This is the fourth issue of the International Journal of Performability Engineering of the year 2016. This issue of the journal consists of seven technical papers including two short communications and a book review.
The first paper has suggested a bootstrap re-sampling procedure to address the issue of the scarcity of observed data. The authors employed the Gram-Charlier functions and order statistics to approximate the distributions. It is demonstrated how to derive them for a separate repair project and a larger project consisting of a number of concurrently running subprojects. Following this, guidance is provided on how to decide the deadline for completion of the whole work. A simple example is provided.
In the second paper a non-repairable multi-state system with imprecise probabilities and performance rates are taken, representing imprecise probabilities by interval valued probabilities. These intervals are evaluated by computing bound of interval valued ordinary differential equation of the system. For imprecise performance rates random fuzzy variables are introduced. The proposed method is based on hybrid universal generating function and probability intervals to incorporate the uncertainty problem in availability assessment of the system. Finally, availability p-boxes of the system have been evaluated along with a numerical example.
The third paper has shown how knowledge management can be used to achieve quality in finished products and proposed a hybrid spiral model which is integration of spiral model and knowledge management. The authors have proposed a hybrid spiral model to improve software quality using knowledge management which is based on knowledge flow during process. Proposed hybrid spiral model has been illustrated with an example.
In the fourth paper, the data on the wind speed and power generated from a location in the state of Karnataka, India, has been analyzed for the duration of three months. It has been shown that the probability distribution of wind speed conforms closely to Rayleigh distribution. It is demonstrated that, while the wind speed conforms to Rayleigh distribution, the electrical power developed follows a Weibull distribution with two parameters. Besides using graphical methods for estimating the Weibull parameters, Maximum likelihood equations are set up to estimate the parameters. These parameters have been used in estimating / forecasting of wind power using both Weibull algorithm as well as the Monte Carlo Method.
The fifth paper is a review paper on application of simulation for reliability computations. Various simulation methodologies such as Monte Carlo simulation, Discrete event (DE) simulation, Subset simulation, Hybrid subset simulation, Simulated annealing, Stochastic simulation, Digital simulation, and Markov System Dynamics (MSD) simulation are discussed in details. Applications of these techniques in reliability engineering, their advantages and limitations are also presented. The authors have opined that modeling of multi-state devices, degradation and wear out phenomena, state-space approach, point and steady state availability of complex systems can be simplified by simulation techniques. It is also found that the full potential of simulation as a system modeling and analysis approach has not been explored till date in the field of reliability engineering.
The sixth paper (short communication) has proposed a new fault diagnosis method for planetary gearbox based on empirical mode decomposition (EMD) and adaptive multi-scale morphological gradient filter (AMMGF). The proposed method has two dominant strengths: it can be applied to non-linear and non-stationary signal, and it can achieve the goal of de-noising. The framework of the proposed method is introduced first. Experimental data acquired from a planetary gearbox test-rig is then utilized to validate effectiveness of the proposed method. The method is demonstrated to have good performance on both extracting faults characteristic frequency and de-noising.
The seventh paper (short communication) is on the importance measures used in risk-informed applications to characterize the importance of component failures, human errors, common cause failures, etc. In order to decrease the impact of external factors and increase the system survivability, a multilevel protection is applied to its components or subsystems. A multi-state protection survivability importance based on the transitions of protection states is introduced in order to determine which level of protection has the greatest influence on the entire system survivability.
This issue also consists of a book review. The book reviewed is “Energetic Nanomaterials: Synthesis, Characterization, and Application”, Published by Elsevier (2016). This book is edited by Vladimir E. Zarko and Alexander A. Gromov.
I hope that the papers in this issue will be informative to the readers and motivate to do more research in new directions. Suggestions to further improve the journal are most welcome.

Prediction of the duration of a repair and maintenance project of a gas transport system is an important part of planning activities. There exist numerous sources of uncertainties that may result in time overruns possibly leading to multiple negative consequences. Our experience in planning this work suggests that accepting the stochastic nature of the project duration is a constructive step towards the preparedness to contingencies and defining penalties for repair companies. To support this approach, one needs to construct probability distributions of the durations of the projects. To address the issue of the scarcity of observed data, we suggest using a bootstrap resampling procedure. Gram-Charlier functions and order statistics are employed to approximate the distributions. It is demonstrated how to derive them for a separate repair project and a larger project consisting of a number of concurrently running subprojects. Following this, guidance is provided on how to decide about what duration should define the deadline for completion of the whole work. A simple example is provided.

In this paper a non-repairable multi-state system with imprecise probabilities and performance rates are taken, representing imprecise probabilities by interval valued probabilities. These intervals are evaluated by computing bound of interval valued ordinary differential equation of the system. For imprecise performance rates random fuzzy variables are introduced. The proposed method is based on hybrid universal generating function (universal generating function representation of random fuzzy variable) and probability intervals to incorporate the uncertainty problem in availability assessment of the system. Finally, availability p-boxes of the system have been evaluated along with a numerical example.

In a fast and dynamic competitive environment it is not easy to survive and maintain a credit in market. It can be possible when user/customer trust on product quality and its performance. This can be possible when process is well defined because quality of product directly related to the quality of process. To develop a mature and effective process lot of resources is required, every organization want to achieve that at a minimum cost and time. That can be achieved when they effectively utilize the knowledge and experience. At present knowledge management is an area where quality is derived. In this paper we show how knowledge management is used to achieve quality in finished product and proposed a hybrid spiral model which is integration of spiral model and knowledge management. We proposed a hybrid spiral model to improve software quality using knowledge management which is based on knowledge flow during process. Proposed hybrid spiral model has been illustrated with example.

Modeling of wind power is essential for an effective management and balancing of a power grid, supporting real-time operations. Forecasting the expected wind power production would help to deal with uncertainties. The data driven approach for forecasting is expected to give detailed information on the system and real time measurements. Wind being a natural phenomenon, probabilistic methods need to be employed in generated wind power, based on previous history of the system. In this paper, the data on the wind speed and power generated from a location in the state of Karnataka, India, has been analyzed for the duration of three months. It has been shown that the probability distribution of wind speed conforms closely to Rayleigh distribution. It is expressly demonstrated that, while the wind speed conforms to Rayleigh distribution, the electrical power developed follows a Weibull distribution in two parameters. Besides using graphical methods for estimating the Weibull parameters, Maximum likelihood equations are set up to estimate the parameters. These parameters have been used in estimating / forecasting of wind power using both Weibull algorithm as well as the Monte Carlo Method.

Simulation, or more specifically numerical simulation, is a very powerful tool for modeling of engineering, social, business, life science and other systems where the traditional analytical or graphical techniques become very complex, especially if some of the system variables have stochastic nature. Simulation has been applied for solving problems in reliability engineering also. In this paper a literature review on the application of simulation techniques for modeling and analysis of problems in reliability engineering is presented. Various simulation methodologies such as Monte Carlo simulation, Discrete event (DE) simulation, Subset simulation, Hybrid subset simulation, Simulated annealing, Stochastic simulation, Digital simulation, and Markov System Dynamics (MSD) simulation are discussed in details. Applications of these techniques in reliability engineering, their advantages and limitations are also presented. It is also found that the full potential of simulation as a system modeling and analysis approach has not been explored till date in the field of reliability engineering. Since several variables in reliability engineering field such as time to failure, time between failures, time to repair, down time, and others have stochastic nature, simulation approach is very appropriate. It appears that modeling of multi-state devices, degradation and wear out phenomena, state-space approach, point and steady state availability of complex systems can be simplified by simulation techniques. Therefore, this paper also has suggested that more research in these areas is needed.

Planetary gearbox is widely used in many fields due to its robustness and high power-weight ratio, but implementation of fault diagnosis on it is challenging. This paper proposes a new fault diagnosis method for planetary gearbox based on empirical mode decomposition (EMD) and adaptive multi-scale morphological gradient filter (AMMGF). The proposed method has two dominant strengths: it can be applied to non-linear and non-stationary signal, and it can achieve the goal of de-noising. The framework of the proposed method is introduced first. Experimental data acquired from a planetary gearbox test-rig is then utilized to validate effectiveness of the proposed method. The method is demonstrated to have good performance on both extracting faults characteristic frequency and de-noising.