International Journal of Reliability, Quality and Safety Engineering

This paper deals with the modeling and computation of in-service aircraft reliability at the preliminary design stage. This problem is crucial for aircraft designers because it enables them to evaluate in-service interruption rates, in view of designing the system and of optimizing aircraft support. In the context of a sequence of flight cycles, standard reliability methods are not computationally conceivable with respect to industrial timing constraints. In this paper, first we construct the mathematical framework of in-service aircraft reliability. Second, we use this model in order to demonstrate recursive formulae linking the probabilities of the main failure events. Third, from these analytic developments, we derive relevent reliability bounds. We use these bounds to design an efficient algorithm to estimate operational interruption rate indicators. Finally, we show the usefulness of our approach on real-world cases provided by Airbus.

There has been a lack of attention to the subject of risk management in the design and operation of software. This is strange because the risk to reliability is a critical problem in attempts to achieve a safe operation of the software. To address this problem, we evaluate existing models and introduce a new model for software risk prediction. The new model — cumulative failures gradient function — is based on the principles of neural networks. This metric identifiers the minimum test time required to achieve maximum improvement in software quality. We used three NASA Space Shuttle software systems in the evaluation of both existing and new models. The results showed that it was not possible to consistently rank these systems because the validity of the risk predictions varied depending on the risk model that was used. Therefore, the results suggest that it is advisable to use a variety of models to comprehensively evaluate the software risk.

We examine two open engineering problems in the area of testing and formal verification of internet-enabled service oriented architectures (SOA). The first involves deciding when to formally and exhaustively verify versus when to informally and non-exhaustively test. The second concerns scalability limitations associated with formal verification, to which we propose a semi-formal technique that uses software agents. Finally, we assess how these findings can improve current software quality assurance practices.

Addressing the first problem, we present and explain two classes of tradeoffs. External tradeoffs between assurance, performance, and flexibility are determined by the business needs of each application, whether it be in engineering, commerce, or entertainment. Internal tradeoffs between assurance, scale, and level of detail involve the technical challenges of feasibly verifying or testing an SOA. To help decide whether to exhaustively verify or non-exhaustively test, we present and explain these two classes of tradeoffs.

Identifying a middle ground between testing and verification, we propose using software agents to simulate services in a composition. Technologically, this approach has the advantage of assuring the quality of compositions that are too large to exhaustively verify. Operationally, it supports testing these compositions in the laboratory without access to source code or use of network resources of third-party services. We identify and exploit the structural similarities between agents and services, examining how doing so can assure the quality of service compositions.

This paper addresses the redundancy allocation problem of multi-state series-parallel reliability structures where each subsystem can consist of maximum two types of redundant components. The objective is to minimize the total investment cost of system design satisfying system reliability constraint and the consumer load demand. The demand distribution is presented as a piecewise cumulative load curve. The configuration uses the binary components from a list of available products to provide redundancy so as to increase system reliability. The components are characterized by their feeding capacity, reliability and cost. A system that consists of elements with different reliability and productivity parameters has the capacity strongly dependent upon the selection of components constituting its structure. An ant colony optimization algorithm has been presented to analyze the problem and suggest an optimal system structure. The solution approach consists of a series of simple steps as used in early ant colony optimization algorithms dealing with other optimization problems and still proves efficient over the prevalent methods with regard to solutions obtained/computation time. Three multi-state system design problems have been solved for illustration.

This article presents a practical approach to reliability assessment of a complex safety instrumented system that is susceptible to common cause failures. The approach is based on fault tree analysis where the common cause failures are included by post-processing the minimal cut sets. The approach is illustrated by a case study of a safety instrumented function of a workover control system that is used during maintenance interventions into subsea oil and gas wells. The case study shows that the approach is well suited for identifying potential failures in complex systems and for including design engineers in the verification of the reliability analyses. Unlike many software tools for fault tree analysis, the approach gives conservative estimates for reliability. The suggested approach represents a useful extension to current reliability analysis methods.