Many laboratory fault failure experiments are conducted as analogue of earthquakes, in which most of them are coupled with acoustic emission (AE) as a powerful diagnostic tool for investigating failure precursors. The purpose of this study is to predict time to the next failure in a laboratory fault failure experiment before failures happen based on the instantaneous recorded AE signals. A customized deep learning network comprising the convolutional neural network module and the recurrent neural network module is built and trained using raw AE data directly. No statistical characteristics or handmade features are extracted from raw data, avoiding any possible precursor information losses. More than 600 million AE data from a repetitive fault failure experiment are segmented as several thousand equilong sequences to form training and validation samples. The proposed network delivers satisfactory predicted results with the R2 value 0.55, much better than results using traditional earthquake catalogs method. Results of this study also demonstrate that our network does not prioritize those AE signals collected when failures impend, which is a common bias in traditional earthquake prediction methods. This study definitely holds the promise of using deep learning in earthquake prediction. Further studies are needed when analogous studies proceed to an industrial practice.

许多实验室故障失效实验都是模拟地震进行的，其中大多数实验都与声发射（AE）结合使用，作为研究故障前兆的有力诊断工具。这项研究的目的是根据瞬时记录的AE信号预测发生故障之前在实验室故障实验中发生下一次故障的时间。直接使用原始AE数据构建和训练包含卷积神经网络模块和递归神经网络模块的定制深度学习网络。没有从原始数据中提取统计特征或手工特征，从而避免了任何可能的前体信息损失。来自重复故障失效实验的超过6亿个AE数据被分割为数千个等长序列，以形成训练和验证样本。拟议的网络提供令人满意的R2值为0.55的预测结果，比使用传统地震目录法的结果要好得多。这项研究的结果还表明，当故障发生时，我们的网络没有优先考虑收集到的AE信号，这是传统地震预测方法中的常见偏见。这项研究无疑具有在地震预测中使用深度学习的希望。当类似的研究进入工业实践时，需要进一步的研究。这项研究的结果还表明，当故障发生时，我们的网络没有优先考虑收集到的AE信号，这是传统地震预测方法中的常见偏见。这项研究无疑具有在地震预测中使用深度学习的希望。当类似的研究进入工业实践时，需要进一步的研究。这项研究的结果还表明，当故障发生时，我们的网络没有优先考虑收集到的AE信号，这是传统地震预测方法中的常见偏见。这项研究无疑具有在地震预测中使用深度学习的希望。当类似的研究进入工业实践时，需要进一步的研究。