Machine learning methods have achieved outstanding results in the fault detection of industrial processes when the training and test data follow the same distribution or originate from the same situation. However, the pre-trained detection model fails when the operation status changes or unknown fault occurs during the actual production. Therefore, this paper proposes a novel bilayer convolutional transfer learning neural network (BCTLNN) to improve the generalization of detection model. BCTLNN is a bilayer network (local and global level) in order to extract the fault features. Transfer learning strategies (fine-tuning and domain adaptation) are introduced to learn the domain invariant features by minimizing the divergence between different domain data. Experiments on the benchmark bearing data and the agglomeration fault data from an actual polyethylene process are employed to verify the effectiveness of the proposed method.

中文翻译：

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用于工业故障检测的改进的双层卷积迁移学习神经网络

机器学习方法在训练和测试数据服从相同分布或源自相同情况的工业过程故障检测中取得了突出的效果。但是，在实际生产过程中，当运行状态发生变化或出现未知故障时，预训练的检测模型就会失效。因此，本文提出了一种新颖的双层卷积迁移学习神经网络（BCTLNN）来提高检测模型的泛化能力。BCTLNN 是一个双层网络（本地和全局级别），用于提取故障特征。引入迁移学习策略（微调和域适应）通过最小化不同域数据之间的差异来学习域不变特征。