approach proposed by a few authors in Brazil, inspired by a procedure used to design rail shafts. Methodology II is based on well-established techniques proposed by the bearing manufacturers (as SKF and Timken).After of have estimated L10, is determined the bearing lifetime (L) using two correction parameters (one based on reliability (a1 ) and the other based on the working conditions of the bearing (aISO). This in order to determine whether it was being replaced at the appropriate time, to improve its replacement periods, decrease maintenance costs, make the most of the life of the bearing and detect possible causes of faults it may present at any given time.In addition, it discusses some Condition Monitoring Techniques that can help identify anomalies, faults and breakdowns in these bearings. It touches on the phenomenon of electric current through wheel bearings, the way to identify, detect and prevent it, and some measurement techniques. Finally, it explains in detail the bearings process reconditioning (mounting, dismounting and reconditioning) that accomplished some companies in order to maximize the life of these.

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.

La complejidad del sistema ferroviario aumenta cuando se utiliza más electrónica. Cuando se introducen nuevos trenes equipados con componentes electrónicos importantes en una infraestructura ferroviaria que no ha sido renovada en los últimos años, como la sueca, puede surgir uno de los problemas más relevantes de ferrocarril: el sistema de parada de emergencia activa los frenos del material rodante, causando largos retrasos con efectos en cascada. La causa es que el TCC (centro de control de trenes) puede detectar señales inesperadas debido a los campos electromagnéticos transitorios que puedan interferir en los circuitos de señalización y control.

Railway traffic has increased over the last decade due to greater energy costs and the need to reduce emissions. Ensuring the dependability and capacity of railway infrastructure requires efficient and effective maintenance which, in turn, requires good understanding of various physical behaviours, e.g. deterioration and environmental effects. This paper studies the effect of cold climate on railway infrastructure performance using statistics and historical work order data. It finds differences in the number of work orders as a function of season and geographical location.

Railway traffic has increased over the last decade and it is believed to increase further with the movement of transportation from road to rail, due to the increasing energy costs and demand to reduce emissions. As a result of increasing need of railway capacity, more efficient and effective operation and maintenance is required. To manage the assets effectively within the business objectives, the results of operation and maintenance activities must be measured and monitored.

Performance indicators are developed to support infrastructure managers in decision making, but they are often ad hoc and seldom standardised. In this paper, performance indicators for railway infrastructure, with primary focus on the railway track, have been mapped and compared with indicators of European Standards. The listed indicators can be applied to form a performance measurement system for railway infrastructure.Keywords: railway infrastructure, maintenance, operation, management, performance measurement, indicator.

Railway traffic has increased over the last decade due to its fuel efficiency and the need to reduce emissions. The railway infrastructure performance needs to be measured to allow assets to be managed effectively against set objectives. Various systems are used to collect and store data on traffic, failures, inspections, track quality, etc. However, these systems are often used in an ad hoc manner, partly because of the weaknesses of traditional performance measurement systems. This paper proposes a link and effect model which is focused on the areas of continuous improvement, the key elements of strategic planning and on the underlying factors responsible for the railway performance. The model provides information on the performance of railway systems and components, and how they are linked to each other and to the overall objectives, thereby facilitating proactive decision-making. The model is applied in a case study on the Iron Ore Line, Sweden. The performance of a section of the line is studied in terms of failures, train delays and repair times, and ranked through a risk matrix and composite indicator.

Value driven maintenance (VDM) is a fairly new maintenance management method based on four maintenance value drivers and to calculate the discounted present value (DPV) of the maintenance strategy. However, the dependability of the engineering assets needs to be assessed in order to make an estimation of the DPV. Therefore, the European standard EN 15341 has been studied, in order to find the most essential indicators for the four value drivers and for estimation of the DPV. Terminology containing drivers and killers are common in the field of asset management, but definitions are scarce. One section in this paper is therefore dedicated to review these terms.

Purpose – Value driven maintenance (VDM) is a fairly new maintenance management methodology based on four maintenance value drivers and the formula of net present value (NPV) to calculate the value of different maintenance strategies. However, the dependability of the engineering assets needs to be assessed in order to make an estimation of the NPV. Therefore, standardised indicators have been critically analysed to find the most essential indicators for the four value drivers and for estimation of the NPV. Terminology containing performance drivers and killers are common in the field of asset management, but not many publications can be found for their detailed descriptions. One section in this paper is therefore dedicated to review these terms. A comprehensive description and classification of performance killers and drivers, and of indicators for VDM are presented in this paper.

Design/methodology/approach – Review of literature for technical terminology and review of standards for identification of indicators for maintenance performance measurement and NPV of maintenance.

Findings – Common description of technical terminology as used by researchers and identification of the most important indicators for maintenance performance measurement and the NPV of maintenance. Indicators classified under economic, technical, organizational and HSE perspectives from EN 15341 standards are discussed and identified.

Value – Description of emerging terminology in maintenance performance measurement adds to the consistency in communication of researchers and business stakeholders. Also, the identified maintenance performance indicators can facilitate performance measurement of organisations new to the process of measuring and analysing their performance.

Prognosis can be defined as the course of predicting a failure of equipment or a component in advance, whereas prognostication refers to the act of prediction. The three main branches of condition based maintenance are diagnosis, prognosis, and treatment-prognosis, however prognosis is admittedly the most difficult. Also, this area has been the least described in literature and the knowledge about it in a maintenance management context is still poorly systematized.

To this day, formal professional attention to prognosis, in the field of maintenance management and engineering in the everyday care of machinery, is often relegated to a secondary status although the availability of prognostic information can considerably improve (e.g. reduce costs, maximize uptime) the performance of machinery and maintenance processes. Ideally, assessment of a prognosis of remaining useful life should be deliberate and explicit. In order to support the maintenance crew in the achievement of this objective an increasing amount of prognostic information is available. Over the last decade, system integration has grown in popularity as it allows organizations to streamline business processes. It is necessary to integrate management data from CMMS (Computer Maintenance Management Systems) with CM (Condition Monitoring) systems and finally SCADA (Supervisory Control And Data Acquisition) and other control systems, widely used in production but with a seldom usage in asset diagnosis and prognosis.

The most obvious obstacle in the integration of these data is the disparate nature of the data types involved, moreover several attempts to remedy this problem have fizzled out. Although there have been many recent efforts to collect and maintain large repositories of these types of data, there have been relatively few studies to identify the ways these datasets could be related and linked for prognosis and maintenance decision making. After identifying what and how to predict incipient failures and developing a corresponding prognosis, maintenance engineers must consider how to communicate the prediction. In this activity once again, technicians' psychosocial attributes and values may influence how they discuss prognoses with asset managers. Regardless of whether prognostic assessments are subjective or objective, however, technicians should consider two major points.

Firstly, the maintenance crew should clarify in their own minds the link, if any, between their prognostic assessment and their consequent decision making. Secondly, they should consider the ways that they and their assets might benefit from explicitly discussing how the prognostic assessment is linked with diagnostics and preventive maintenance recommendations. These and other steps that maintenance engineers should take in incorporating prognostic information into their decision making are discussed in this paper. The objective is to give an overview of how the integration of disparate data sources, commonly available in industry, can be achieved for maintenance prognosis and optimal decision making.

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.

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.