The PAS55 is the standard, specified by the Institute of Assets Management (IAM) for the purpose of Assets Management, and it states the assets could be financial, physical, human, information etc., which have a distinct value to an organization. Here, our concern is to the physical assets, which include plants, buildings, machinery, equipment, vehicles or other items etc. Asset Management is the management of physical assets (their selection, maintenance, inspection, renewal or disposal) and plays a key role in determining the operational performance and profitability of industries that operate assets as part of their core business. Asset Management is an art and science of making the right decisions and optimising this process with an overall objective of minimising the whole life cost of assets but there can be other critical factors such as risk or business continuity in such a decision making. This necessitates a cross-disciplinary collaboration to achieve best net, sustained value-for-money in the selection, design/ acquisition, operations, maintenance and renewal/ disposal of physical infrastructure and equipment.

When a suggestion was made by the Guest editors of this special issue who have worked with the General Electric and other international concerns, it just seemed to be the right choice of the subject on which IJPE could bring out a special issue for the benefit of its readers. The result is before them to see. However our concern here was restricted to RAM’s activities that influence such management practices. The response to the call of papers to this special issue was overwhelming and we did receive as many as 16 papers and. out of these papers, eventually, eight papers were finally selected by the Guest Editors for the special issue after a long and competent review process. It is hoped that these would act as a catalyst for further interest and research in the area and more papers would be submitted in future to IJPE in this area.

The eight papers included in this special issue are from different areas of application, for example there is a paper on clustering of wind turbines for predicting the power output and for developing an optimal operational and maintenance strategy in an integrated manner. There is another paper from heavy heavy duty gas turbine operations, where the work reported in the paper is expected to help in the development of more sophisticated life prediction models for the gas turbine components for high performance. There is yet another paper on the reciprocating compressor, where optimization of opportunistic maintenance has been considered to minimize the life cycle costs.

We have another application from the mining equipment area, in which the effect of extreme environment is considered and maintenance strategy to counter the adverse effect of this environment under which the mining equipment works is presented to improve its overall performance. We have yet another paper from military aviation where the optimal maintenance strategy has been considered to improve the performance of the system.

There is a paper which presents the maintenance decision-making process for the case of a multi-component production unit based on output-based maintenance (OBM) technique. The OBM applies condition based maintenance (CBM) approach and uses machine output measure as the main monitoring parameter for maintenance decision making. Another paper presents the case study from machine tool area in which the CNC grinding machine operation and maintenance data is used to optimize machine tool configuration based on LCC, availability and overall equipment effectiveness. The last paper of this special issue provides a simple yet effective method for estimating the expected number of failures for large arrays of repairable units from an operational aircraft fleet. This is economically achieved and when more data is available, precision can be improved.

Thus the Guest Editors’ selection of papers has attempted to cover a wide range of applications and present case studies which make the Assets Management issue richer in contents.

It is sincerely hoped that this issue will generate further interest among our readers, who are looking for new approaches and new applications in the field of Assets Management.

Lastly, I would like to record my deep appreciation and sincere thanks to all the Guest Editors who laboriously worked to make this special issue possible. Particularly, the coordination task by Dr, B.K. Lad is highly appreciated. Thanks are also due to all the authors who contributed to this special issue and cooperated in maintaining the time schedule.

In a world of ever-increasing technological complexity, it is imperative that users develop means to effectively maintain their equipment to keep them operational and to get the best performance out of them. Fortunately, with increased computational abilities and developments in analysis and modelling techniques, there are pathways to migrate through the minefield of reliability/maintenance issues. This special issue addresses computational techniques on RAM and Asset Management and industrial best practices to assist in developing optimal uptime for equipment and cost effective strategies for maintaining it. Articles in this special issue cover novel approaches, practical applications and demonstrated case studies from wind turbines, gas turbines, heavy duty compressors, aviation and mining industry and machine tools/production machines. It is expected that this issue will further encourage the development and application of RAM and Asset Management techniques for wide range of industrial problems.

The first paper, Clustering Analysis to Improve the Reliability and Maintainability of Wind Turbines with Self-Organizing Map Neural Network, by Zafar Hameed, and Kesheng Wang, presents an efficient approach to develop an integrated operational and maintenance strategy to enhance the reliability and availability of the wind turbine. The approach is based on clustering of wind turbines in a wind farm using the behavioural similarities of different wind turbines. The paper presents a case study, where it has been demonstrated how the information obtained from the clustering analysis can be used for predicting the power output and in developing the optimal operational and maintenance strategy for a group of wind turbines.

The second paper, Diagnostics and Damage Prediction Model for Heavy Duty Gas Turbine Combustor Hardware Failure, by Seema Chopra, and Anurag Agarwal, presents a method for damage prediction using liner bulging data. The approach is especially very helpful in dealing with the situations of insufficient data at a given exposure level for developing damage prediction models.

The third paper, Opportunistic Actions for Subassemblies of a Reciprocating Compressor: An LCC Based Approach, by Mohammad Asjad, Satish Mohite, Makarand S. Kulkarni, and O.P. Gandhi, presents a Life Cycle Cost (LCC) based approach for opportunistic maintenance of a reciprocating compressor. The authors have jointly carried out the research with a leading compressor manufacturing company in India and reported significant saving in Life Cycle Cost (LCC) while ensuring the required level of operational availability.

The fourth paper, Reliability Analysis of Mining Equipment Considering Operational Environments- A Case Study, by Simon Furuly, Abbas Barabadi, and Javad Barabady, presents a case study of application of a Proportional Hazard Model (PHM) in order to quantify the effects of climate conditions on the hazard rate of the Stacker belt in The Svea coal mine – in Svalbard, Norway. The result of the study shows that the hazard rate of the Stacker belt in winter can be four times more than the rest of the year. This is an important finding for maintenance and spare parts planning of the coal mine.

The fifth paper, Reliability based Methodologies for Optimal Maintenance Policies in Military Aviation, by Nomesh Bolia, and R.N. Rai, deals with an important problem in Military aviation of identifying High Failure Rate Components (HFRC) to shortlist them for reliability improvement and subjecting them to reviewed maintenance actions.

The sixth paper, Maintenance Decision-Making Process for a Multi-Component Production Unit using Output-Based Maintenance (OBM) Technique – A Case Study for Non-Repairable Two Serial Components Unit, is written by Rosmaini Ahmad and Shahrul Kamaruddin. The article describes how machine output measures can be used to for maintenance decision making. It uses a “rule-based” decision tree approach for maintenance decision making which makes the entire process of decision making easy to understand and interpret. The authors have demonstrated the applicability of the proposed decision algorithm in making maintenance decisions to real industry case of production machinery.

The seventh paper, Reliability and Maintenance Based Design of Machine Tools, by Bhupesh K. Lad and Makarad S. Kulkarni, presents a novel approach for selection of optimal machine tool configuration by simultaneous consideration of reliability and maintenance. It is a user oriented approach that helps the manufacturer in providing a customized solution (system configuration and maintenance schedule) to the users. Such methodologies will be helpful particularly in dealing with situations where customers make manufacturers more responsible, for the cost of failures incurred by them throughout the life of the system, by getting into long-term maintenance contract.

The last paper, Fleet-Level Reliability Analysis of Repairable Units: A Non-Parametric Approach using the Mean Cumulative Function, by Jan Block, Alireza Ahmadi, Tommy Tyrberg, and Uday Kumar, describes a simple methodology for estimating the expected number of failures of repairable units, particularly during the latter part of the life cycle of the system concerned. This is a highly relevant and very important approach for military aircraft, whose planning horizon is longer than that of commercial aircraft and for which very long intervals between aircraft generations mean that spares and maintenance may become difficult and expensive to obtain towards the end of the system’s life. The proposed reliability analysis method is applied on field data gathered during the operational life of the Swedish military aircraft system FPL 37 Viggen from 1977 to 2006. The research was financially supported by the Swedish National Aeronautics Research Programme, through the NFFP5 project, Enhanced Life Cycle Assessment for Performance-based Logistics.

We would like to congratulate the authors for contributing to the advancement of RAM and Asset Management techniques and applying them in their respective industries. We are grateful to the authors for their patience and cooperation in helping to achieve the high quality of the papers. We are immensely grateful to referees for their prompt review of papers and sparing their valuable time. Lastly, we would like to thank Editor-in-Chief, Professor Krishna B. Misra, for providing us the opportunity to organize this special issue and his continuous support and help in this endeavour.

Timothy Collins is a Reliability Consulting Engineer for General Electric; Power and Water Business. He received a BSEE Degree in 1984 from The Ohio State University. Tim has over 28 years of design experience working with a range of projects including gas turbine accessory systems, wind turbine accessories, fuel cells, air defense systems, onboard weapon systems and personnel carrier weapon and transmission systems. He is a certified Design for Six Sigma Black Belt and has received numerous awards for his efforts. (Email: timothy1.collins@ge.com)

Bhupesh Kumar Lad is an Assistant Professor in discipline of mechanical engineering at Indian Institute of Technology (IIT) Indore, India. Bhupesh received his Ph.D. degree from Department of Mechanical Engineering at IIT Delhi, India, in 2010. He completed his Master of Engineering in Mechanical Engineering from the Rajiv Gandhi Technical University Bhopal, India, in 2005; and Bachelor in Mechanical Engineering, from Government Engineering College Bilaspur, India, in 2002. He worked with Remote Prognostics Lab, General Electric, India, during 2010-2011. He has published several articles in leading technical journals. His research areas include reliability/maintenance of mechanical systems, prognosis of gearbox/bearing, and integration of reliability and maintenance of production machines with the shop floor level operations policies. (Email: bklad@iiti.ac.in)

Jagmeet Singh is currently a Vice President in Chief Data Office of Citibank. Jagmeet received a Ph.D. degree from the Department of Mechanical Engineering at MIT. He received a S.M. degree in Mechanical Engineering from MIT, in 2003 and a B.Tech. degree in Mechanical Engineering from Indian Institute of Technology, Kanpur, India. He has published several articles in leading technical journals. He has filed seven patents in US, and two International Patents. He has served as a Session Chair and a Journal Reviewer for ASME. He holds certifications in the field of Reliability, Data Quality, Data Modeling and Metadata Management, Data Governance, Information Management and Master Data Management. His areas of expertise include Reliability, Multivariate Statistics, RCA, CBM, PHM, Pattern Recognition, Predictive Analytics, Time Series Analysis, Six Sigma, Assembly Architecture, and Systems Engineering. He is a recipient of numerous awards at Citi, GE and IIT. (Email: jagmeet.singh@gmail.com)

Reliability and maintainability of wind turbines are posing new challenges and issues due to advancements in new and sophisticated technologies. This has necessitated development of novel, efficient, and cost-effective strategies for enhancing the availability for power output and operational life. There are certain ways to achieve such objectives where every approach has certain pros and cons. One possible technique is to use the wind speed and power output data for exploring the behavioral similarities of different wind turbines. Based on the similarity measures, a group of turbines may appear together in the form of clusters. This is accomplished by working with the vast piles of data which are pre-processed by using statistical time domain features to provide input to a self-organizing map (SOM) neural network. Based on the clustering results, operational and maintenance strategies are planned for a group of wind turbines in contrast to doing the same work for individual ones. A case study is presented where it has been shown how the information obtained from the clustering analysis would be used for predicting the power output and then developing the optimal operational and maintenance strategies in an integrated manner.

The focus of this paper is on the degradation of the combustion liner and developing the risk prediction model to predict the damage based on hours, starts and key operating parameters. The multivariate K-means clustering technique is used for classifying the data into different sets of clusters i.e., hours, starts or hours to start ratio vs. deformation. The effect of hour to start ratio on the liner deformation was studied, with the significant clusters obtained from K-means clustering. It is concluded that the hours-to-start ratio (N-ratio) can be a good indicator of component life and provides useful information while modeling the metallurgical damage for component life prediction. The damage growth model is developed using Liner Bulging data and it is shown that N-Ratio is a critical factor in damage prediction as well. The analysis is illustrated with the help of a limited set of combustor liner inspection data for actual heavy-duty gas turbine operation. Future guidelines provided in the paper are expected to spawn additional work in the area of advanced gas turbine diagnostics.

In this paper, an opportunistic maintenance approach has been studied on a reciprocating compressor, which consists of series connected structure. When the system is down, either under corrective or preventive maintenance, the opportunity to replace even preventive non-failed subassemblies is considered. The optimization of opportunistic action depends upon the failure cost, which again depends upon the minimum cost of opportunistic action. The impact of opportunistic maintenance on the Life Cycle Cost (LCC) of a reciprocating compressor has been simulated. The case study result shows the reduction of INR 12,541,750 in LCC of a reciprocating compressor and ensures the required level of operational availability, when the opportunistic maintenance is incorporated in its traditional maintenance approach.

In this paper the application of a Proportional Hazard Model (PHM) in order to quantify the effects of climate conditions on the hazard rate of the Stacker belt in The Svea coal mine – in Svalbard, Norway –are discussed. The result of the study shows that the hazard rate of the Stacker belt in winter can be four times more than the rest of the year, which needs to be considered in the maintenance plan of the mine.

In Military aviation, depots confront situations of declaring certain components as High Failure Rate Components (HFRC) to shortlist them for reliability improvement and subjecting them to reviewed maintenance actions. Presently this is done mainly through intuition, experience or based on the number of unscheduled failures at repair depots. This paper develops a methodology by selecting three variants of the same aero engine as a case for deciding a threshold based on reliability, at which the component can be rendered HFRC. Further an optimization methodology based on downtime is also evolved to review the overhaul policy for the HFRCs. Generalized Renewal Process (GRP) models have been utilized to compute Maximum Likelihood Estimators (MLEs). The authenticity of all the developed models is tested through numerical examples and is validated with the existing field conditions.

This paper presents the maintenance decision-making process for the case of a multi-component unit based on output-based maintenance (OBM) technique. OBM is an innovative maintenance technique that applies condition based maintenance (CBM) approach. The principle of OBM is to use machine output measure as the main monitoring parameter for maintenance decision making. A multi-component decision algorithm for the case of non-repairable two serial components unit based on the OBM technique is proposed to illustrate the process of maintenance decision making. The proposed decision algorithm is designed and developed based on ‘rule-based’ decision tree approach, which makes the entire process of decision making easy to understand and interpret. Two maintenance decisions are considered to be decided upon: the right time to perform maintenance and the right component that requires maintenance. An example using a real industry case is presented to demonstrate the applicability of the proposed decision algorithm in making maintenance decisions. Validation result shows that the proposed model provides practical and reliable decisions. This paper ends with a conclusion and some recommendations for future studies..

In this paper a methodology for selection of optimal machine tool configuration by simultaneously considering reliability and maintenance is developed. Methodology is based on measures like Life Cycle Cost (LCC), Availability (A) and Overall Equipment Effectiveness (OEE). These measures explicitly consider the user’s cost structure and shop floor level policy parameters. A simulation based Genetic Algorithm (GA) is used to solve the simultaneous optimization problem. Case example using operations and maintenance data of a CNC grinding machine is presented.

This paper describes the use of the mean cumulative function (MCF) and linear estimates based on the recurrence rate to predict the expected number of failures in the future. Reliability data from two repairable units are used to verify the procedure and comparison. The empirical data used in the paper is based on field data gathered during the operational life of the Swedish military aircraft system FPL 37 Viggen from 1977 to 2006, which essentially is the whole life cycle of the system.

This paper assesses the performance of node eviction schemes in vehicular networking. To secure inter-vehicle communication, a misbehaved node's certificate must be revoked to stop it from injecting messages in the network. The evaluation metrics trade off speed, time taken to remove the node, and accuracy, separation of bad from good. Among various factors affecting a scheme’s performance, the model focuses on the percentage of attacker-controlled nodes. The model abstracts the process of node eviction in order to evaluate a variety of node eviction schemes in vehicular ad-hoc networks (VANETs) for safety-critical services. The novel approach of specifying two subnets, without labeling Bad or Good, increases the flexibility of the modeling. The study discovers the potential of exploring a new class of node eviction schemes.

In order to define an acceptable equivalence between a normal and a logistic distribution, a common standardized way is by the identification of their two first statistical moments. We propose an alternative method based on equality of their differential entropies, which demonstrates the validity of the usual standardization method.