The increasing complexity of the modern engineering system has made the multisource information fusion a necessary yet challenging task. In the context of reliability engineering, the information fusion process is either ineffective or less efficient as the aggregation error increases with respect to the collection of multiple dependent pieces of evidence. To address this challenge, this paper proposes a comprehensive Bayesian approach for system reliability evaluation that considers multiple, dependent sources of information. We show that the so‐called “Bayesian anomaly,” a type of aggregation error, is caused by misuse of the dependent information and could be eliminated if all available information is properly addressed. A topological technique is employed as a tool for information fusion in the likelihood construction. A likelihood‐based approach is then developed to formulate the overall likelihood as well as the reliability model. These two techniques are embedded into a comprehensive Bayesian framework to allow for an efficient evaluation of the system reliability. Our approach has also been extended to include imprecise information, such as interval and/or censored data, which is more frequently encountered in practical engineering. We demonstrate the proposed method through several numerical cases and one real‐life application example. This study provides a better understanding of the role of dependent information in system reliability evaluation. In addition, it presents an efficient pathway to extract the inherent dependency information embedded in imperfect data sets.

中文翻译：

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系统可靠性评估中多个不精确相关信息聚合中的贝叶斯异常研究

现代工程系统日益复杂，已使多源信息融合成为一项必要而又具有挑战性的任务。在可靠性工程的背景下，信息融合过程要么无效，要么效率不高，因为相对于多个相关证据的收集，聚集误差增加了。为了解决这一挑战，本文提出了一种用于系统可靠性评估的综合贝叶斯方法，该方法考虑了多个相互依赖的信息源。我们表明，所谓的“贝叶斯异常”是一种聚集错误，是由于对相关信息的滥用所致，如果正确处理了所有可用信息，则可以将其消除。拓扑技术被用作可能性构建中信息融合的工具。然后，开发了一种基于可能性的方法来制定总体可能性以及可靠性模型。这两种技术都嵌入了全面的贝叶斯框架中，可以有效评估系统的可靠性。我们的方法也已扩展到包括不精确的信息，例如间隔和/或审查数据，这在实际工程中经常遇到。我们通过几个数值案例和一个实际应用示例演示了该方法。这项研究可以更好地理解相关信息在系统可靠性评估中的作用。此外，它提供了一种有效的途径来提取嵌入不完善数据集中的固有依赖性信息。