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May 2013

Guest Editorial

Volume 9, Number 3, May 2013  pp 243-244

TIMOTHY COLLINS, BHUPESH KUMAR AND JAGMEET SINGH


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)

 
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