Abstract
In the early stage of product development, reliability evaluation is an indispensable step before launching a product onto the market. It is not realistic to evaluate the reliability of a new product by a host of reliability tests due to the limiting factors of time and test costs. Evaluating the reliability of products in a short time is a challenging problem. In this paper, an approach is proposed that combines a group of experts’ judgments and limited failure data. Novel features of this approach are that it can reflect various kinds of information without considering the individual weight and reduces aggregation error in the uncertainty quantification of multiple inconsistent pieces of information. First, an expert system is established by the Bayesian best–worst method and fuzzy logic inference, which collects and aggregates a group of expert opinions to estimate the reliability improvement factor. Then, an adaptive Bayesian melding method is investigated to generate a posterior by inaccurate prior knowledge and limited test data; this method is made more computationally efficient by implementing an improved sampling importance resampling algorithm. Finally, an application for the reliability evaluation of a subsystem of a CNC lathe is discussed to illustrate the framework, which is shown to validate the reasonability and robustness of our proposal.
Highlights
Reliability evaluation is an important process to judge whether a product meets the reliability requirements in the product development (PD) process
There are several limitations of these methods: (i) experts’ judgments are qualitative information, and we need to convert this information into quantitative information; (ii) a group of experts must be invited to evaluate the reliability of new products, and integrating these expert opinions to accurately reflect the advice of experts is a challenge; (iii) a suitable prior should be selected for Bayesian inference.; and (iv) a simulation method must be investigated to calculate the posterior of Bayesian inference
An expert system is established to obtain expert evaluations regarding product reliability, which contains a collection of expert-level knowledge and experience in a field and is capable of utilizing human expert knowledge and problem-solving methods to evaluate the reliability of a new product
Summary
Reliability evaluation is an important process to judge whether a product meets the reliability requirements in the product development (PD) process. Limited failure data represent an important foundation for reliability evaluation, and a well-established framework or methodology is necessary to develop that integrates a variety of expert information combined with other prior information to evaluate the reliability of a new product in the early stage of PD. An adaptive Bayesian melding (ABM) method is investigated and modified to integrate experts’ judgments and limited test data, and a modified sampling importance resampling algorithm is employed to obtain the posterior distribution. This framework is applied to the evaluation of a new type of servo turret, which is an important subsystem of a CNC lathe.
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