International Journal of Reliability, Quality and Safety Engineering

In this paper, we discuss the operational performability evaluation model for the software-based system, introducing the concept of systemability which is defined as the reliability characteristic subject to the uncertainty of the field environment. Assuming that the software system can process the multiple tasks simultaneously and that the arrival process of the tasks follows a nonhomogeneous Poisson process, we analyze the distribution of the number of tasks whose processes can be completed within the processing time limit with the infinite-server queuing theory. Here we take the position that the software reliability characteristic in the testing phase is originally different from that in the operation phase. Then, the software failure-occurrence phenomenon in the operation phase is described with the Markovian software reliability model with systemability, i.e., we consider the randomness of the environmental factor which is introduced to bridge the gap between the software failure-occurrence characteristics during the testing and the operation phases. We derive several software performability measures considering the real-time property; these are given as the functions of time and the number of debugging activities. Finally, we illustrate several numerical examples of the measures to investigate the impact of consideration of systemability on the system performability evaluation.

The present paper proposes a multi-objective approach to find out an optimal periodic maintenance policy for a repairable and stochastically deteriorating multi-component system over a finite time horizon. The tackled problem concerns the determination of the system elements to replace at each scheduled and periodical system inspection by ensuring the simultaneous minimization of both the expected total maintenance cost and the expected global system unavailability time. It is assumed that in the case of system elements failure they are instantaneously detected and repaired by means of minimal repair actions in order to rapidly restore the system. A nonlinear integer mathematical programming model is developed to solve the treated multi-objective problem, whereas the Pareto optimal frontier is described by the Lexicographic Goal Programming and the ε-constraint methods. To explain the whole procedure, a case study is solved and the related considerations are given.

Durability and reliability performance is one of the most important concerns of ground vehicle systems, which usually experience cyclic fatigue loadings and eventually fail over time. Creep and oxidation caused damages at elevated temperature conditions further shorten the life of a system and make the life assessment even more complex. One of the key challenges posed to design engineers is to find a way to accelerate the durability tests for products with multiple failure modes and to validate designs within development cycle to satisfy customer's and market's requirements. The accelerated testing procedures for products with single failure modes have been studied for several decades and essentially well established even though some fundamental issues are still unsolved. By contrast, much is needed to do for the accelerated testing procedure of products with multiple failure modes and their interactions. In this paper, a new accelerated testing and data analysis procedure suitable for products with linear homoscedastic data pattern is proposed. Examples related to durability life of high temperature components with single failure modes such as fatigue, creep, and oxidation are provided to demonstrate the procedure developed. Durability life assessment of components with multiple failure modes is also investigated and demonstrated with creep-fatigue and fatigue-oxidation damage analyses.

Cox's proportional hazard model is potentially the most used method in life data and survival analysis. Although the method is relatively simple to understand, its major difficulties in estimation are observed when time-dependent covariates with repeated events are used as input variables. An alternative approach to simplify hazard ratio prediction for repeated events is evaluated for a practical application using small appliances reliability data from an accelerated experimental design based on consumer usage.

Ocean turbines are a promising new source of clean energy, but their remote and inhospitable environment (the open ocean) poses reliability challenges. Machine condition monitoring/prognostic health monitoring (MCM/PHM) systems assure the reliability of these turbines by detecting and predicting machine state. These MCM/PHM systems use sensor data (such as vibration information) to determine whether or not the machine is operating properly. However, not all sensor data corresponds to the machine state: some portions of the sensor signal are influenced by certain environmental conditions which do not directly relate to machine health. Therefore, models must be built which can detect system state regardless of these environmental operating conditions. The proposed baseline-differencing approach permits this by creating a baseline for different conditions (such that each baseline represents what the normal, healthy machine state looks like while in that operating condition) and using the difference of the observed data and this baseline to train and evaluate models. We present two case studies, both conducted on data from a dynamometer representing an ocean turbine, to demonstrate the improved predictive capabilities of models which incorporate baseline-differencing, compared to the models which use the nonbaselined data. The results show that significantly more high-quality models can be built with baseline-differencing.