International Journal of Reliability, Quality and Safety Engineering

In this paper, a new continuous sampling plan is introduced whose sampling inspection phase is characterized by the maximum allowable number of nonconforming units c₁ and c₂ and the sampling rates f₁ and f₂ and is designated as CSP-(C₁, C₂). A Markov chain model for the proposed plan is formulated and certain common measures of performance are derived. The continuous sampling plan of type CSP-C developed by Govindaraju and Kandasamy¹² is a special case of this new sampling plan. Moreover, Dodge's continuous sampling plan (CSP-1) also becomes a special case of proposed plan. The advantage of the proposed plan is to achieve a reduction in the average fraction inspected at good quality levels compared with the CSP-1 and other continuous sampling plans.

Knowing when a process has changed would simplify the search for and identification of the special cause. In this paper, we compare the maximum likelihood estimator (MLE) of the process change point (that is, when the process changed) to built-in change point estimators from binomial CUSUM and EWMA control charts. We conclude that it is better to use the maximum likelihood change point estimator when a CUSUM or EWMA control chart signals a change in the process fraction nonconforming. The results show that the MLE provides process engineers with an accurate and useful estimate of the last subgroup from the unchanged process.

This paper develops a mathematical model to derive the optimal preventive maintenance warranty (PMW) policy for repairable products with age-dependent maintenance costs. Under a PMW, any product failures are rectified by minimal repair, and additional preventive maintenance actions are carried out within the warranty period. When the costs for preventive maintenance and minimal repair are age-dependent, the optimal number of preventive maintenance actions, corresponding maintenance degrees, and the maintenance schedule for designing a PMW policy are derived here such that the expected total warranty cost is minimized. Under some reasonable conditions, we show that there exists a unique optimal PMW policy in which the product is maintained periodically with the same preventive maintenance degree. Using this property, an efficient algorithm is provided to search for the optimal policy. Some related models developed in the literature are discussed and these models are in fact special cases of the model proposed in this paper. Furthermore, when the life-time distribution of a product is Weibull, a closed-form expression of the optimal policy is obtained. Finally, the impact of providing preventive maintenance is evaluated through numerical examples.

The multivariate Hotelling's T² control chart is designed to be used in a mass production for processes where data to estimate the mean vector and covariance matrix as well as the computation of control limits are available before a production run. Recent years have seen a trend in manufacturing industries to produce smaller lot sizes, a.k.a., low volume production which is a result of increased importance given to just-in-time (JIT) manufacturing techniques, synchronous manufacturing and the reduction of in-process inventory and costs. This new manufacturing environment is also referred to as short runs production or short runs. In a short runs environment, it is difficult or perhaps impossible to establish a reliable historical data set in setting valid control limits and in estimating process parameters due to the availability of insufficient data for a particular process because production runs are usually short and change frequently from one process to another. There is also a need to start charting at or very near the beginning of the run in such a case. Another problem encountered in a short runs production such as in job shops is that there are many different types of measurements so that many different control charts are needed. Standardized control charts that allow different statistics to be plotted on the same chart are extremely useful in short runs. Control charts with standard scale simplify the control charting process in a short runs environment. In this paper, we address the multivariate short runs problems for process dispersion based on individual measurements by presenting the required formulas so that the chart can be used from the start of production, whether or not prior information for estimating the chart's limit and its parameter is available. The proposed chart plots standardized statistics for multiple parts on the same chart. This paper extends the work of the authors in Ref. 13.

In a production process, when quality of the product depends on more than one characteristic, 'Multivariate Quality Control' (MQC) techniques are efficiently used. Many MQC techniques have been developed to control the multivariate variable processes, but no much work has been reported to control the multivariate attribute processes.

In this article, to detect a change in the vector of fraction non-conforming, we develop 'Likelihood Ratio based Multi-Attribute Control Chart' (LR-MACC) using the exact joint distribution and the LR-test under multinomial setup. It is verified that, in some situations, LR-MACC is superior to the MNP chart proposed by Lu et al.⁷

When MACC gives a signal, the attributes responsible are not readily identifiable. Therefore, a procedure to detect the responsible attributes is also developed.

A hierarchical uncertainty model of reliability is studied when a type of probability distributions of the component times to failure is known, but their parameters or some of parameters are defined by experts with corresponding degrees of belief. Two methods for computing the reliability measures are developed and analyzed from their practical application point of view. A procedure of updating the beliefs after observing a failure is investigated. Numerical examples illustrating the proposed model are given.