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Structural Health Monitoring and Damage Assessment Using Measured FRFs from Multiple Sensors, Part II: Decision Making with RBF Networks

机译：使用来自多个传感器的实测FRF进行结构健康监测和损伤评估，第二部分：使用RBF网络进行决策

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This paper is the second of two papers concerned with structural health monitoring and damage assessment using measured FRFs from multiple sensors, and discusses the decision making technique with radial basis function (RBF) neural networks. In PART 1 of the paper, the correlation criteria showed their capability to indicate various changes to the structure's state. PART 2, presented here, develops the methodology of decision theory to identify precisely all of the structure states. Although, the statistical approach can be used for classification, interpreting the information is difficult. Neural network techniques have been proven to possess many advantages for classification due to their learning ability and good generalization. In this paper, the radial basis function neural network is applied for function approximation and recognition. The key idea is to partition the input space (the indicators of the correlation criteria) into a number of subspaces that are in the form of hyper spheres. Then, the widely used k-mean clustering algorithm was selected as a logical approach to detecting the structure states. A bookshelf structure with measured frequency responses from 24 accelerometers was used to demonstrate the effectiveness of the method. The results show the successful classification of all structure states, for instance, the undamaged and damage states, damage locations and damage levels, and the environmental variability.
机译：本文是有关使用来自多个传感器的实测FRF进行结构健康监测和损伤评估的两篇论文中的第二篇，并讨论了基于径向基函数（RBF）神经网络的决策技术。在本文的第1部分中，相关标准显示了它们指示结构状态的各种变化的能力。此处介绍的第2部分将开发决策理论的方法，以精确地识别所有结构状态。尽管可以使用统计方法进行分类，但是难以解释信息。神经网络技术由于具有学习能力和良好的通用性，已被证明具有许多分类优势。本文将径向基函数神经网络应用于函数逼近和识别。关键思想是将输入空间（相关标准的指标）划分为多个超球面形式的子空间。然后，选择了广泛使用的k均值聚类算法作为检测结构状态的逻辑方法。具有24个加速度计的测量频率响应的书架结构被用来证明该方法的有效性。结果表明，成功地对所有结构状态进行了分类，例如，未损坏和损坏状态，损坏位置和损坏级别以及环境变异性。

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