International Journal of Reliability, Quality and Safety Engineering

Shewhart control charts based on the simulation method are proposed for monitoring separate variance components of the single-factor random effect model. Monte Carlo simulation results show that the proposed control charts have competitive performance relative to the approximate Shewhart regression control charts (ARSCCs) proposed by Chang and Gan² in terms of the average run length (ARL). Compared with the ARSCCs, our control charts can be constructed easily with less sample resources. The application of our proposed method is illustrated with two examples.

When monitoring the process variability, it is a common practice that a Phase I data set is used to estimate the unknown in-control process standard deviation σ₀ or variance to set up the control limits, then monitoring proceeds. Once the process is considered to be in-control, the estimated control limits are assumed as fixed. This practice ignores the effect of estimating the unknown in-control process variance . In this paper, we derive the exact run length distribution of the S² control chart when the in-control process variance is estimated and find that m = 200 or more Phase I samples are needed to neglect the effect of using estimated control limits. New control limits when m is small are also derived.

This paper provides an approach for assessing the uncertainty associated with the estimate of the availability of a two-state repairable system. During the design stage it is often necessary to allocate scarce testing resources among various components in an efficient manner. Although there are a variety of importance and uncertainty measures for the reliability of a system, there are limited measures for systems availability. This study attempts to fill the gaps on availability importance measures and provide insights for techniques to reduce the variance of a system-level availability estimate efficiently. The variance importance measure is constructed such that it provides a measure of the improvement in the variance of the system level availability estimate through the reduction of the variance of the various component availability estimates. In addition, a cost model is developed that trades-off cost and uncertainty. The measure is illustrated for five common system structures. Monte Carlo Simulation is used to illustrate the use of the assessment tools on a specific problem. Observations conclude that results are consistent with reliability importance measures.

The current study uses reliability models for the improvement of the operation of a ship's "bilge — water separator" system. A "bilge-water separator" is a mechanism which cleans and inspects the ship's bilge water before it is discharged into the sea. Homogeneous continuous time Markov models have been used to record and estimate possible hazards and system failures in two different operational scenarios. If the photocell unit of the system fails, the ship may cause severe sea pollution. This study attempts to estimate the probability of sea pollution based on empirical data. In addition, the results of the model are compared with those of a system in which a second metering unit is added in an effort to to find out if this alteration improves the systems' efficiency.

Finding techniques to reduce software developmental effort and produce highly reliable software is an extremely vital goal for software developers. One method that has proven quite useful is the application of software metrics-based classification models. Classification models can be constructed to identify faulty components in a software system with high accuracy. Significant research has been dedicated towards developing methods for improving the quality of software metrics-based classification models. It has been shown in several studies that the accuracy of these models improves when irrelevant attributes are identified and eliminated from the training data set.

This study presents a rough set theory approach, based on classical set theory, for identifying and eliminating irrelevant attributes from a training data set. Rough set theory is used to find small groups of attributes, determined by the relationships that exist between the objects in a data set, with comparable discernibility as larger sets of attributes. This allows for the development of simpler classification models that are easy for analyst to understand and explain to others.

We built case-based reasoning models in order to evaluate their classification performance on the smaller subsets of attributes selected using rough set theory. The empirical studies demonstrated that by applying a rough set approach to find small subsets of attributes we can build case-based reasoning models with an accuracy comparable to, and in some cases better than, a case-based reasoning model built with a complete set of attributes.