Automation in structural health monitoring (SHM) has greatly benefited from computer science's recent advances. Unlike images, the existing datasets for other types of input, such as vibration-based damage data, are relatively smaller, less diverse, and highly imbalanced. Therefore, the reliability of data-driven models developed for safety-critical assessments can be questionable. This paper proposes a dual Bayesian inference where damage predictions are accompanied by measuring the model's confidence in predictions. First, it is shown how dual classification is integrated with Bayesian inference. Later, we introduce a surrogate deep learning module to transform the raw uncertainty output into an easily interpretable prediction uncertainty index (PUI). The PUI metric can be used to alarm a decision-maker of the potential mistakes. The proposed dual Bayesian models are investigated on a 2D structure with seven different sensor layouts. Our approach yields increased robustness for different metrics compared with the benchmark. In addition to the performance boost, PUI information paves the way for a risk-informed implementation of deep learning models in vibration-based damage diagnosis.

中文翻译：

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双贝叶斯推理用于基于风险的基于振动的损伤诊断

结构健康监测 (SHM) 的自动化极大地受益于计算机科学的最新进展。与图像不同，其他类型输入的现有数据集（例如基于振动的损坏数据）相对较小、多样性较低且高度不平衡。因此，为安全关键评估开发的数据驱动模型的可靠性可能值得怀疑。本文提出了一种双重贝叶斯推理，其中损伤预测伴随着测量模型对预测的置信度。首先，展示了如何将双重分类与贝叶斯推理相结合。稍后，我们引入了替代深度学习模块，将原始不确定性输出转换为易于解释的预测不确定性指数 (PUI)。PUI 指标可用于警告决策者潜在的错误。在具有七种不同传感器布局的二维结构上研究了所提出的双贝叶斯模型。与基准相比，我们的方法提高了不同指标的稳健性。除了性能提升之外，PUI 信息还为在基于振动的损坏诊断中以风险为依据实施深度学习模型铺平了道路。