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Volume 13, Number 2, March 2017 - Editorial - p 107

Sanjay K. Chaturvedi and Steven Li

This is the second IJPE issue of the year 2017 and very first issue under the new EICs. Being associated since its inception in Mid-2005 with this journal and its founder EIC, Prof. Krishna B. Misra in July-2005, we have witnessed several changes in its presentation and contents- from Quarterly to Bi-monthly, in 2011, besides, occasional changes in editorial board from time to time and to widen its coverage to a larger gamut of Performability Engineering- a term formally coined by Prof. K. B. Misra by presenting its holistic view centred around 4Ps: Product, Process, Planet and People with an aim not only to cover engineering systems but also the processes, environment and human systems, in order to achieve sustainable, dependable and cleaner systems and products for now and in future - over their entire life time (birth to post death), in truest sense, in order to save our planet for the future generations to prosper and flourish as well.

The present issue consists of twelve articles dealing with different arenas of Performability Engineering and one short communication that presents a bionic autonomic nervous system (BANS) based approach for cloud resource management. The very first one is a paper with an extensive wealth of information for the airline service industry. The fact that the data is processed and analysed in a logical, simple and arithmetic way is very beneficial to the readers. Results of this investigation are also of practical relevance to both the airline industry and logistics researchers. In the second paper, a methodology to identify the critical component of a centrifugal pump and reliability analysis are presented by combining Finite Element Model, FMEA and Stress-Strength interference theory. Bayesian procedures have been applied in many areas of engineering research- most often in the areas wherein the data is scarce or subjective. A critical step in this procedure is the specification of the prior distribution. The third paper takes up this issue and presents the extension of the work by the same authors that has been implemented in JMP® of SAS institute. It presents a procedure of Bayesian estimation of the Weibull distribution based on a single random sample characterizing prior data- not a limitation of the approach, and a single random sample characterizing current data.

The lack of an appropriate method to set the targeted equipment effectiveness, in accordance with some strategic objectives of an organization, is a handicap to guide managers to achieve their individual performance. In fourth article, a Fuzzy theory based model that allows comparison decision criteria pair to determine the target overall equipment effectiveness (OEE) from the classic OEE to control the available resources is proposed. In the same vein, seventh article, a TPM implementation to improve the level of quality and to reduce the manufacturing cost of the product to increase the OEE of a windmill component manufacturing industry, specifically in the CNC machine shop, is presented. The fifth paper discusses the real-time simulation and analysis of an eddy current annular shaped permanent magnet damper to ensure safety of windows and doors against winds and human behaviour, which may otherwise cause noise and damage to the physical structure.

System degradation modelling has been a key issue when performing any type of performance study. The sixth paper presents an improved model of degradation phenomenon based on the graphical duration model (GDM) by integrating the concept of conditional sojourn time distribution. It is expected to perform failure prognosis computations with higher accuracy than those obtained by using standard degradation models for the systems as employed in railways or road infrastructures. The eighth article of this issue examines and review the chemical and mechanical properties of natural fiber reinforced polymer bonded composites, besides, comparing the processing techniques for the reinforced composite materials.

The ninth article of this issue studies the patterns of students learning behaviours to predict which students are more likely to drop out in Massive Open Online Courses. The tenth article proposes an approach to analyze and calculate the micro-scale stress of viscoelastic fluid in displacing residual oil by combining an upper-convected Maxwell constitutive equation and other boundary conditions. The eleventh paper introduces a Linear Mixing Random Measures based clustering algorithm to group elements when different clusters may share the same elements. In the last article, a new Hybrid Model based Latent Variables Sampling algorithm is presented to address the challenges of inferring dynamic complex network.


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