In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven health monitoring systems is gaining in popularity due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as Deep Learning. The main purpose of this paper is to combine deep neural networks with Bidirectional Long Short Term Memory and advanced statistical analysis involving Instantaneous Frequency and Spectral Kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from acoustic emission events (cracks). We investigated on effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of future on structural health monitoring (SHM) technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.

翻译：在现代建筑基础设施中,设计适应性和不受监督的由数据驱动的健康监测系统的机会越来越受欢迎,因为从具有通信能力的低成本传感器和深学习等先进的模型工具获得大量大数据,本文件的主要目的是将深神经网络与双向长期短期记忆结合起来,并进行涉及不时频率和光谱疾病在内的高级统计分析,以开发一个精确的分类工具,用于对来自声学排放事件(裂缝)的抗拉、剪和混合模式进行分类。我们调查了有效事件描述器,以捕捉不同类型模式的独特特征。实验结果测试证实,这一方法在不同裂缝事件之间实现有希望的分类,并能够影响未来结构健康监测技术的设计。这种方法有效地将早期损害分类为92%的准确度,这有利于计划维护。