Abstract

A belief rule-based (BRB) model with attribute reliability (BRB-r) has been developed recently, where the systematic uncertainty is regarded as attribute reliability by extending the traditional BRB model. The BRB-r model provides a framework to deal with the systematic uncertainty, but the drawbacks in modeling and inference reduces the accuracy of it. This paper proposed a new modeling and inference approach to improve the effectiveness of the BRB-r. This approach is constituted by two parts: data processing and BRB inference. In the data processing, the attribute reliability is calculated based on the auto regressive model, while the parameters of BRB-r are optimized using the differential evolution algorithm. In the BRB inference, a new attribute reliability fusion algorithm is proposed, which can effectively integrate attribute reliability into the BRB model and ensure the rationality in different situations. A benchmark case about pipeline leak detection and a practical case about condition monitoring are studied to demonstrate the rationality and feasibility of the proposed approach to the BRB-r model.

中文翻译：

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具有属性可靠性的信念规则库的新建模与推理方法

摘要

最近开发了一种具有属性可靠性（BRB-r）的基于信念规则的（BRB）模型，其中通过扩展传统的BRB模型将系统不确定性视为属性可靠性。BRB-r模型提供了处理系统不确定性的框架，但是建模和推理的缺点降低了其准确性。本文提出了一种新的建模和推理方法，以提高BRB-r的有效性。此方法由两部分组成：数据处理和BRB推理。在数据处理中，基于自回归模型计算属性可靠性，同时使用差分进化算法优化BRB-r的参数。在BRB推断中，提出了一种新的属性可靠性融合算法，可以有效地将属性可靠性整合到BRB模型中，并确保在不同情况下的合理性。研究了关于管道泄漏检测的基准案例和关于状态监测的实际案例，以证明所提出的BRB-r模型方法的合理性和可行性。