In this issue, we bring to our readers some new upcoming ideas and interesting areas of research, which it is hoped will be of interest to our readers. This issue covers two reviews of recent books and 10 regular papers covering all several areas of performability. There are two important papers from Professor Aven of Stavanger University of Norway in the area of risk including the first paper in which, the authors demonstrate how conceptual pragmatism can be used as a suitable framework for probability models which can be used in risk assessment.

In the second paper of this issue from University of Toronto, Canada, the authors provide a wavelet transformation approach to detect early failure of the gear shaft, which can be of great help in fault detection and diagonostic anlysis of gear transmission systems. This technique can be of immense value to reliability and maintenance engineers.

The third paper of the issue is from Stavanger University, Norway, and discusses an important issue of the best available technique (BAT) in fulfilling the European Union's Integrated Pollution Prevention and Control (IPPC) directive towards sustainability considerations. The paper employs the criteria of life cycle costs as the tool in arriving at the best available technique from the several technically viable alternatives of system design, particularly at the initial stage of a project. A case study related to the selection of power supply systems for offshore oil and gas installations illustrates the method.

The fourth paper of this issue is from France and presents a methodology that helps designing a monitoring system particularly in cases where a model does not exist or it is difficult to find an analytical relationship between observed features of the system to identify the system state. The methodology enables to define and extend as much as possible the regions of feature space where a trained monitoring system can operate safely and precisely according to performance requirements. If the system experiences a situation which is characterized by features outside the trained region, the monitoring system is simply disabled and data may be recorded to update the monitoring system. The proposed methodology is applied to aircraft turbine start capability monitoring system to determine the extent of the feature space in which the monitoring system can operate. Some experiments on the application are also carried out.

The fifth paper of the issue is from Brazil and the authors present a case study on verification of time to breakdown of a new insulating fluid one of the objective of the paper is to verify if times to breakdown of insulating fluid between electrodes recorded at three different voltages have an exponential distribution as predicted by theory. The other objective o the paper is if the Eyring's acceleration model of can provide shape and scale parameters for an underlying Inverse Weibull model, obtained under two accelerating conditions. The Inverse Weibull model has been used in Bayesian reliability estimation to represent the information available about the shape parameter of an underlying Weibull sampling distribution.

The sixth paper of this issue is from three authors from Sweden, U.K. and Turkey, respectively and addresses an important problem of failure of railways turnouts, which may adversely affect the system availability, safety, operating and support costs. The paper presents a diagnostics method for 'drive-rod out-of-adjustment' failure mode - one of the most frequently observed failure modes. SVM with Gaussian kernel is used for failure classification. In addition, results of the feature selection with statistical t-test and feature reduction with principal component analysis are compared in the paper.

The seventh paper of the issue is from authors from Latvia and is a review of the authors' interesting previous work related to the analysis of tensile strength of uni-directionally fiber-reinforced composite material, considered as a series of parallel systems with defects. Additionally, a specific model is studied based on an assumption that only failure of the damaged parts of a specimen can take place. A version of the Poisson distribution is used for probability mass function of the number of defects. It is claimed by the authors that the proposed models allow estimating the probability of the presence of defects in order to clarify improvements of the production technology needed to increase reliability, and to predict the scale effect of tensile strength of the composite. Strength test data of different composite materials are processed and the results analyzed. A numerical comparison of different models is provided in the paper.

The eighth paper of this issue is again from Norway and discusses an analytic approach when using event trees and fault trees in a quantitative risk assessment context. The basic question raised is when to introduce probability models and frequentist probabilities (chances) instead of using direct probability assignments for the events of the trees. The author argues that such models should only be used if the key quantities of interest of the risk assessment are frequentist probabilities and when systematic information updating is important for meeting the aim of the analysis. An example of an event tree related to the analysis of an LNG (Liquefied Natural Gas) plant illustrates the analysis and discussion.

The ninth paper of the issue is from DRDO, India and presents a case study on vehicles used in the field. The authors use Artificial Neural Networks (ANN) approach for assessing reliability/ availability of vehicles. The methodology adopted is demonstrated with the help of a case study which includes collection, sorting and grouping of vehicle failure data. Then distribution parameters are estimated and best fitting probability distribution is identified for predicting vehicle reliability. Subsequently, the trained ANN (using SLP model) is used to predict the vehicle reliability. Suitability of a RDBMS (Oracle) for training ANN and predicting vehicle reliability is also presented. The developed methodology has been able to predict reliability of vehicle very close to its observed values.

The tenth paper of this issue is again from India and presents a case study on availability analysis of a bubble gum producing system. The paper develops a model which then used to assess availability using Markov method.

The use of probabilistic models, as in Bayesian analysis, assumes some sort of model stability: populations of similar units need to be constructed. But such stability is often not fulfilled. An extended approach is thus required, and a framework for such an approach is conceptualistic pragmatism which links the probabilistic analysis with knowledge theory and the quality movement with its focus on continuous improvement. In this paper we restrict attention to the applications of probabilistic models in a risk assessment context. The purpose of the paper is to investigate how conceptualistic pragmatism may work as a suitable framework for such an extended approach in a risk assessment context. The key to make the framework operational is to perform broad analyses of uncertainties.

Fault detection and diagnosis of gear transmission systems have attracted considerable attention in recent years, but there are very few papers dealing with the early detection of shaft cracks. In this paper, an approach to gear shaft fault detection based on the application of the wavelet transform to both the time synchronously averaged (TSA) signal and residual signal is presented. The autocovariance of maximal energy coefficients based on the wavelet transform is first proposed to evaluate the gear shaft fault advancement quantitatively. For a comparison, the advantages and disadvantages of some approaches such as using standard deviation, kurtosis and the application of the Kolmogorov-Smirnov test (K-S test), used as fault indicators with continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for residual signal, are discussed. It is demonstrated using real vibration data that the early faults in gear shafts can be detected and identified successfully using wavelet transforms combined with the approaches mentioned above.

Frequent technological advancements demand extended qualification processes for newly introduced techniques. In this context, European Union’s Integrated Pollution Prevention and Control (IPPC) directive provides a backbone for evaluating Best Available Technique (BAT) and making sustainable decisions. The selection of BAT is a challenging process, as frequent technological advancements result in numerous innovative solutions. This manuscript illustrates the use of life cycle costing (LCC) as a tool for selecting the best available and qualified technique (BAQT) from several technically viable alternatives. An industrial case study is carried out to demonstrate how to use LCC effectively while comparing two alternatives in initial phase of a project. The manuscript also illustrates how to integrate health, safety and environmental (HSE) aspects in parallel with LCC principles and make sustainable decisions.

The presented system monitors a turbofan start sequence using indicators and operating conditions to detect abnormal behavior. It is based on the analysis of the residuals between the measured indicator values and the corresponding estimated values assuming healthy state. Estimation uses regression models trained on a database. However, as in many monitoring problems, the amount of data is limited due to application issues and covers only a limited region of the feature space. Thus, the models are trained in a limited domain defined implicitly by the available learning data and their efficiency is not controlled outside this implicit domain. This paper deals with the definition and the extension of the models validity region while keeping the extension effect on the monitoring process under control. A methodology based on one-class SVM is proposed and is applied to the presented monitoring system. Practical and methodological conclusions are drawn from the proposed experiments.

In this paper the test purpose will have two objectives: First will be to verify if times to breakdown of insulating fluid between electrodes recorded at three different voltages have an exponential distribution as predicted by theory. Second will be to assess whether or not the accelerated model proposed by Eyring will be able to translate results for the shape and scale parameters of an underlying Inverse Weibull model, obtained under two accelerating using conditions, to expected normal using condition results for these two parameters. The product being analyzed is a new type of insulate fluid, and the accelerating factor is the voltage stresses applied to the fluid at two different levels (30KV and 40KV). The normal operating voltage is 25KV and it was possible to test the fluid at normal voltage using condition. Both results for the two parameters of the Inverse Weibull model, obtained under normal using condition and translated from accelerated using conditions to normal conditions, will be compared to each other to assess the accuracy of the Eyring model when the accelerating factor is only the voltage stress.

Railway turnout systems are one of the most critical pieces of equipment in railway infrastructure. Early identification of failures in turnout systems is important to obtain increased availability and safety, and reduced operating and support costs. This paper aims to develop a method to identify ‘drive-rod out-of-adjustment’ failure mode, one of the most frequently observed failure modes. Support Vector Machine (SVM) with Gaussian kernel is used for diagnosis. In addition, the results of feature selection with statistical t-test and feature reduction with principal component analysis (PCA) are compared in the paper.

This paper is a review of the authors’ previous work devoted to the analysis of tensile strength of unidirectionally fiber-reinforced composite material, considered as a series of parallel systems with defects. Additionally, a specific model is studied based on an assumption that only failure of the damaged parts of a specimen can take place. A version of the Poisson distribution is used for probability mass function of the number of defects. The proposed models allow estimating the probability of the presence of defects in order to clarify improvements of the production technology needed to increase reliability, and to predict the scale effect of tensile strength of the composite. Strength test data of different composite materials are processed and the results analyzed. A numerical comparison of different models is provided.

This paper discusses the analysis approach when using event trees and fault trees in a quantitative risk assessment context. The basic question raised is when to introduce probability models and frequentist probabilities (chances) instead of using direct probability assignments for the events of the trees. We argue that such models should only be used if the key quantities of interest of the risk assessment are frequentist probabilities and when systematic information updating is important for meeting the aim of the analysis. An example of an event tree related to the analysis of an LNG (Liquefied Natural Gas) plant illustrates the analysis and discussion.

ANNs are usually very effective as computational tools and have found extensive utilisation in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance besides its learning and generalisation capabilities. The aim of this paper is to familiarise with ANN-based computing (neuro-computing). The predicted and observed vehicle reliability using trained ANN is very close as compared to Weibull probability distribution. The methodology adopted is demonstrated with the help of a case study which includes collection, sorting and grouping of vehicle failure data. Then distribution parameters are estimated and best fitting probability distribution is identified for predicting vehicle reliability. Subsequently the trained ANN (using SLP model) is used to predict the vehicle reliability. Suitability of a RDBMS (Oracle) for training ANN and predicting vehicle reliability is also presented. The developed methodology has been able to predict reliability of vehicle very close to its observed values.

This paper develops a mathematical model of a complex bubble gum production system with an attempt to improve its availability. The methodology for determining the availability of the system is based on Markov Modeling. The mathematical model is established using probability considerations and supplementary variable technique. Lagrange’s method for partial differential equations is utilized to obtain the state probabilities. The reliability characteristics are evaluated and analyzed in accordance with practical situation and operational behavior of the system. Availability analysis of the system has helped in identifying the critical factors and assessing their impact on the system availability. I is possible to achieve an improvement in system availability by 3.27 per cent.