用于悬臂梁损伤检测的神经符号模型
Neuro-symbolic model for cantilever beams damage detection
AI总结：
针对深度学习损伤检测缺乏内在可解释性的问题，本文提出融合卷积网络与真实逻辑查询的 Logic Convolutional Neural Regressor，用于悬臂梁损伤检测，并通过数值与实验室实验验证其优越性。
摘要：
在过去十年中，损伤检测方法迅速从先进的信号处理方法转向机器学习，尤其是深度学习模型，以准确且非侵入式地评估梁结构的状态。但是，随着深度学习模型达到其性能峰值，其在适用性方面的局限和脆弱性也被观察到。在运行条件下缺乏可信性的最重要原因之一，是深度学习系统缺少内在可解释性，这源于知识被编码在张量值中，且没有纳入逻辑约束。本文提出一种用于悬臂梁损伤检测的神经符号模型，该模型基于一种新型认知架构，在其中将卷积网络的处理能力与通过查询提供的交互式控制相结合，而这些查询是通过将真实逻辑直接纳入模型来实现的。该混合判别模型以 Logic Convolutional Neural Regressor（逻辑卷积神经回归器）之名提出，并在一个悬臂梁相对固有频率偏移值数据集上进行测试，该数据集源自一个原创数学关系。所得结果保留了深度学习模型的全部预测能力，同时使用三种距离作为可满足性谓词，使系统在实际应用中更可信、更可扩展。研究开展了大量数值实验和实验室实验，所有实验均证明了该混合方法的优越性，可为解决损伤检测问题开辟一条新路径。
英文摘要：
In the last decade, damage detection approaches swiftly changed from advanced signal processing methods to machine learning and especially deep learning models, to accurately and non-intrusively estimate the state of the beam structures. But as the deep learning models reached their peak performances, also their limitations in applicability and vulnerabilities were observed. One of the most important reason for the lack of trustworthiness in operational conditions is the absence of intrinsic explainability of the deep learning system, due to the encoding of the knowledge in tensor values and without the inclusion of logical constraints. In this paper, we propose a neuro-symbolic model for the detection of damages in cantilever beams based on a novel cognitive architecture in which we join the processing power of convolutional networks with the interactive control offered by queries realized through the inclusion of real logic directly into the model. The hybrid discriminative model is introduced under the name Logic Convolutional Neural Regressor and it is tested on a dataset of values of the relative natural frequency shifts of cantilever beams derived from an original mathematical relation. While the obtained results preserve all the predictive capabilities of deep learning models, the usage of three distances as predicates for satisfiability, makes the system more trustworthy and scalable for practical applications. Extensive numerical and laboratory experiments were performed, and they all demonstrated the superiority of the hybrid approach, which can open a new path for solving the damage detection problem.
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