Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea.
Sensors (Basel). 2022 May 12;22(10):3687. doi: 10.3390/s22103687.
This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback-Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen; as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen's useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback-Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen's work.
本文提出了一种基于 Kullback-Leibler 散度（KLD）和深度学习的混凝土梁健康指标构建的新方法。健康指标（HI）的构建是监测混凝土结构健康的剩余使用寿命（RUL）方法的重要组成部分。通过构建 HI，可以对劣化过程进行处理和描述，以便将其转发到预测模块进行 RUL 预测。通过预测 RUL，可以根据当前样本的情况识别退化进度和故障；因此，可以计划进行维护，以降低安全风险、降低财务成本并延长样本的使用寿命。通过使用深度神经网络（DNN）从原始声发射（AE）数据中构建 HI 来描述劣化，其参数通过使用堆叠自编码器（SAE）进行预训练和微调来获得。Kullback-Leibler 散度是在参考正态条件信号和当前未知信号之间计算的，用于表示混凝土结构的劣化过程，目前尚未针对混凝土梁进行研究。基于 DNN 的构造器然后学习从具有 KLD 值作为训练标签的原始数据生成 HI。使用混凝土样本的运行至失效测试数据对 HI 构建结果进行了评估，评估指标有两个：构建结果的拟合度分析和 RUL 预测。结果证实了 KLD 在描述劣化过程中的可靠性，与其他方法相比有了很大的改进。此外，该方法不需要对 AE 或系统故障的性质有专业知识，这比需要这种专业知识的基于模型的方法更有利。此外，AE 提供了在役监测，允许在不中断样本工作的情况下执行 RUL 预测任务。
