International Journal of Reliability, Quality and Safety Engineering

Rolling Contact Fatigue (RCF) is a major cause of rail failure leading to replacement of rails. RCF defects are caused by a combination of high normal and tangential stresses between the wheel and rail. In this study, a methodology has been proposed to evaluate P-F (Potential failure-functional failure) interval of RCF defects based on RCF defect rate distribution and fatigue design life distribution. For estimating fatigue design life distribution, load under variable amplitude has been considered which is a case in the mixed traffic scenario. Stochastic S-N curve has been considered to account for probabilistic nature of fatigue life. The stress history can be calculated from the actual load history. RCF defect distribution can be estimated from the probability density function of the defects from the actual field data. The proper estimation of P-F interval will help the infrastructure managers to define rail inspection interval scientifically.

Models that describe the failure phenomenon and consequent enhancement in reliability due to fault removal are termed as Software Reliability Growth Models (SRGM). As the size of software system is large and the number of faults detected during the testing phase becomes large, so the change of the number of faults that are detected and removed through each debugging becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. In such a situation, we can model the software fault detection process as a stochastic process with continuous-state space. Several continuous-state space SRGM based on stochastic differential equations of type to assess software reliability for large scale software systems have been proposed in the literature so far. However these continuous-state space SRGM have seldom taken the effect of testing-effort into consideration. The resources, such as manpower used for fault detection/removal, number of executed test-cases and CPU hours spent in executing software under test, are well-known as one of the most important factors related to the software reliability growth process. Some of the existing SRGM define errors of different severity. Severity of a failure or fault is the impact it has on the operation of a software-based system. Different faults may require different amount of testing efforts and testing strategy for their removal from the software. The aim of this paper is to determine the type of faults and their proportion present in the software. We have also assumed that learning of removal team grows as testing progresses due to experience and have incorporated the logistic removal rate during modeling of different types of faults with testing-effort by applying a mathematical technique of stochastic differential equations of type. Finally, a goodness-of-fit comparison between proposed models and existing continuous-state space SRGM using stochastic differential equations has been conducted.

The false alarm probability (FAP) is the metric typically used to design and evaluate the performance of Phase I control charts. It is shown that in situations where the exact or the asymptotic joint p.d.f. of the standardized charting statistics follows a singular standard multivariate normal distribution with a common negative correlation, the FAP of some Shewhart-type Phase I charts for the mean can be expressed as a multiple integral of the joint p.d.f. Hence the required charting constants can be calculated (and the control chart can be implemented) by evaluating this integral. A table with the charting constants is provided for some popular choices of the nominal FAP (denoted FAP₀) and the number of Phase I samples, m. The proposed methodology is useful to unify some existing Phase I charts and is illustrated with two charts from the literature: the p chart for the fraction nonconforming and the chart for the mean. A summary and some conclusions are provided.

Computer based systems have increased dramatically in scope, complexity, pervasiveness. Most industries are highly dependent on computers for their basic day to day functioning. Safe & reliable software operations are an essential requirement for many systems across different industries. The number of functions to be included in a software system is decided during the software development. Any software system must be constructed in such a way that execution can resume even after the occurrence of failure with minimal loss of data and time. Such software systems which can continue execution even in presence of faults are called fault tolerant software. When failure occurs one of the redundant software modules get executed and prevent system failure. The fault tolerant software systems are usually developed by integrating COTS (commercial off-the-shelf) software components. The motivation for using COTS components is that they will reduce overall system development costs and reduce development time. In this paper, reliability models for fault tolerant consensus recovery blocks are analyzed. In first optimization model, we formulate joint optimization problem in which reliability maximization of software system and execution time minimization for each function of software system are considered under budgetary constraint. In the second model the issue of compatibility among alternatives available for different modules, is discussed. Numerical illustrations are provided to demonstrate the developed models.

This paper, proposes the Multi-Criteria Decision Making (MCDM) methodology for selection of a maintenance strategy to assure the consistency and effectiveness of maintenance decisions. The methodology is based on an AHP-enhanced TOPSIS, VIKOR and benefit-cost ratio, in which the importance of the effectiveness appraisal criteria of a maintenance strategy is determined by the use of AHP. Furthermore, in the proposed methodology the different maintenance policies are ranked using the benefit-cost ratio, TOPSIS and VIKOR. The method provides a basis for consideration of different priority factors governing decisions, which may include the rate of return, total profit, or lowest investment. When the preference is the rate of return, the benefit-cost ratio is used, and for the total profit TOPSIS is applied. In cases where the decision maker has specific preferences, such as the lowest investment, VIKOR is adopted. The proposed method has been tested through a case study within the aviation context for an aircraft system. It has been found that using the methodology presented in the paper, the relative advantage and disadvantage of each maintenance strategy can be identified in consideration of different aspects, which contributes to the consistent and rationalized justification of the maintenance task selection. The study shows that application of the combined AHP, TOPSIS, and VIKOR methodologies is an applicable and effective way to implement a rigorous approach for identifying the most effective maintenance alternative.

The importance of Software Reliability Growth Models to control the testing process and for quantitative assessment of software reliability is a well established fact. However, difficulties created by their underlying assumptions, their relevance and validity to real testing environment have made the selection of appropriate model an uphill task. Recently, new dimensions have been added to software reliability engineering with the development of unified modeling schemes. These schemes have proved seminal in the development of the general theory, partially because of their simplicity and mathematical tractability. In this paper, we propose a unified scheme for discrete software reliability growth modeling using the fault detection/correction rate function. Standard probability distributions have been used to model the fault correction and detection times. Initially, we have formulated the unified scheme when the fault correction is immediate to the failure observation and later we extend it to the cases where removal is a two stage process namely failure observation followed by fault detection/correction. The use of fault detection/correction rate or else known as hazard rate to represent stage wise removal of fault during testing highlights the importance of the proposed framework. Convolution of probability distribution functions has been used to represent Stage-wise removal of fault i.e. failure observation, fault detection/fault correction. Few Discrete models have been derived using the proposed methodology. Parameter estimation on two real software failure datasets has been worked out. The results obtained are fairly accurate and quite encouraging.

Quality of a software product should be tracked during the software lifecycle right from the architectural phase to its operational phase. Heterogeneous systems consist of several globally distributed components, thus rendering their reliability evaluation more complex with respect to the conventional methods. The objective of our work is to expand the evaluation process for effective reliability analysis by using both white box and black box approaches at prototype and at module/component level before the actual development. In this paper the, Black box testing is based on non-functional requirements for early quantitative analysis for the reliability estimation of the application development based on the output results of the prototype development. White box testing is based on inter-component interactions which deal with probabilistic software behavior. It uses an internal perspective of the system to design test cases based on internal structure at requirements and design phases. This paper has been applied for evolution of effective reliability quantification analysis at prototype level of a financial application case study with both non functional test data of software Development Life cycle (SDLC) phases captured from defect consolidation table in the form orthogonal defect classification as well functional requirements at requirement and design phases captured through software architectural modeling paradigms.

In this paper, beginning with a concise overview of the Available Transfer Capability (ATC) evaluation methods, we make a proposition for reliability management in the planning horizon of deregulated power systems through the concept of Adequacy Resiliency. The derived indices are meant as indicators of adaptability of power systems to ensure the required reliability levels. Improvements to this conceptualization upon the deployment of Flexible AC Transmission System (FACTS) devices are then put forward. We also explore the option of employing the created indices to the operational horizon of power systems, explaining the means of market enhancement. Core reliability issues arising out of the usage of FACTS are then discussed.