Abstract:

As usual, in this first issue of the ninth year of publication of International Journal of Performability Engineering, we bring to our readers 10 new papers and one Short Communication, which together provide several new techniques and applications in the upcoming areas of performability engineering.

The first paper of this issue provides a new sampling plan for censored life testing when life time follows Weibull distribution, whereas the next paper of the issue proposes a new approach to software performability considering the aspect of collusion status during the binding-phase of open source softwares in open source solution. This is a new paradigm to reduce cost and quick delivery. The next paper provides an an important tool of assessing the condition of a power transformer using Disolved gas Analysis and Partial Discharge measurement. This technique would help predict the incipient breakdown of the power transformer insulation well in advance and help avoid the failure of power transformer. The next paper is on energy saving strategies for a building with glazed fa?ade when it is exposed to Sun. This study can help evolve environmentally favourable and sustainable solutions for energy saving in buildings particularly in tropical zone. The next paper of the issue compares the efficacy of two pattern recognition approaches for fault detection, viz., Artificial Neural Networks (ANN) and Support Vector Machines (SVM) as it is crucial for developing an efficient approach for fault diagnosis.

The sixth paper of this issue discusses the effect of network size, transmission range, and network coverage area, on the reliability measures of MANETS by modeling them as Geometric Random Graphs. The reliability of these ad hoc network is the most challenging and interesting area because of changing of topology, particularly during emergency situations like human-induced disasters, military conflicts, emergency grounds, commercial applications etc. The next paper discusses the advances of clustering as clustering is a very important business strategy as propagated originally by Porter and is extended in this paper by the authors in enhancing a firm’s performability. The paper demonstrates the usefulness of clustering in case of Samsung Electric Corporation of South Korea. The next paper describes a four quadrant framework for characterizing print shops in the printing industry based on the workflow complexity and utilization of resources. The formalism of heavy tail distributions is used to characterize the extreme levels of variability observed in job size. Methods of quantifying and characterizing the variability are presented. The implementation of the LDP Lean Document Production solution in a large transaction shop is presented and is shown to deliver substantial productivity benefits. In the next paper, a new application of FMEA to an Off-shore Floating Desalination plant is made when the plant is run by an autonomous variable power source such as wind generator. This is a typical application paper. The last paper of the issue is again a typical application paper that optimizes the system availability of a Coal Handling Plant using Genetic Algorithms.

In addition to the above papers, we also present a short communication that offers a new approach for evaluating reliability of a 1-out-of-(n+1) warm standby system subject to fault level coverage. It is sincerely hoped that this issue will generate considerable interest among our readers, who are looking for new ideas and applications in the field of performability engineering.

Besides these papers, we have also included in this issue two important announcements –the first one is about a special issue of IJPE on Performance of Space Vehicles being guest-edited by Dr. William E. Vesely, of NASA Headquarters,, who is well-known for his generic contributions in reliability engineering and the other announcement is about starting a book series on Performability Engineering which has already contracted four books from authors of various countries and some more are in the pipeline.