In current studies of the belief rule base (BRB) model, the attributes are assumed to be fully reliable and the observation data are directly used as input. However, in engineering practice, the observation data may be affected by some disturbance factors, including the quality of the sensors and noise in the environment. Then, the reliability of observation data may be affected and the modeling accuracy of the BRB is therefore influenced. As such, a new BRB model with attribute reliability (BRB-r) is proposed in this paper. In particular, a calculation method of attribute reliability is given based on the statistical method. Moreover, to integrate the attribute reliability into the BRB-r, a new calculation method of matching degree is developed. The model's overall reliability denotes its ability to provide the correct result. When the attributes are unreliable, the overall reliability of the BRB-r is degraded. Thus, a calculation method for the overall reliability of the BRB-r is developed to support decision-making in engineering practice. A case study of the safety assessment of a diesel engine is conducted to demonstrate the efficiency of the proposed BRB-r model.

中文翻译：

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一种新的具有属性可靠性的信念规则库模型

在当前对信念规则库 (BRB) 模型的研究中，假设属性是完全可靠的，并且观察数据直接用作输入。然而，在工程实践中，观测数据可能会受到一些干扰因素的影响，包括传感器的质量和环境中的噪声。进而影响观测数据的可靠性，进而影响BRB的建模精度。为此，本文提出了一种新的具有属性可靠性的 BRB 模型（BRB-r）。特别地，给出了一种基于统计方法的属性可靠性计算方法。此外，为了将属性可靠性整合到BRB-r中，开发了一种新的匹配度计算方法。模型的整体可靠性表示其提供正确结果的能力。当属性不可靠时，BRB-r 的整体可靠性降低。因此，开发了一种用于 BRB-r 整体可靠性的计算方法，以支持工程实践中的决策。进行了柴油发动机安全评估的案例研究，以证明所提出的 BRB-r 模型的效率。