In this paper, a cumulative sum control chart scheme is designed for the detection of the outbreak of an epidemic to demonstrate the use of CUSUM chart in the non-manufacturing sector. The designed schemes are then applied on data on diabetic disease to illustrate the detection of outbreak of epidemic and also a retrospective analysis is carried out and the local means are computed.
In this article, an Isolated Truncated Chain deferred Sampling Plan for Weibull product life distribution is proposed when the testing is truncated at a specified time. This type of sampling plan is used to save the testing time. The optimal sample sizes, required for testing product quality to ascertain a true mean life are obtained under a given Maximum Allowable Percent Defective, test termination ratios and acceptance numbers. The operating characteristics formula of the proposed plan was developed. The operating characteristics and mean-ratio were used to assess the performance of the plan. The study revealed that: Weibull distribution have low failure rate; as mean life ratio increase, the failure rate reduces and the minimum sample size increase as the acceptance number, maximum allowable percent defective and experiment time ratio increases; The study concluded that the modified required minimum sample sizes were smaller compared to those in the literature making it a more economical plan to be adopted when time and cost of production is expensive and the testing is destructive.
Artificial Neural Network (ANN) which is designed to mimic the human brain have been used in the literature for identifying variable(s) that is(are) responsible for out-ofcontrol signal and the training algorithms have played a significant role in the identification of the aberrant variable(s). In this paper the effect of three algorithms in the training of ANN for pattern recognition of bivariate process is studied. Situations in which the algorithms performed satisfactorily with respect to recognition accuracy (in percentages), epochs and MSE were identified. The result of study shows that the Levenberg-Marquardt (trainlm) is the best algorithm for pattern recognition of bivariate manufacturing process in terms of recognition accuracy and the resilient backpropagation (trainrp) is best in terms of speed and mean square error performance.
SUMMARY
The paper proposes plans that improve on existing chain and deferred sampling plans.
The plans are mixed in the sense that they combine elements from both chained and deferred plans. This combination gives an operating characteristic curve closer to that of the traditional double sampling plan than either the simple chain or the simple deferred inspection plan when there is a trend in process quality. The plans require fewer items to be sampled than the traditional double sampling plans and have a delay in the decision only where there are indications that quality is poor.
The relative ease with which these plans can be made compatible with the double sampling plans in the MIL‐STD‐105D and some other similar schemes is noted.
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