Recent developments in sensor technologies in combination with advances in information and communication technologies has provided with new opportunities to deal with the complex and multidisciplinary area of maintenance in an effective way. In near future, it will be easier to merge data from machine health condition with business information to arrive at the best possible maintenance and business solutions leading to e-Maintenance. This special issue is a small timely step to move in that direction.

This special issue was initiated while organizing the First International Congress and Workshop on e-Maintenance in Lule? during June 22-24, 2010 by the Division of Operation and Maintenance Engineering of Lule? University of Technology, Sweden. Some representative papers befitting the theme of this special issue were selected from the presented papers and with additional inputs from the authors and following a blind review and subsequent revision before final acceptance of papers for this special issue of International Journal of Performability Engineering.

With emergence of intelligent sensors to measure and monitor the health of the engineering assets and gradual implementation of information and communication technologies (ICT) in the organization, conceptualization and development of e-Maintenance has turned into a reality. While e-Maintenance shows a lot of promise, seamless integration of ICT into the industrial environment still remains a challenge. The e-Maintenance concept found its application into engineering asset management as a part of e-Manufacturing and e-Business in the early 21^st century. e-Maintenance is a concept necessary to enable and implement proactive decision making process; supporting e-Technologies; including e-Monitoring, e-Diagnostics, and e-Prognostics.

The first paper, A Generic Approach for Predictive Maintenance Considering Changing Ageing Conditions, deals with the analysis of condition in which data plays an important role to determine the health condition of machines. The paper proposes a sequence of steps which allows the determination of ageing indicators using a generic approach which are used to predict the behaviour of a machine. The current predictive maintenance approaches are often based on economical or statistical failure criteria and not on technical details of the concerned processes.

The second paper, Model based Fault Diagnosis of Rotor Systems, proposes a model based scheme for fault diagnosis of a rotor system. Since the vibration in rotating machinery is mostly caused by unbalance, misalignment, shaft crack, mechanical looseness and other malfunctions, the proposed scheme successfully detects and identifies the type, location and amount of fault in a rotor system for unbalances, misalignments and cracks. The method further demonstrates the efficacy of the model based fault detection system for a simple rotor-bearing system.

In the third paper, Mapping FMEA into Bayesian Networks, the authors use FMEA to detect possible flaws in a system or elements, especially in the design phase. This knowledge can be mapped into a Bayesian Network and this information can be used further, in software applications for maintenance. A good FMEA has the necessary features to build a good Bayesian Network: bottom-up (or top-down) analysis of all the components and subcomponents and cause-failure-effect chains. The Bayesian network has been used for the FMEA of a marine diesel engine, as a diagnostic application that uses a set of on-line lube-oil sensors.

The fourth paper, Maintenance Optimization for Large Coal-fired Power Plants, deals with minimization of the costs of scheduled and unscheduled plant downtimes as well as well as the financial losses involved. The authors have discussed the tools for optimizing maintenance cycles and avoiding downtimes and associated expenses through a software program INSTRA.

The fifth paper, A Simulation Approach to the Optimization of Railway Infrastructure Maintenance Strategies, looks into increased availability of railway infrastructure through diagnostics and preventive maintenance. The authors have presented a Petri net based modeling method for Monte Carlo simulation and validated it with a case study on French high speed line.

The sixth paper of the special issue, Essential Components of e-Maintenance, looks into the common features of the definition of e-Maintenance and the authors bring out the initial set of essential components for e-Maintenance. Besides, the definition of e-Maintenance, its relation in context of other e-Domains; and opportunities and challenges are presented.

In the seventh paper of this issue, e-Maintenance of Railway Asset based on a Reliable Condition Prediction, the authors emphasize that an efficient e-Maintenance concept helps provide a reliable basis for condition diagnosis and prediction. For the managers of railway infrastructure, it is essential to ensure high process efficiency through sustainable economical decisions. For this purpose maintenance is considered as a cost driver for the railway operation.

The eighth Paper, Development of Information System for e-Maintenance within Aerospace Industry, describes an approach for development of information products by linking theories to practical methodologies and tools like Quality Function Deployment in the context of a modern combat aircraft. The paper concluded that information product s for aerospace industry developed through e-Maintenance solutions can provide effective information logistics for internal and external stakeholders involved in the maintenance process.

The ninth paper, Information Logistics as a Guide for Research and Practice of e-Maintenance Operations, proposes a conceptual framework to guide both the practice and the research of e-Maintenance operations. The framework combines an Industrial Value Chain with a Buyer-Consumer value chain, where their intersections are articulated in terms of categories derived from Information Logistics. A brief case study, from a leading European truck-manufacturer, illustrates the proposed conceptual framework in application.

We take this opportunity to sincerely thank reviewers who helped us greatly in selecting, the best papers out of the several and in providing their helpful and timely reviews, which made this issue possible. Last but not the least; we would like to thank Professor Krishna B. Misra, Editor-in-Chief of IJPE for the help and support at all stages in bringing out this issue.

Lastly, we do hope that this special issue of nine papers will be found useful to the researchers and academia and lead to newer innovations and techniques in this important area.

Uday Kumar obtained his B. Tech. from India in the year 1979. After working for 6 years in Indian mining industries, he joined the postgraduate program of Lule? University of Technology, Lule?, Sweden and obtained a Ph.D. degree in field of Reliability and Maintenance in 1990. Afterwards, he worked as a Senior Lecturer and Associate Professor at Lule? University 1990-1996. In 1997, he was appointed as a Professor of Mechanical Engineering (Maintenance) at University of Stavanger, Stavanger, Norway. Presently, he is Professor of Operation and Maintenance Engineering at Lule? University of Technology, Lule?, Sweden. His research interests are equipment maintenance, equipment selection, reliability and maintainability analysis, System analysis, etc. He has published more than 170 papers in International Journals and Conference Proceedings. He is a Regional Editor of International Journal of Performability Engineering and an Editor-in-Chief of IJSAEM.

Email: Uday.Kumar@ltu.se.

Ramin Karim is an Assistant Professor at Lule? University of Technology (LTU). He has a B.Sc. in Computer Science, and both a Licentiate of Engineering and a Ph.D. in Operation & Maintenance Engineering. He has worked within the Information & Communication Technology (ICT) area for 18 years, as architect, project manager, software designer, product owner, and developer. Karim is engaged in research at LTU in the area eMaintenance and has published more than 15 papers. Email: Ramin.Karim@ltu.se

Aditya Parida is an Associate Professor with Lule? University of Technology. He obtained his Ph.D. in Operation and Maintenance Engineering. His area of research is asset and maintenance performance measurement, RCM and eMaintenance. Besides teaching, he is actively involved in supervising the research students and projects. He has 50 publications to his credit, in addition to author of two books, three book chapters and editor of two International congress proceedings. Email: Aditya.Parida@ltu.se

The field of maintenance changed dramatically after the World War II. By 1950's, systems had became more complex and large, and the concern for the uptime of these systems became the priority of the industry and the management. This became the rallying point for the concept of preventive maintenance with the realization that performing regular maintenance and refurbishment could keep equipment operating longer between failures. This came to be known as Periodic Maintenance, or Calendar Based Maintenance or Preventive Maintenance (PM) or planned preventive Maintenance (PPM). The goal was to have most of the equipment be able to operate most of the time until the next scheduled maintenance outage. This approach provided control over the maintenance schedule; however, the systems were still susceptible to failures between maintenance cycles. Opportunistic Maintenance is an extension of planned maintenance and is actually planning of maintenance around opportunity for access to equipment.

Even by 1960's, the concern was mainly confined to the overhauls of equipment done at fixed intervals. But as the cost of maintenance increased the necessity of maintenance planning and control systems was felt. In fact, the amount of capital tied up in fixed assets increased enormously and the necessity of maximizing the life of these assets was felt more acutely.

As the downtime reduced output, it affected the productive capability of physical assets by increasing operating costs and interfering with customer service and by 1970's, this was further aggravated by the worldwide move towards just-in-time systems, where reduced stocks of work-in-progress meant that quite small breakdowns could stop the whole plant. Instead of waiting for a machine to fail before working on it, or performing maintenance on a machine regardless of its condition (PM), the idea of performing maintenance on equipment only when it indicates impending faults – Predictive Maintenance (PdM) – took hold and the idea of using PdM to perform maintenance on machines only when they exhibit signs of mechanical failure had come to be known as Condition Based Maintenance (CBM). This process of maintenance has become more proactive than reactive in their maintenance tasking. Moreover, the cost of maintenance is also rising, in absolute terms and as a proportion of total expenditure. In some industries, it is the highest or the second highest element of operating costs. Consequently, in last thirty years it has moved from almost nowhere to the top of the cost control priority. Also it is becoming apparent that there is less and less connection between the operating age of the assets and how likely they are to fail.

In recent times, there has been tremendous growth in the maintenance concepts and techniques. The change in emphasis includes:

• Decision support tools, such as hazard studies, failure modes and effects analyses and expert systems
• New maintenance techniques, such as condition based monitoring (CBM) or CMMS
• Availability of better sensing and monitoring devices and equipment
• Designing equipment with emphasis on reliability (and safety) and maintainability (or Design out)
• A major shift in organizational thinking towards participation, team-working and flexibility.

It is now possible to improve asset performance and at the same time contain and even reduce the cost of maintenance by choosing the right choices of techniques.

Reliability Centred Maintenance (RCM) provides a framework which enables users to respond to these challenges, quickly and easily. RCM uses failure mode and effect analysis (FMEA) in order to identify significant components and failure modes. This helps in selecting the appropriate maintenance action for preventing such future failures.

Total Productive Maintenance (TPM) and Total Quality Maintenance (TQM) are the means to maintain and continuously improve the technical and economical effectiveness of production process. The TPM optimizes the effectiveness of production means in a structured manner.

Maintenance Information and Control Systems started with charts and indexes. For large assets, the computerized models were developed for managing and controlling large inventories. Early computerized systems tended to concentrate on job scheduling, resource management and inventories. But CBM has now developed more sophisticated tools such as vibration analysis etc. The standardization of maintenance data was significantly advanced by MIMOSA (Machinery Information Management Open System Alliance) which is ISO 13374-1 compliant. Computer-based systems starting from the perspective of enterprise resource planning started coming in vogue.

Computerized Maintenance Management System (CMMS), which is also known as Enterprise Asset Management (EAM) is a stand alone computer program to schedule and manage the entire maintenance related activities.

Fast and reliable internet and mobile networks have opened up boundless opportunities for system information integration. CBM uses a wide range of devices and equipment to monitor the health of the machines and processes. For predictive maintenance (PdM), which helps determine the condition of in-service equipment in order to predict when maintenance should be performed, any relevant means is acceptable to determine equipment condition, and to predict potential failure, which may even include the use of the human senses (appearance, sound, feel, smell etc.), machine performance monitoring, and statistical process control techniques. But the other sophisticated technologies used in monitoring include:

• Vibration Level Measurement and Analysis
• Corrosion Monitoring
• Frequency Spectra (FFT Analysis)
• Acoustic Emission (Ultrasonics)
• High Frequency Emissions
• Infrared Thermography
• Lubricant or Oil Analysis (Wear Debris)
• Leakage Detection
• Motor Current Analysis
• Crack Detection

The major trends today are:

• The development of smart sensors, and other low-cost on-line monitoring systems that will permit the cost-effective continuous monitoring of important equipment
• Increasingly sophisticated condition monitoring software, with rapidly developing expert diagnosis capabilities
• Increasing integration, and acceptance for interfacing condition monitoring software with CMMS and Process Control software
• A reduction in the cost of applying condition monitoring technologies.

Today, wireless sensors, MEMS, with reduction size and power requirement the devices, have further revolutionized the system of monitoring and recording and analyzing of maintenance data. Miniaturization of transducers for the measurement of pressure, strain, acceleration, angular displacement, force, flow temperature etc. has revolutionized the practice. Better and better diagnostic, monitoring, signal analysis and prognostics approaches are available. Expert systems have been developed for carrying out maintenance related activities and decisions. The area of PHM (Prognostics and Health Management) is assuming importance and is becoming indispensable. We had a special issue exclusively devoted to PHM in this journal’s (IJPE) September 2010 issue. All this is making the task of e-Maintenance feasible and real. In the present issue we explore the current thinking in the area of maintenance leading to e-maintenance. It is hoped the readers would find the papers included in the current issue rewarding in familiarizing and exhorting to further research.

Of recent, emphasis is being placed on core business by several major companies of the world and under this thinking; companies are transferring previously in-house functions to external specialist companies. Use of experts or specialists and reasons for choosing in-house teams on-site accommodated teams or manned-up technicians brought from outside for the completion of a job, is becoming routinely common these days. Subcontracting for maintenance job is also becoming quite common in the globalizing world of trade these days and the number and size of companies engaged in maintenance work is growing very fast.

I take the opportunity to thank the Guest Editors and reviewers, who helped bringing out this issue and lastly thanks are due to the authors who contributed to this special issue and cooperated in maintaining timeliness and standards of presentations.

In the area of e-Maintenance the analysis of condition data plays an important role in order to determine the health condition of machines. The results of such an analysis are used to decide whether maintenance actions for a machine have to be scheduled or not. Predictive Maintenance is the next step as it offers the possibility to prognosticate the remaining time until a maintenance action of a machine has to be scheduled. Unfortunately, current solutions are only suitable for very specific use cases like reliability predictions based on vibration monitoring. Furthermore, they do not consider the fact that a machine may deteriorate non-uniformly, depending on external impacts (e.g., the work piece material in a milling machine, the changing fruit acid concentration in a bottling plant). These two problems are addressed in this paper. Therefore, concepts for a generic determination of reliability indicators and the handling of aging variability are presented.

A continuing task in engineering is to increase the reliability, availability and safety of technical processes and to achieve these fault diagnosis becomes an advanced supervision tool in the present industries. Vibration in rotating machinery is mostly caused by unbalance, misalignment, shaft crack, mechanical looseness and other malfunctions. The objective of this paper is to propose a model based scheme for fault diagnosis of a rotor system. Presence of faults changes the dynamic behaviour of the system which is taken into account by equivalent loads acting on the healthy system model. In order to diagnose the faults in a rotor system the experimental time responses for healthy system as well as for faulty system were used. It was observed that the proposed scheme successfully detects and identifies the type, location and amount of fault in a rotor system for unbalance, misalignment and crack. This method has thus demonstrated the efficacy of the model based fault detection system for a simple rotor-bearing system.

FMEA is a useful tool that helps to find possible flaws in a system or element, mainly in the design phase. But it is a mere document that has no further 'direct' use than the observation. Mapping all this knowledge into a Bayesian Network would make it possible to use the information in further ways, like software applications for maintenance. A good FMEA has the necessary features to build a good Bayesian Network: Bottom-up (or Top-Down) analysis of all the components and subcomponents and cause-failure-effect chains. In this paper we will detail the steps followed to create the Bayesian Network, using the FMEA of a Marine Diesel Engine, as well as the use of it in a diagnosis application that uses a set of on-line lube-oil sensors.

Since power is not a product suitable for storage, it must be consumed at the moment of generation or go to waste. The goal, therefore, is to minimise the costs of scheduled and unscheduled plant downtimes as well as the financial losses involved. A tool for optimizing maintenance cycles and avoiding the above-mentioned downtimes and expenses is the software program INSTRA. With some preparatory work, INSTRA will optimize maintenance cycles and create a database of the maintenance jobs to be performed. Additionally, INSTRA can be constantly modified to serve as a basis for decision making regarding maintenance optimization as new technologies in the power plant sector (e.g., lignite drying, CCS technologies) are developed. The simultaneous sampling and implementation of new unit-specific data will ensure that the software tool remains as realistic as possible.

In recent years, new diagnosis systems for railway infrastructure have been developed and launched into market. Their major objective is to increase the availability of railway infrastructure and its external systems by means of preventive maintenance, anticipating malfunctions and failures. The acquisition and installation of such systems comprises the investment of large amounts of money. In order to predict the potential benefit, it would be meaningful to model the current infrastructure maintenance process and simulate the effects of the introduction of modifications, e.g., a diagnosis system. In this work a Petri net based modeling method for Monte Carlo simulation is presented, and validated by means of a case study of a French high speed line.

Many intellectual, societal, business and technological forces are continuously pushing forward the frontiers of science. When combined, they provide an umbrella for generating new fields and exploring new grounds. One such a new field is e-Maintenance. e-Maintenance addresses new needs and provides various benefits in form of increased availability, reduced lifecycle cost and increased customer value. However, it suffers from lack of a commonly defined basis supporting the existence of e-Maintenance and determining the essential components inherent in the e-Maintenance domain. In this paper, we suggest an initial set of components that serve as the groundwork of the e-Maintenance universe. The set outlines ten initial components. These are definition, business, organization, product, service, methodology, technology, information, customer, and education and training. The paper also suggests a definition of e-Maintenance, places e-Maintenance in the context of other e-Domains, and elicits e-Maintenance intellectual opportunities and challenges to be met by both the academia and industry.

To ensure a strong position within the transport market sustainable economical decisions and the implementation of new methods and techniques for achieving higher process efficiency are necessary for the railway infrastructure managers. Starting from a primarily time scheduled maintenance an improvement of the rail infrastructure asset maintenance can contribute to this aim, especially when considering that maintenance is a cost driver in the operation of railway systems. e-Maintenance for railway infrastructure is such a concept. Implemented in a reliable way it gives the infrastructure managers a better idea about the asset condition, reduces the number of maintenance activities to be taken manually and therefore the risk for maintenance workers in the track bed. Mandatory for an efficient e-Maintenance concept is a reliable condition diagnosis and prediction.

Maintenance and support providers to modern aircraft need to manage an increasing amount of information generated by growing system complexity and stakeholder requirements. This introduces new risks in the information management process and makes traditional information services and systems inadequate. However, recent advancements in information and communication technology (ICT) have contributed to the emerging approach of e-Maintenance, which forms an important building block to achieve the desired information logistics. e-Maintenance enables remote and real time maintenance, and includes; collection, monitoring, analysis and distribution of data and information as decision-support to stakeholders of the maintenance and support processes, independent of organization or geographical location, 24 hours a day and 7 days a week (24/7). This paper describes a proposed development of information products by linking theories to practical methodologies and tools (e.g., Quality Function Deployment, QFD) through the development of a demonstrator of a stakeholder-based information product in the context of a modern combat aircraft.

Although the development of e-maintenance operations is understood to offer promising opportunities, it seems to be mainly driven by Information and Communication Technologies (ICT) applications development. This is unfortunate, as ICT has no value in itself; rather its benefit comes from the way in which it is utilized within its particular context. Thus, a conceptual framework is proposed to guide both the practice and the research of e-maintenance operations. The framework combines an Industrial Value Chain with a Buyer-Consumer Value Chain, where their intersections articulated in terms of categories derived from Information Logistics. This provides a structure for the conception of e-maintenance that needs to be populated with published research and current e-maintenance practice. This may uncover white spaces where research efforts deserve particular attention and are driven by value generation – for instance, economic. A brief case study, from a leading European truck-manufacturer, illustrates the proposed conceptual framework in application.

Many practical systems are phased-mission systems (PMS) where the mission consists of multiple, consecutive, and non-overlapping phases. An accurate reliability analysis of a PMS must consider the statistical dependencies of component states across phases as well as dynamics in system configuration, success criteria and component behavior. In this paper, we propose an efficient method for exact reliability evaluation of k-out-of-n systems with identical components subject to phased-mission requirements where the k values and failure time distributions can change with the phases. We also consider the time-varying and phase-dependent failure rates and associated cumulative damage effects. The proposed method is based on conditional probabilities and an efficient recursive formula to compute these probabilities. The main advantage of this method is that both its computational time and memory requirements are linear in terms of the system size.