ABSTRACT Anomaly detection in vibration signals is an important technique for fault diagnosis, monitoring, and maintenance in nuclear power plants. Therefore, various signal-analysis methods that apply statistical, machine-learning, and deep-learning techniques have been proposed. In particular, deep neural networks (DNNs) have excellent recognition accuracy and do not require the designing of a feature extractor. However, to apply a DNN model for the analysis of time-series data, its parameters must be optimized. This requires not only signal data acquired from real systems, but also data labels that explain any abnormality in the signals. This requires data preparation, and it is time consuming and difficult for humans to annotate manually, especially when the data includes complicated features. Therefore, to extract abnormal features latent in time-series data automatically, we devised a DNN-model training method. To train the DNN model, we propose a novel weakly supervised training method by devising a loss function. We confirmed through experiments that the proposed approach can be used to detect, identify, and localize anomalies in vibration signal data. Furthermore, by applying this method to a fault-classification problem, we confirmed that it can be used to extract features that represent each type of the failures of rotating machinery.

中文翻译：

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基于弱监督长短期记忆的轴承故障诊断

摘要 振动信号异常检测是核电站故障诊断、监测和维护的重要技术。因此，已经提出了各种应用统计、机器学习和深度学习技术的信号分析方法。特别是，深度神经网络 (DNN) 具有出色的识别准确率，并且不需要设计特征提取器。但是，要将 DNN 模型应用于时间序列数据的分析，必须对其参数进行优化。这不仅需要从真实系统中获取的信号数据，还需要能够解释信号异常的数据标签。这需要数据准备，人工标注费时费力，尤其是当数据包含复杂特征时。所以，为了自动提取时间序列数据中潜在的异常特征，我们设计了一种 DNN 模型训练方法。为了训练 DNN 模型，我们通过设计损失函数提出了一种新颖的弱监督训练方法。我们通过实验证实，所提出的方法可用于检测、识别和定位振动信号数据中的异常。此外，通过将此方法应用于故障分类问题，我们确认它可用于提取代表旋转机械各种故障类型的特征。并定位振动信号数据中的异常。此外，通过将此方法应用于故障分类问题，我们确认它可用于提取代表旋转机械各种故障类型的特征。并定位振动信号数据中的异常。此外，通过将此方法应用于故障分类问题，我们确认它可用于提取代表旋转机械各种故障类型的特征。