International Journal of Reliability, Quality and Safety Engineering

As the 1988 radiocarbon dating of the Turin Shroud (TS) was debatable also from a statistical point of view, new dating methods have been proposed. This paper presents the result of an improved mechanical dating. A recent cyclic-load machine has been improved to better fix fibers under test by using a special support designed for the purpose. The mechanical behavior of linen fibers of three different ages are measured and compared discussing these results in reference to the complex structure of aged linen fibers; the three samples are a linen fiber from an Egyptian mummy of 27${}^{\mathrm{\text{th}}}$ Century B.C.; a linen fiber coming from the TS, and a recent linen fiber. This machine allowed to confirm the previous results regarding the TS mechanical dating, showing that these results are again compatible with the First Century A.D., the period in which Jesus Christ lived in Palestine. The improved machine also showed that the complex nonlinear behavior of the fibers is also due to the packing of the Secondary Cell Wall of the linen fibers, mostly composed of micro fibrils, that produces a memory-effect. The stiffening of the linen fibers with the loading increasing is a property detected for all the tested fibers that must not be forgotten also in the construction of flax fibers based composites.

This paper considers a system whose deterioration is modeled as a discrete-time and discrete-state Markov chain and is subject to randomly occurring shocks. The system is more likely to deteriorate after the occurrence of shocks. At each time epoch, we can select from one of three possible actions: operate, repair, or replace. The difference between repair and replace is whether the influence of shocks is removed or not. While repair does not change the number of shocks that the system has suffered, replacement can return it to zero. We derive an expected discounted cost for an unbounded horizon and show that a generalized control limit policy holds under certain assumptions on the costs and the transition probability. Several structural properties of the optimal maintenance policy are also investigated under different assumptions for the occurrence of shocks. These results are useful for numerically determining the optimal maintenance policy. Finally, we consider some special cases by imposing constraints on the costs and the probabilities.

Not every company can afford or is willing to invest in long-term life testing due to time and cost constraints. But such a test can provide very valuable insight about product field behavior during its lifetime. Authors of this paper present a case study on hard disk drive long-term life test data analysis. In order to deal with the non-monotonic hazard rate behavior, the bi-modal mixed life distribution and bi-modal competing failure modeling are applied to fit the time to failure data. The model parameters’ estimation, their physical implications, and field long-term reliability predictions using these two approaches are discussed and compared. The recommendations are given regarding the applicability of each model from an engineering application perspective.

At present, the cloud computing with big data is known as a next-generation software service paradigm. However, the effective methods of software reliability analysis considering the big data and cloud computing have been only few presented. In particular, it is important to consider the optimal data partitioning in terms of cloud computing with big data. Considering the cloud computing with big data, it will be useful for the software managers to estimate the total software cost in order to make allocations the optimal data area to the cloud user. We propose the method of component-oriented reliability assessment based on neural network in order to the optimal data partitioning for cloud computing with big data in this paper. Moreover, we propose the method of system-wide reliability assessment based on the jump diffusion process model considering the big data on cloud computing. Furthermore, we propose the optimal maintenance problem based on the jump diffusion model. Considering the contract cost for the maximum number of subscriber as the cloud user, we find the optimum maintenance time by minimizing the total software cost.

A stable dynamic system implies safety, reliability, and satisfactory performance. However, the determination of stability is very difficult when the system is nonlinear and when the ever present uncertainties in the components must be considered. Herein a response-based approach that uses both system and time information obtained through singular value decomposition is presented to determine the stability space of nonlinear, uncertain dynamic systems: any approximating linearization of the nonlinearities has been obviated. The approach extends previous work for linear systems that invoked only the variability of the left singular vectors to predict stability. In the new approach, the variability of the right singular vectors is augmented to that of the left singular vectors and it is shown that a simulation time span, as short as two or three periods, is sufficient to predict stability over the entire life-time dynamics rendering the method very efficient. The stability space is a subset of the design space and its robustness is proportional to the tolerances assigned to the random design variables. Errors due to sampling size, time increments, and number of singular vectors used are controllable. The method can be implemented with readily available software. A study of a practical engineering system with different tolerances and different time spans shows the efficacy of the proposed approach.