国外文献 > 1.轴承 > 7. 智能方法与计算 > 神经网络
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| 标 题： |
A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings
基于卷积神经网络的滚动轴承劣化指标构建与健康预测：采用双向长短期记忆网络 |
| 作 者： |
Cheng, Yiwei
Hu, Kui
Wu, Jun
Zhu, Haiping
Shao, Xinyu |
| 关键词： |
Rolling bearing [滚动轴承]
Degradation indicator construction [ 降解指示剂的制备]
Health prognosis [ 健康预后]
Convolutional neural network [ 卷积神经网络]
Bidirectional long short-term memory network [ 双向长短时记忆网络] |
| 出版年： |
2021 |
| 期刊名： |
Advanced engineering informatics
高级工程信息学 |
| 年卷期： |
2021, vol.48 |
| 摘 要： |
Health prognosis of rolling bearing is of great significance to improve its safety and reliability. This paper presents a novel health prognosis method for the rolling bearing based on convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) model. First, a new nonlinear degradation indicator (DI) is designed which can be utilized as training label. Then, through learning and capturing the mapping relationship between raw vibration signals and DI of the rolling bearing, a CNN model is introduced to estimate the DI value of the rolling bearing. And, BiLSTM models are set up to carry out health prognosis using the estimated DI, including future DI and remaining useful life prediction. An experiment verification is implemented to validate the effectiveness of the proposed method. Results show the excellent ability of future DI prediction, and demonstrate the superiority of the proposed method in the field of remaining useful life prediction compared with other existing deep learning models.
滚动轴承的健康预测对其安全性和可靠性提升具有重要意义。本文提出了一种基于卷积神经网络（CNN）和双向长短期记忆网络（BiLSTM）模型的新型滚动轴承健康预测方法。首先，设计了一种新的非线性劣化指标（DI），可用作训练标签； 然后，通过学习并捕捉原始振动信号与滚动轴承DI之间的映射关系，引入CNN模型来估计滚动轴承的DI值。同时，构建BiLSTM模型，利用所估计的DI值进行健康状态预测，包括未来DI值及剩余使用寿命预测。通过实验验证了该方法的有效性。结果表明，该方法在预测未来DI值方面表现出优异能力，并且在剩余使用寿命预测领域相较于其他现有深度学习模型具有明显优势。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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