## 1 Introduction  
Reliability evaluation is an important process to judge whether a product meets the reliability requirements in the product development (PD) process. PD is often considered as the most influential process to gain a competitive advantage in different industries [1, 2]. The modern industrial field needs to constantly update old products and introduce new ones to achieve market development; however, reliability evaluation should be done before a new product is launched onto the market. In order to evaluate the reliability of products, Bayesian inference [3], the expectation-maximization algorithm [4], MLE [5], non-linear Benard's median rank [6] and the Weibull probability plot [7] were developed. By these traditional methods, enough failure data need to be collected as the basis for reliability assessment. However, it is highly desirable to obtain a large amount of failure data on a product through extensive testing, which is very expensive, and the large amount of time and resources required makes it impractical. Limited failure data are prevalent in the industry due to the high reliability of products, which has been caused by technological advances and the testing time and budget constraints imposed by the competitive market. Risk managers need to make decisions with limited failure data [8]. NASA tested the xenon thruster through 30,352 h of testing, handling more than 495 kg of xenon propellant to validate the thruster life model and demonstrate the thruster's propellant swallowing capability [9]. While a detailed analysis of each failure is highly desirable, it is also very expensive. The significant amount of time and resources required makes it impractical [10]. In recent years, some researchers have conducted evaluations of the reliability of products in the early stage of PD. [PERSON] et al. [11] proposed a framework to evaluate the reliability of a new product in the early stage of PD and used fuzzy inference methods to combine and quantify subjective information to reflect its impact on product reliability. [PERSON], [PERSON] and [PERSON] [12] proposed a quality assurance method that incorporates risk analysis and failure analysis in the design phase of medical PD and predicted product failure and risk by the Markov chain technique. [PERSON] et al. [13] provided a comprehensive evaluation of product reliability to evaluate a new product, integrating Bernoulli data, lifetime data and degradation data. Degradation data can provide important information for reliability assessment, and evaluation based on degradation has been studied extensively [14]. Although degradation data could provide a great deal of information on reliability over a certain period, nevertheless, it is not suitable for new products with unclear or diverse failure mechanisms, such as complex systems. Expert judgment plays a relevant role in supporting the validity and scientific rigor of the reliability analysis [15], and experts can provide judgments regarding new products based on their experience and information on old products, which could be regarded as an important source for reliability evaluation. Nevertheless, experts' judgments are subjective, and the judgement of experts in various fields should be incorporated. Therefore, the further refinement of expert judgment analysis is needed to make the experts' decisions robust, but affordable and suitable for different applications [15; 16]. In addition, limited failure data represent an important foundation for reliability evaluation, and thus 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. In this paper, we only obtain limited failure data due to time and cost constraints. If quantitative failure data are inappropriate or inadequate, only qualitative data, such as expert opinions described in words, can be used to assess the safety and reliability of the system.