Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea.
PD Technology Co., Ulsan 44610, Korea.
Sensors (Basel). 2021 Nov 22;21(22):7761. doi: 10.3390/s21227761.
In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure's failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is automatically constructed from raw AE data with a deep neural network pretrained and fine-tuned by a stacked autoencoder deep neural network (SAE-DNN). For the deep neural network structure to perform a more accurate construction of health indicator lines, a hit removal process with a one-class support vector machine (OC-SVM), which has not been investigated in previous studies, is proposed to extract only the hits which matter the most to the portrait of deterioration. The new set of hits is then harnessed as the training labels for the deep neural network. After the completion of the health indicator line construction, health indicators are forwarded to a long short-term memory recurrent neural network (LSTM-RNN) for the training and validation of the remaining useful life prediction, as this structure is capable of capturing the long-term dependencies, even with a limited set of data. Our prediction result shows a significant improvement in comparison with a similar scheme but without the hit removal process and other methods, such as the gated recurrent unit recurrent neural network (GRU-RNN) and the simple recurrent neural network.
本研究提出了一种从原始声发射（AE）数据进行剩余使用寿命（RUL）预测的方案，旨在预测混凝土结构在失效前发生故障的可能性，从而延长其使用寿命并最小化意外损坏的风险。该方案通过健康指标（HI）来描述劣化过程，该指标是使用深度神经网络自动从原始 AE 数据中构建的，并使用堆叠自动编码器深度神经网络（SAE-DNN）进行预训练和微调。为了使深度神经网络结构更准确地构建健康指标线，提出了一种使用单类支持向量机（OC-SVM）的剔除过程，该过程在之前的研究中尚未被研究过，用于仅提取对劣化描述最重要的命中。然后，将新的命中集用作深度神经网络的训练标签。在完成健康指标线的构建后，将健康指标转发到长短期记忆递归神经网络（LSTM-RNN）中进行剩余使用寿命预测的训练和验证，因为这种结构能够捕获长期依赖关系，即使数据有限。与没有剔除过程和其他方法（如门控递归单元递归神经网络（GRU-RNN）和简单递归神经网络）的类似方案相比，我们的预测结果有了显著的提高。
