With this issue, IJPE is entering in the 11th year of its publication. An update on IJPE is provided on page 100 of this issue which tells us that by publishing 52 issues so far (which means on an average of more than 5 issues per year) and by having authors’ footprint over 50 countries of the world, IJPE has established an impressive record of its visibility. During this period, IJPE has published 438 full papers and 50 short communications. By publishing 89 book reviews, IJPE has kept its readers fully informed about the arrivals of the new titles that are available in the literature. IJPE has brought out 22 special issues or sections of an issue involving guest-editors who are well-known specialists in various areas covered by IJPE’s scope during this period. This provide a close interaction between the academia, researchers and practitioners.

In this issue as usual, we present 9 full papers and a short communication, besides providing reviews of three recent books for the benefit of IJPE readers.

The first paper, Technology Qualification Program Integrated with Product Development Process by Maryam Rahimi and Marvin Rausand concerns implementation of a technology qualification program (TQP) which some industries have developed to reduce the uncertainty pertaining to new products development. This paper evaluates and highlights important features of existing TQPs, and proposes a new approach that aims to rectify some weaknesses of the existing approaches.

The second paper, Simultaneous Use of and R Charts for Positively Correlated Data for Medium Sample Size, by Prajapati,presents modified joint and R charts for various levels of correlation. The performance of the joint modified and R chart is measured in terms of average run lengths (ARLs). The ARLs are computed for sample size of 5, using MATLAB software. The simplicity in the design of the joint modified and R chart for various levels of correlation makes it suitable for the industries; where the data are correlated.

The third paper, Estimation of Maximum In-Service Inspection Intervals Based on Risk: A Fuzzy Logic Based Approach, by Chandima Ratnayake proposes a fuzzy logic based expert system for estimating maximum in-service inspection intervals. The estimation of maximum in-service inspection intervals is done based on probability of failure (PoF), consequence of failure (CoF) and currently established values of maximum inspection intervals (MIIs) with respect to different risk levels.

The fourth paper, Particle Swarm Algorithm for Optimization of Complex System Reliability by Sangeeta Pant et.al., a particle swarm optimization algorithm is presented and the performance of the proposed algorithm is tested on some complex engineering optimization problems.

The fifth paper, Early Prediction of Software Fault-Prone Module using Artificial Neural Network,,by Manjubala and Goyal proposes two approaches for the selection of software metrics. First a sensitivity analysis using proposed artificial neural network (ANN) model, named as SA-ANN, is studied and then principal component analysis is used as a tool for dimension reduction with use of ANN model, named as PCA-ANN. The proposed approaches are applied to four NASA datasets to study the effect of scaling on the prediction accuracy.

The sixth paper, Classification of Static Mechanical Equipment using a Fuzzy Inference System: A Case Study from an Offshore Installation, by Seneviratne and Chandima Ratnayake, suggests a fuzzy inference system (FIS) to revise and fine-tune an existing static mechanical equipment classification which has been utilized for the inspection and maintenance of a North Sea P&PF. The proposed approach supports establishing optimal risk-based inspection programs whilst enhancing the quality of in-service inspection programs.

The seventh paper, Bearing Fault Detection based on Stochastic Resonance Optimized by Levenberg-Marquardt Algorithm, by Xinghui Zhang, et al. presents a different method from the existing ones which search the optimal system parameters in a Stochastic Resonance model and uses Levenberg-Marquardt approach which is basically a numeric optimization method to solve this problem. The proposed method is validated by simulation signal and two bearing fault signals.

The eighth paper, Optimum Time-Censored Step-Stress PALTSP with Competing Causes of Failure Using Tampered Failure Rate Model, by Srivastava and Sharma, present a time-censored step-stress partially accelerated life test variable repetitive group sampling (RGS) plan with competing causes of failure using tampered failure rate model and Weibull life distribution. The optimum plan proposed consists in finding out optimum stress change point and optimum sample size by minimizing the average sample number of a lot such that producer’s and consumer’s interests are safeguarded.

The ninth paper, Subset Cut Enumeration of Flow Networks with Imperfect Nodes, by Chakraborty and Goyal, presents a simple approach to consider node failures in reliability evaluation of flow networks using subset cut approach. The method is computationally efficient as it requires less computational time to enumerate subset cuts for networks with imperfect nodes and links compared to other approaches available in the literature.

The last paper which is a short communication, Joint Lot-size and Preventive Maintenance Optimization for a Production System, by Liu et al. presents a framework to solve the optimal lot-size of each product in a production system producing multiple products, together with the optimal preventive maintenance epoch which minimizes the system’s cost rate in long run.

For high-reliability applications such as the subsea oil and gas industry, it is necessary to assure that new products have the required quality and reliability before they are put into operation. To provide such assurance, some industries have implemented a technology qualification program (TQP) to reduce the uncertainty for new products development. Several TQP approaches have been proposed, but no approach has yet been generally accepted. Some producers have merged seemingly attractive features from different TQP approaches, but this has not always given a practical and cost-efficient approach. This paper evaluates and highlights important features of existing TQPs, and based on the findings combined with a thorough literature survey proposes a new approach that aims to rectify some weaknesses of the existing approaches.

The chart is used to monitor the process mean while range (R) and S charts are used to monitor the process standard deviation. But when it is required to monitor the changes in both mean and standard deviation of the process, the joint and R charts should be used simultaneously for better results. The design of modified joint and R chart is based upon the sum of Chi-squares theory and it may be used for medium sample sizes. The performance of modified joint chart is compared with joint Shewhart chart for sample sizes of five. It is found that the joint modified and R chart outperforms the joint Shewhart and R chart at all the levels of correlation and process shifts in the mean and standard deviation. It is observed that the performance of joint chart deteriorates as the level of correlation increases. The simplicity in the design of the modified joint and R chart makes it suitable for the industries where the data are positively correlated.

Risk based inspection analysis (RBIA) on offshore topsides static mechanical pressure systems aid the optimum performance of in-service inspection. Risk based in-service inspection analysis (RBISIA) essentially considers failures by loss of containment of the pressure envelope and supports decision making in inspection planning. This is based on the potential failure risk of a system, a sub-system or a thickness measurement location (TML): comprising the consequence of failure (CoF) and probability of failure (PoF). The RBISIA process is designed to aid the development of optimized inspection and recommendations for monitoring and testing plans for production systems. In this context, a tailor-made risk matrix supports the estimation of maximum inspection intervals (MIIs). When the MIIs are calculated using a risk matrix, suboptimal classification tends to occur as there are no means to incorporate actual circumstances at the boundary of the input ranges or at the levels of linguistic data and risk categories. This manuscript suggests fuzzy logic based approach via a fuzzy inference system (FIS) to overcome the aforementioned. Membership functions (MFs) and the rule base development have been carried out in alignment with a tailor-made risk matrix which has been utilized by a plant operator organization. A rule view and a calculation result have been demonstrated to illustrate the methodology.

In recent years, a broad class of stochastic metaheuristics, such as Tabu search, simulated annealing, genetic algorithm, particle swarm optimization, ant colony optimization etc. has been applied for reliability optimization problems. In this paper a particle swarm optimization algorithm is presented. Then, the performance of the proposed algorithm is tested on some complex engineering optimization problems. They are three well-known complex reliability optimization problems. Finally, the results are compared with those given by several well-known methods. Numerical experiments demonstrate that the proposed method is promising and the results obtained by proposed algorithm are either superior or comparable to the previously best known results presented in literature for reliability optimization of complex systems in terms of computation time as well as solution quality.

Prediction of software modules into fault-prone (FP) and not-fault-prone (NFP) categories using software metrics allows prioritization of testing resources to fault-prone modules for achieving higher reliability growth and cost effectiveness. This paper proposes an Artificial Neural Network (ANN) model with use of Sensitivity Analysis (SA-ANN) and Principal Component Analysis (PCA-ANN) for dimensionality reduction of the prediction problem. In SA-ANN model, a non-linear logarithmic scaling approach is used to scale metrics values, which improves quality of ANN training, followed by sensitivity analysis to rank and choose top Sensitivity Casual Index (SCI) value metrics. In PCA-ANN model, PCA is used for reducing dimensions of the problem and then the reduced dimension data is scaled using logarithmic function followed by training and prediction by ANN model. Simulations are carried out for four benchmark datasets to evaluate and compare the classification accuracy of proposed models with existing models. It has been found that non-linear scaling has good effect on predictive capability and PCA-ANN model provides higher accuracy than SA-ANN model and some other existing models for four datasets.

A recent audit of oil and gas (O&G) production and process facilities (P&PFs) functioning on the Norwegian Continental Shelf (NCS) revealed that inadequate classification of equipment tends to increase the probability of maintenance induced failures. Hence, to mitigate the problem, this manuscript suggests a fuzzy inference system (FIS) to further revise and fine-tune an existing static mechanical equipment classification which has been utilized for the inspection and maintenance of a North Sea P&PF. Such a revision and fine-tuning of the existing classification enables the equipment in a sub-system of a P&PF to be identified by its degradation mechanism and classified under common degradation groups (e.g., corrosion loops, erosion loops, etc.). A case study has been performed using condition monitoring data and historical in-service inspection data retrieved from the piping inspection database (PIDB) belonging to a P&PF located on the NCS.

Bearings are one of the most important components in rotating machineries because their failure could cause catastrophic disasters of whole system. Currently, one of the main problems when implementing bearing prognostics and health management is how to detect the incipient fault as soon as possible. This capability can enable the operators having sufficient time to implement preventive maintenance activities. For incipient fault, its vibration signal is relatively weak and always submerged in the noise, which makes the fault hard to be detected. Stochastic resonance is a good way to detect the weak signal in strong noise. However, the effect of the stochastic resonance depends on the adjustment of two parameters. Current parameter optimization methods are mainly depend on some random searching algorithms like particle swarm optimization, genetic algorithm etc. However, these methods may converge to local optima and need more searching time. So, the Levenberg-Marquardt algorithm is utilized to optimize the two parameters in this paper. The resonance effect is evaluated by signal-to-noise ratio. In order to validate the effectiveness of the stochastic resonance optimized by Levenberg-Marquardt, two bearing fault data sets were used. The analysis results state the proposed method could detect the fault earlier.

In this paper accelerated life testing is incorporated in life test sampling plans to induce early failures of high reliability items. Life test under accelerated environmental conditions may be fully accelerated or partially accelerated. In fully accelerated life testing all the test units are run at accelerated condition, while in partially accelerated life testing they are run at both normal and accelerated conditions. Many products have more than one cause of failure. Optimum time-censored step-stress partially accelerated life test sampling plan (PALTSP) with competing causes of failure has been designed using tampered failure rate model and variable repetitive group sampling plan. The optimum plan consists in finding out optimum stress change point and optimum sample size by minimizing the average sample number of a lot such that producer’s and consumer’s interests are safeguarded. Bilevel programming approach is used for the purpose. The method developed has been explained using an example.

A general assumption made in evaluation of flow network reliability is perfectly reliable nodes. Under such assumption, network reliability is evaluated through subset cut or composite path set approaches. This paper presents a simple approach to account for node failures in reliability evaluation of flow networks using subset cut approach. The proposed approach starts with generating combinations of nodes of the network and utilizes subset cuts (with perfect nodes) to find the valid additional subset cuts due to imperfect nodes. These additional subset cuts consist of only nodes or nodes-links combinations. To illustrate efficacy of the proposed approach, computational results on various benchmarks networks are provided. The proposed method is applicable to evaluate additional minimal cut sets due to imperfect nodes from minimal cut sets (with perfect nodes) for networks considering only connectivity as the success criteria or additional subset cut set for networks with multiple node pair capacity requirements.

This paper considers a production system producing multiple products alternately, and a production cycle is formed by a complete run of all products which go through the system in sequence. Two different preventive maintenance policies are studied. A framework is proposed to solve the optimal lot-size of each product together with the optimal preventive maintenance epoch which minimizes the system’s cost rate in long run.