Deep learning has a strong feature learning ability, which has proved its effectiveness in fault prediction and remaining useful life prediction of rotatory machine. However, training a deep network from scratch requires a large amount of training data and is time-consuming. In the practical model training process, it is difficult for the deep model to converge when the parameter initialization is inappropriate, which results in poor prediction performance. In this paper, a novel deep learning framework is proposed to predict the remaining useful life of rotatory machine with high accuracy. Firstly, model parameters and feature learning ability of the pretrained model are transferred to the new network by means of transfer learning to achieve reasonable initialization. Then, the specific sensor signals are converted to RGB image as the specific task data to fine-tune the parameters of the high-level network structure. The features extracted from the pretrained network are the input into the Bidirectional Long Short-Term Memory to obtain the RUL prediction results. The ability of LSTM to model sequence signals and the dynamic learning ability of bidirectional propagation to time information contribute to accurate RUL prediction. Finally, the deep model proposed in this paper is tested on the sensor signal dataset of bearing and gearbox. The high accuracy prediction results show the superiority of the transfer learning-based sequential network in RUL prediction.

中文翻译：

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深度神经网络的顺序神经网络与残差神经网络对旋转机械剩余使用寿命的预测

深度学习具有很强的特征学习能力，证明了其在故障预测和旋转机器剩余使用寿命预测中的有效性。但是，从头开始训练深层网络需要大量训练数据，并且很耗时。在实际模型训练过程中，当参数初始化不合适时，深层模型难以收敛，导致预测性能较差。在本文中，提出了一种新颖的深度学习框架，可以高精度地预测旋转机械的剩余使用寿命。首先，通过转移学习将预训练模型的模型参数和特征学习能力转移到新网络中，以实现合理的初始化。然后，特定的传感器信号被转换为RGB图像作为特定的任务数据，以微调高级网络结构的参数。从预训练网络中提取的特征被输入到双向长短期存储器中以获得RUL预测结果。LSTM对序列信号建模的能力以及对时间信息的双向传播的动态学习能力有助于准确的RUL预测。最后，在轴承和齿轮箱的传感器信号数据集上测试了本文提出的深度模型。高精度的预测结果显示了基于转移学习的顺序网络在RUL预测中的优越性。从预训练网络中提取的特征被输入到双向长短期存储器中以获得RUL预测结果。LSTM对序列信号建模的能力以及对时间信息的双向传播的动态学习能力有助于准确的RUL预测。最后，在轴承和齿轮箱的传感器信号数据集上测试了本文提出的深度模型。高精度的预测结果显示了基于转移学习的顺序网络在RUL预测中的优越性。从预训练网络中提取的特征被输入到双向长短期存储器中以获得RUL预测结果。LSTM对序列信号建模的能力以及对时间信息的双向传播的动态学习能力有助于准确的RUL预测。最后，在轴承和变速箱的传感器信号数据集上测试了本文提出的深度模型。高精度的预测结果显示了基于转移学习的顺序网络在RUL预测中的优越性。本文提出的深度模型在轴承和齿轮箱的传感器信号数据集上进行了测试。高精度的预测结果显示了基于转移学习的顺序网络在RUL预测中的优越性。本文提出的深度模型在轴承和齿轮箱的传感器信号数据集上进行了测试。高精度的预测结果显示了基于转移学习的顺序网络在RUL预测中的优越性。