Building Up of Fuzzy Evaluation Model of Life Performance Based on Type-II Censored Data

Abstract

The semiconductor industry is a rapidly growing sector. As collection technologies for production data continue to improve and the Internet of Things matures, production data analysis improves, thus accelerating progress towards smart manufacturing. This not only enhances the process quality, but also increases product lifetime and reliability. Under the assumption of exponential distribution, the ratio of lifetime and warranty has been proposed as a lifetime performance index for electronic products. As unknown parameters of the index, to use point estimates to assess lifetime performance may cause misjudgment due to sampling errors. In addition, cost and time limitations often lead to small sample sizes that can affect the results of the analysis. Type-II censored data are widely applied in production and manufacturing engineering. Thus, this paper proposes an unbiased and consistent estimator of lifetime performance based on type-II censored data. The 100(1 − α)% confidence interval of the proposed index is derived based on its probability density function. Overly small sample sizes not only make the length estimates of lifetime performance index intervals for electronic products too long, but they also increase sampling errors, which distort the estimation and test results. We therefore used the aforementioned interval to construct a fuzzy test model for the assessment of product lifetime and further help manufacturers to be more prudent and precise to evaluate the performance of product life cycles. A numerical example illustrates the applicability of the proposed model.

Fuzzy Evaluation Model of Machining Process Loss

“…Consequently, statistical tests will vary due to the sample size, such that inconsistent decisions will be generated. As a result, this study first derived a 100 × (1 − α)% confidence interval of δ, as well as developed a confidence interval-based fuzzy test for this index [13]. Next, this study derived a 100% confidence interval of the process expected loss, as well as established a confidence intervalbased fuzzy test for the process expected loss.…”

“…As noted by some studies, the α-cuts of the triangular fuzzy number θL is illustrated as follows [10,13]:…”

“…In addition, companies emphasize the mechanism of quick responses, and-given costs and timeliness-usually the sample size is not big. Accordingly, the sample size will make statistical tests vary, and then inconsistent decisions will be made [11,[13][14][15]]. In addition, many studies have pointed out that the confidence interval-based fuzzy testing model can incorporate past accumulated data with experts' experiences, so the precision of the test can be maintained in the state of small samples [10].…”

“…In addition, many studies have pointed out that the confidence interval-based fuzzy testing model can incorporate past accumulated data with experts' experiences, so the precision of the test can be maintained in the state of small samples [10]. Furthermore, according to the studies mentioned in the above literature [8,9,13], fuzzy tests based on confidence intervals collect data with real numbers instead of fuzzy numbers, which are relatively simple and easy to collect. Therefore, in this paper, we propose a confidence interval-based fuzzy testing model for index θ L .…”

Fuzzy Decision-Making and Resource Management Model of Performance Evaluation Indices

“…Furthermore, this model takes the prioritization of service item improvements into account, offering a basis for decision making when resources are limited. Lastly, with standardized decision-making values and critical values, compared with other existing methods based on fuzzy tests and confidence intervals, this model is more convenient for decision makers [30,31].…”