National Engineering Research Center of Fiber Optic Sensing Technology and Networks, Wuhan University of Technology, Wuhan 430070, China.
School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel). 2022 Aug 1;22(15):5744. doi: 10.3390/s22155744.
Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain satisfactory accuracy. To overcome this drawback, this paper presents a long short-term memory (LSTM) neural network with transfer learning and ensemble learning and combines it with an unsupervised health indicator (HI) construction method for remaining-useful-life prediction. This study consists of the following parts: (1) utilizing the characteristics of deep belief networks and self-organizing map networks to translate raw sensor data to a synthetic HI that can effectively reflect system health; and (2) introducing transfer learning and ensemble learning to provide the required degradation mechanism for the RUL prediction model based on LSTM to improve the performance of the model. The performance of the proposed method is verified by two bearing datasets collected from experimental data, and the results show that the proposed method obtains better performance than comparable methods.
剩余使用寿命 (RUL) 的预测对于提高制造设备的安全性和可靠性具有重要意义。然而，在实际工业中，对于在小样本故障上训练的 RUL 预测模型，很难获得令人满意的准确性。为了克服这一缺点，本文提出了一种具有迁移学习和集成学习的长短期记忆 (LSTM) 神经网络，并结合了一种用于剩余使用寿命预测的无监督健康指标 (HI) 构建方法。本研究包括以下几个部分：（1）利用深度置信网络和自组织映射网络的特点，将原始传感器数据转换为能够有效反映系统健康状况的综合 HI；（2）引入迁移学习和集成学习，为基于 LSTM 的 RUL 预测模型提供所需的退化机制，从而提高模型的性能。通过两个来自实验数据的轴承数据集验证了所提出方法的性能，结果表明，所提出的方法比可比方法获得了更好的性能。
