In recent years, more and more researchers are using deep learning to monitor and diagnose mechanical equipment faults. When new task data not considered in the training stage are generated during the operation of the equipment, it is difficult for the model to recognize this type of data. If only new task data are used in training, it will lead to poor performance in the old task. When using all the data in training, with the accumulation of task data, the cost of data storage will increase and the speed of model update will be greatly reduced. Therefore, an intelligent fault diagnosis method based on the layer regeneration network under class imbalanced samples is proposed, which uses only new task data to update the model. The method holds that the data contain some information of other categories, but they are covered by information of their own categories, and the knowledge between classes is extracted by knowledge distillation to enhance the learning of other categories. First, the cross-domain learning method based on parameter transfer is adopted to make the layer regeneration network model (LRNM) converge quickly on the new task. Then, the implicit knowledge related to the old task in the new task data is extracted by the distillation learning method to adjust global parameters, alleviate the catastrophic forgetting problem in model updating, and realize the model continuous learning. Through experiments, the use of dark knowledge can effectively enhance the learning of other types of knowledge.

中文翻译：

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具有参数传递和知识提炼的层级再生网络用于轴承智能故障诊断的类不平衡样本

近年来，越来越多的研究人员使用深度学习来监测和诊断机械设备故障。当设备运行过程中产生了训练阶段未考虑的新任务数据时，模型很难识别此类数据。如果在训练中只使用新的任务数据，会导致在旧任务中表现不佳。在训练中使用所有数据时，随着任务数据的积累，数据存储成本会增加，模型更新速度会大大降低。因此，提出了一种基于类不平衡样本下层再生网络的智能故障诊断方法，该方法仅使用新的任务数据来更新模型。该方法认为数据包含其他类别的一些信息，但它们都被自己类别的信息所覆盖，通过知识蒸馏提取类别之间的知识，以增强对其他类别的学习。首先，采用基于参数传递的跨域学习方法，使层再生网络模型（LRNM）在新任务上快速收敛。然后，通过蒸馏学习方法提取新任务数据中与旧任务相关的隐性知识，调整全局参数，缓解模型更新中的灾难性遗忘问题，实现模型的持续学习。通过实验，暗知识的使用可以有效地增强其他类型知识的学习。采用基于参数迁移的跨域学习方法，使层再生网络模型（LRNM）在新任务上快速收敛。然后，通过蒸馏学习方法提取新任务数据中与旧任务相关的隐性知识，调整全局参数，缓解模型更新中的灾难性遗忘问题，实现模型的持续学习。通过实验，暗知识的使用可以有效地增强其他类型知识的学习。采用基于参数迁移的跨域学习方法，使层再生网络模型（LRNM）在新任务上快速收敛。然后，通过蒸馏学习方法提取新任务数据中与旧任务相关的隐性知识，调整全局参数，缓解模型更新中的灾难性遗忘问题，实现模型的持续学习。通过实验，暗知识的使用可以有效的增强其他类型知识的学习。缓解模型更新中的灾难性遗忘问题，实现模型的持续学习。通过实验，暗知识的使用可以有效地增强其他类型知识的学习。缓解模型更新中的灾难性遗忘问题，实现模型的持续学习。通过实验，暗知识的使用可以有效地增强其他类型知识的学习。