Design of a t‐chart using generalized multiple dependent state sampling

Abstract

In this article, a new t-chart based on generalized multiple dependent state (GMDS) sampling is proposed for efficient monitoring of a process by assuming that the time between events follows the exponential distribution. The proposed t-chart has two pair of control limits and utilizes the past sample information with the current sample information. The control chart coefficients are estimated by considering different values of the in-control average run lengths. The proposed t-chart is compared with the existing chart by using the out-of-control average run length and extra quadratic loss function. The comparison reveals that the proposed charting strategy has better shift detection ability in process mean. An illustrative example is given for the practical implementation of the proposed chart.

A Control Chart for Exponentially Distributed Characteristics Using Modified Multiple Dependent State Sampling

“…e performance evaluation of GMDS is more efficient than that of MDS. When compared with the GMDS results presented by Aslam et al [21], the performance of the proposed chart when using simulated data is investigated in this section when r 0 � 200, 300, and 370, as shown in Tables 9-11.…”

“…Additionally, Yan et al [20] developed an MDS sampling scheme based on a coefficient of variation of normally distributed quality characteristics and showed the superiority of the proposed plan over the single sampling plan. e form of MDS is then generalized by Aslam et al [21,22] to generalize multiple dependent state sampling (GMDS) for the application of variable and attribute quality characteristics. It is proven that introducing an additional parameter to MDS to form GMDS increases the monitoring sensitivity significantly.…”

Modified DS <i>np</i> Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

“…An adaptive control chart was created by Khan et al [24] for monitoring the mean using EWMA statistics under MDS sampling. Aslam et al [25] created a novel t-chart using generalized multiple dependent state (GMDS) sampling and the presumption that the time between events followed an exponential distribution. Raza et al [26] constructed a new control chart for monitoring multivariate Poisson count data under GMDS sampling.…”

Moving average control charts for the rayleigh and inverse rayleigh distributions under time truncated life test

“…Lifetime of a product is an important quality characteristic in a production process, and hence it should be monitor, control, and assure for any product. A number of lifetime distributions have been used in control chart to monitor the lifetime of product when in a production process under truncated life test, especially in structured sampling schemes 22,23 …”

“…A number of lifetime distributions have been used in control chart to monitor the lifetime of product when in a production process under truncated life test, especially in structured sampling schemes. 22,23 By exploring the statistical process control (SPC) literature, control chart for monitoring life time of a product using MA statistic has not been studied extensively, except for MA control chart for the Weibull distribution proposed by Alghamdi et al 24 Both Rayleigh and inverse RDs are important lifetime distributions which has several applications in the area of reliability and life testing studies. Motivated by the importance of these distributions and the sensitivity of the MA statistic in detecting small shifts, in this paper a time truncated attribute control chart based on MA scheme is proposed for any one of the Rayleigh and inverse RDs.…”