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 f ifth 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.

Service sector has become more relatively important as it has become a most essential part of any economy. One of the industries in this service sector is the Airlines industry. Being one of the modes of transport that propels the economic activity of a Nation, it has become essential to study the quality of service that should be made available to the users by this industry. So far the focus on airline service research has been to identify airline selection criteria and performance rankings. Of late the focus of research efforts has shifted to examine the issues inherent in various passenger segments. Prime objective of this study is to analyze the relationship between air passengers’ class of service and their perception of service quality and also purpose of the trip, and satisfaction. This investigation indicates that majority of respondents perceived that responsiveness is most important, followed by tangibility, empathy, assurance and reliability. In contrary, consumer satisfaction level is more for tangibility followed by responsiveness, reliability, empathy and assurance.

This research article presents a methodology to identify the critical component of a centrifugal pump and analyze its reliability. Failure analysis of major components of the pump was done, risk priority number was calculated and critical component was identified. The critical component was considered to be composed of two sections. Its reliability was analyzed by mathematical modeling of the first section. A finite element model of the component was developed and stress analysis was done using finite element method, in order to assess the reliability index of the second section. Total reliability of the component was calculated by multiplying the two reliability indices.

This paper is a sequel to [1], wherein we proposed a simple procedure to construct the joint prior and posterior distributions of Weibull parameters based on the underlying reliability function estimates in two time cross–sections. In this paper, we extend the procedure in three aspects: a) the prior data can now be taken in terms of a simple probability paper plot, b) the posterior now includes not only posterior estimates of distribution parameters, but also the posterior estimate of the underlying reliability function along with the respective credibility intervals, and c) we show that the proposed procedures can be applied to any parametric lifetime distribution, not necessarily limited to the location–scale family.

Today, the evaluation of the production systems’ performance requires having an appropriate decision-making indicator and to fix its target. The lack of a method to set the target OEE, in accordance with the strategic objectives of the company, is a handicap to guide managers to achieve their individual performance. In this article, we propose a model, based on the method that allows comparison decision criteria pair determining the target of the OEE from the classic OEE. Therefore, this model allows to guide operational decision-makers (maintenance, production, quality,...) to better control their resources and achieve their objective. A case of study was conducted within the mineral water company oulmes (EMO) to test the proposed model. A comparison shows that the offered model provides sure, more definite and better results than the classical approach (based on experience feedback).

The applications of eddy current dampers have been explored in the past few decades due to their non-contact nature which poses several advantages over the conventional hydraulic dampers. This paper discusses the real-time simulation and analysis of an eddy current damper which can be used for the protection of windows and doors against winds and human behavior. The damper consists of a hollow cylindrical conductor and axially magnetized permanent magnets. The equations have been solved to obtain the final velocity of the window in LABVIEW. Decrease in final velocity of the window with damper is calculated at different instants and for different forces. Performance of the damper is compared for different conductors also. The damping coefficient, damping ration and loss of radial flux density in the air gap are also calculated. The results show that the damper can be successfully used as an alternative to the hydraulic dampers for safety.

System degradation modelling is a key problem when performing any type of reliability study. It is used to determine the quality of the computed reliability indicators and prognostic estimates. However, the mathematical models that are commonly used in reliability studies (Markov chains, gamma process. etc.) make certain assumptions that can lead to a loss of information regarding the degradation dynamics. Many studies have shown how Dynamic Bayesian Networks (DBNs) can be relevant in representing complex multicomponent systems and in performing reliability studies. In a previous paper [10], Donat et al. introduced a type of degradation model based on DBNs called a graphical duration model (GDM) for discrete-state systems to represent a wide range of duration models. This paper introduces a new type of degradation model based on the GDM approach that integrates the concept of conditional sojourn time distributions (CSTDs) to improve the degradation modelling. It introduces the possibility of considering many degradation dynamics simultaneously. It allows the degradation modelling to be adapted based on newly available observations of a system to account for changes in dynamics over time. A comparative study of the presented methodology and the GDM approach was conducted using simulated data to demonstrate the advantages of this new approach in performing prognostic computations. Only two coexisting dynamics are considered in the experiments for the sake of simplicity.

The current levels of competition makes the organizations to continually strive to meet the customer demand but it has been observed that due to problems like machine breakdown, machining and setting process delays it has become difficult in meeting this on time. The objective of this article is to inspect the manufacturing losses arising on account of such problems by prioritizing the root causes with the help of a pareto diagram and finally suggesting the solution to overcome these problems. A case study was carried out to improve the utilization of machine tool and manpower by initiating the practices through, TPM that would also form as a base for lean manufacturing. TPM helps to adopt a systematic work inside the shop floor which reduces the losses in production activity, increases the equipment life, ensures effective utilization of equipments and organized employee behavior. Introduction of new fixture reduces the idle time of machine during component setting and achieving cycle time reduction by analyzing cutting tool and its parameters which helps to increase the output to meet the customer demand. From the final results, it was observed that there was reduction in setup time, cycle time, breakdown losses and rework time, while the overall equipment effectiveness was also found to have increased by about 15%.

This review paper examines the chemical and mechanical properties of natural fiber reinforced polymer bonded composites and the processing techniques are compared for the reinforced composite materials. The chemical and mechanical properties of the different natural fibers composites were compared. Present days natural fibers are attracting many scholars and researchers due to its cost and largely available in nature also processing of these fibers is not hard in comparison to the conventional fiber’s production. Also, Environmental awareness and a growing concern with the greenhouse effect have triggered the construction, automotive, and packing industries to watch out for eco-friendly materials that can replace conventional synthetic polymeric fiber’s. Natural fibers seem to be a good alternate because they are readily available in fibrous form and can be extracted from herb leaves at very low costs. By these reasons the natural fibers are trusted over the regular fibers.

Massive Open Online Courses (MOOCs) have gained rising popularity in recent years. However, MOOCs have faced a challenge of a large number of students dropping out from courses. Most studies predict dropouts based on some general features extracted from historical learning behavior and ignore the diversity of the behaviors. To solve this problem, we first analyze each type of learning behavior independently to get the different behavior patterns between dropout and retention students. We then derive multiple kinds of features from the corresponding types of learning behavior records. After that, we propose three algorithms that make use of these features. The first one trains several detectors based on each types of features. The second utilizes multi-view ensemble learning to anticipate dropouts. The third applies semi-supervised co-training to train the detector. Experimental results justify the rationality of the multi-view features and the proposed approaches achieve better prediction performances.

To analyze and calculate the micro-scale stress of viscoelastic fluid in displacing residual oil and explore the rheological property of non-Newtonian fluid from hydrodynamics angle, an upper-convected Maxwell constitutive equation was selected to simulate viscoelastic fluid. Furthermore, boundary conditions were adopted to calculate the flow field of flow, and the normal deviatoric stress and horizontal stress difference acting on residual oil from viscoelastic fluid were calculated by combining stress tensor theory. The calculated result showed that: the viscosity, elasticity and flow rate of polymer solution are key factors influencing stress and deformation of residual oil. Therefore, methods like increasing mass concentration and molecular mass of polymer solution can be applied to enlarge viscosity and elasticity of polymer solution, thus increasing the displacement stress acting on residual oil and laying foundation for further analysis on deformation and breakup of residual oil.

When observations are organized into groups where commonalties exist amongst them, the traditional clustering models cannot discover shared clusters among groups. In this scenario, the dependent normalized random measures based clustering is a perfect choice. The most interesting property of the proposed LMRM based clustering is that the clusters are assumed to be shared across groups. Hence the problem can be solved immediately. We derive appropriate exchangeable probability partition function, and subsequently also deduce its inference algorithm given any mixture model likelihood. We provide all necessary derivation and solution to this framework. For demonstration, we used mixture of Gaussians likelihood in combination with a dependent structure constructed by linear combinations of completely random measures. Our experiments show superiority performance when using this framework, where the inferred values including both the mixing weights and the number of clusters both respond appropriately to the number of completely random measure used.

Inferring dynamic complex network through a small set of samples is a challenging problem in the field of biological network, social network and transportation network, which can help improve understanding of complex network systems. In this letter, a new Hybrid Model based Latent Variables Sampling algorithm is presented to address the problems of high computation complexity and low accuracy faced by traditional approaches. Experimental results on simulated and real data sets show that the presented method possesses better reasoning performance and significantly improves the precision and efficiency of network inference especially when compared with the other three approaches. Under different dimensions, HM-LVS still has higher accuracy (average 80%) and can effectively reverse engineering dynamic complex networks from time series data.

In the virtual data-center, how to map virtual machines (VMs) to physical machines (PMs) is becoming a hot issue. However, most of existing VM scheduling schemes have not fully considered the reliability and dynamical workloads of VMs. This paper presents a novel bionic autonomic nervous system (BANS) based approach for cloud resource management. This approach supports self-optimization that provides a dynamic and autonomic way to adapt to dynamical workloads and VM resource requirements. For the VM allocation in the self-optimization, this paper presents a reliability-performance-energy correlation model that can model, analyze and evaluate reliability, performance and power consumption simultaneously.