The output performance of the manufacturing system has a direct impact on the mechanical product quality. For guaranteeing\nproduct quality and production cost, many firms try to research the crucial issues on reliability of the manufacturing system with\nsmall sample data, to evaluate whether the manufacturing system is capable or not. The existing reliability methods depend on\na known probability distribution or vast test data. However, the population performances of complex systems become uncertain\nas processing time; namely, their probability distributions are unknown, if the existing methods are still taken into account; it\nis ineffective. This paper proposes a novel evaluation method based on poor information to settle the problems of reliability of\nthe running state of a manufacturing system under the condition of small sample sizes with a known or unknown probability\ndistribution. Via grey bootstrap method, maximum entropy principle, and Poisson process, the experimental investigation on\nreliability evaluation for the running state of the manufacturing system shows that, under the best confidence level P = 0.95, if\nthe reliability degree of achieving running quality is r > 0.65, the intersection area between the inspection data and the intrinsic\ndata is A(T) > 0.3 and the variation probability of the inspection data is Pn(T) � 0.7, and the running state of the manufacturing\nsystem is reliable; otherwise, it is not reliable. And the sensitivity analysis regarding the size of the samples can show that the size of\nthe samples has no effect on the evaluation results obtained by the evaluation method.The evaluation method proposed provides\nthe scientific decision and suggestion for judging the running state of the manufacturing system reasonably, which is efficient,\nprofitable, and organized.
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