We have nine papers included in this issue. They come from different areas of applications for variety of systems and use new modelling approaches.

The first paper, A System Dynamics Model for Transient Availability Modeling of Repairable Redundant Systems, combines the Markov approach with system dynamics simulation approach for availability modelling of a system. Further the paper demonstrates that the authors’ approach can be used for transient availability modeling both with time varying failure and repair rates as well as constant failure and repair rates.

In the second paper of this issue, A Comparison of Hidden Markov and Semi-Markov Modeling for a Deterioration System subject to Vibration Monitoring, the authors compare a hidden Markov and Erlang semi-Markov modeling of a partially observable deteriorating system operating under a varying load and subject to multi-sensor vibration monitoring. The evolution of the unknown state process is described by a hidden, two state semi-Markov process with an Erlang sojourn time distribution in the healthy state. The unknown model parameters are estimated using the EM algorithm. The authors derive explicit formulae for the parameter re-estimation in the EM algorithm, which provides a fast estimation procedure.

The third paper of the issue, An Integrated Approach for Fleet Level Maintenance Planning, the authors develop an integrated approach to simultaneously optimize the decision of location of maintenance, the level of indenture for maintenance, the degree of maintenance and the level of repair spares inventory at different echelons. The authors use time dependent failure rate model for different part failure in a machine. This helps in considering preventive maintenance (PM) for machine and to integrate PM plans with above decisions.

In the fourth paper, Reliability Estimation Based on Moving Average and State Space Model for Rolling Element Bearing, a new method for rotating machine reliability estimation is presented. Wavelet packet feature extraction, moving average, and SSM are combined together for condition estimation. Based on the rolling element bearing analysis, the effectiveness of this method is demonstrated. The results indicate that the moving average filtering can effectively improve the signal noise ratio of the degradation characteristics and improve the accuracy of the prediction.

The fifth paper, Experimental Studies of In-House Developed Magnetorheological Fluid in a Damper, presents a process to develop stable MR fluid using in-house technology at a fraction of cost at which the fluid is available commercially. The developed fluid is tested for on-state as well as off-state rheological properties in a fabricated prototype MR damper.

In the sixth paper, Bi-Objective Topology Design of Communication Networks Using Dynamic Programming, the authors present, what they call as called NTD-CR/B, to design networks with minimum cost and maximum reliability subject to the condition of satisfying a required operational bandwidth. For a given a set of nodes, their possible connecting links, link failure rate, bandwidth and installation cost, the problem selects the best set of links such that the resulting topology meets its required bandwidth Bmin while minimizing its installation cost and maximizing its reliability.

The seventh paper of this issue, Examining Efficacy of Metamodels in predicting Ground Water Table,

the authors examine the capability of Gaussian Process Regression (GPR), Generalized Regression Neural Network (GRNN) and Relevance Vector Machine (RVM) for prediction of Ground Water Table (d_w) at a site. RVM, GRNN and GPR have been adopted as regression techniques. The results show that the developed RVM gives the best model for prediction of d_w at the site, viz., at Vellore.

In the eighth paper, Hybrid Tool for Optimal Preventive Maintenance Schedule for Deteriorating Systems, the authors claim that a hybrid tool combining Simulated Annealing and integrating it with graphical model provides a feasible alternative in arriving at decisions regarding the feasibility of preventive maintenance (PM) over break down maintenance (BDM) and hence providing close to global optimal schedules for PM of deteriorating systems while maximizing availability.

In the last paper of the issue, Stochastic Analysis of a Two-Unit Cold Standby System with Arbitrary Distributions for Life, Repair and Waiting Times, the authors discuss a stochastic model considering general distributions for life, repair and waiting times. Expressions for various measures of the system effectiveness have been derived by making use of semi-Markov processes and regenerative point technique.

Besides the above nine papers, reviews of four latest books are provided in this issue for the benefit of IJPE readers.

In this paper, a novel Markov system dynamics (MSD) simulation frame work for the availability modeling of a repairable system is developed. The proposed method combines the Markov approach with system dynamics simulation approach for availability modeling. This approach will have the advantages of both Markov as well as System dynamics methodologies. Initially, the proposed framework is illustrated for a two component active redundant system with repair with constant failure and repair rates. There after, transient availability modeling with time varying failure and repair rates is performed for the same system. It is worth mentioning here that finding the steady state condition and transient availability modeling are extremely difficult or impossible using traditional approaches. The results of MSD simulation are compared with that obtained by traditional Markov method for validation. This has shown that MSD simulation is a good alternative to the traditional mathematically intensive methods.

We compare a hidden Markov and Erlang semi-Markov modeling of a partially observable deteriorating system operating under a varying load and subject to multi-sensor vibration monitoring. The evolution of the unknown state process is described by a hidden, two state semi-Markov process with an Erlang sojourn time distribution in the healthy state. The unknown model parameters are estimated using the EM algorithm. We derive explicit formulae for the parameter re-estimation in the EM algorithm, which leads to a fast estimation procedure. An optimal Bayesian maintenance policy is developed minimizing the long run expected average cost per unit time and a formula for the mean residual time in the healthy state is derived as a function of the posterior probability statistic. The results show that a simple hidden Erlang model performs better than a hidden Markov model, which is encouraging for considering a more general hidden semi-Markov modeling in future research.

Effective maintenance planning is essential for reducing Life Cycle Cost (LCC) of capital intensive industrial systems. Such systems are generally maintained at fleet level. Fleet level maintenance planning decisions, like level of repair decisions and spare parts stocking decisions, largely contribute to the Life Cycle Cost of the systems. In the present research, an integrated approach is developed to simultaneously optimize the level of repair decisions and spare parts stocking decisions while minimizing the Life Cycle Cost (LCC) of fleet maintenance. In this research, failures of machine components are modeled using time dependent failure rate models thereby making the analysis more practical.

Reliability analysis based on equipment's performance degradation characteristics is one of the important research area in reliability analysis. Many a times, research is carried on the basis of multi-sample analysis, but application is limited to a single equipment reliability prediction. Therefore, the method of reliability prediction based on state space model is proposed for small sample analysis. First, signals about machine working conditions are determined based on-line monitoring technology. Secondly, wavelet packet energy is applied on characteristic extraction for the monitored signals. Frequency band energy is determined to be as characteristic parameter. Then, the degradation characteristics of signal to noise ratio is improved by moving average filtering processing. In the end, state space model was established to predict degradation characteristics of probability density distribution, and the degree of reliability is determined. Rolling element bearing reliability analysis is used an example to demonstrate the rationality and effectiveness of this method.

Applications of magnetorheological fluids which have wide applications in automotive and structural engineering in dampers, clutches etc., have been widely studied. The cost of commercially available MR fluids is restricting its extensive use. In this paper, a stable MR fluid is developed using in-house technology at a fraction of cost at which the fluid is available commercially. The developed fluid is tested for on-state as well as off-state rheological properties in a fabricated MR damper prototype. The off and on-state characteristic of the fluid in terms of viscosity and damping force respectively is evaluated and compared with the commercially available MR fluid. This study has revealed that the developed MR fluid is following the same pattern for damping force as that of commercially available MR fluid and can be used in various commercial applications at highly reduced cost.

This paper provides an algorithm to design a communication network topology with minimal cost (C) and maximum (s, t) reliability (R) subject to a pre-defined bandwidth (Bmin) constraint, given (i) locations of the various computer nodes, (ii) their connecting links, (iii) each link’s reliability, cost and bandwidth, and (iv) a minimum bandwidth for the network. The bi-objective optimization problem is known NP-hard; henceforth we call the problem NTD-CR/B. We use the dynamic programming (DP) formulation as the engine in our proposed approach, DPCR/B, to generate the topology for solving NTD-CR/B. Further, we propose three greedy heuristics that enumerate and order only k?n (s, t) paths, where n is the total number of (s, t) paths in the network. Each heuristic allows DPCR/B to obtain the selected paths to form the topology using only k paths, which improves the time complexity while producing near optimal topology. Extensive simulations on large networks with various sizes show that DPCR/B is able to generate 91.2% optimal results while using only 1.37-27% of all paths in the grid networks that typically contain up to 299 paths.

This article examines the capability of Gaussian Process Regression (GPR), Generalized Regression Neural Network (GRNN) and Relevance Vector Machine (RVM) for prediction of Ground Water Table (dw) at Vellore (India). RVM, GRNN and GPR have been adopted as regression techniques. RVM is a probabilistic model. GRNN approximates any arbitrary function between input and output variables. GPR is a non-parametric model. The developed GPR, RVM and GRNN give the spatial variability of dw at Vellore. Map of dw has been also produced by the GPR, RVM and GRNN models. The results show that the developed RVM gives the best model for prediction of dw at Vellore.

This paper statistically establishes the feasibility of using Simulated Annealing as compared to traditional method for the wide range of deteriorating systems and hence presents a Simulated Annealing assisted hybrid tool to determine optimal PM schedule for maximizing availability of deteriorating systems. The proposed method carries out initially the Weibull analysis on the failure data collected, checks for feasibility of PM and if PM is found feasible it searches for global optimal schedule for PM using SA. The results obtained from SA are statistically compared with those obtained using traditional method (Integrated Graphical model) using inferential statistics. As the findings are quite encouraging, the proposed hybrid tool can be conveniently used for deciding PM strategy.

El-Said and El-Sherbeny [5] analyzed two-identical-unit cold standby system with two stages of repair. The whole repairing process of failed unit is supposed to be completed only in the second stage. The elapsed time between two-stages of repair is called the waiting time. Authors of [5] developed a model and claimed that the results obtained by them are for the case when all the time distributions are general. But they did not get any non-regenerative state in their model which indicates that they could not take care of their claim. The claim has been taken care of in the present paper, wherein a stochastic model is developed considering all the distributions as general. Expressions for various measures of the system effectiveness are obtained by making use of semi-Markov processes and regenerative point technique. A particular case has also been discussed where all the distributions of times have been taken as exponential. Various conclusions and bounds pertaining to the profit of the system have also been obtained.