国外文献 > 1.轴承 > 2. 轴承类型与部件 > 滚动轴承
关键词搜索：
| 标 题： |
A combined deep learning model for damage size estimation of rolling bearing
用于滚动轴承损伤尺寸估计的联合深度学习模型 |
| 作 者： |
Xiyang, Liu
Guo, Chen
Tengfei, Hao
Wenping, Pan |
| 关键词： |
Aero engine [航空发动机]
rolling bearing [ 滚动轴承]
fault diagnosis [ 故障诊断]
damage size [ 损伤大小]
deep convolution network [ 深度卷积网络]
long-short-term memory network [ 长短期记忆网络]
ELEMENT BEARINGS [ 元素轴承]
FAULT-DETECTION [ 故障检测]
NETWORKS [ 网络] |
| 出版年： |
2023 |
| 期刊名： |
International journal of engine research
国际发动机研究杂志 |
| 年卷期： |
2023, vol.24, no.4 |
| 摘 要： |
The identification of damage sizes based on vibration monitoring data is significant in the study of fault evolution, prediction and diagnosis of rolling bearing. There are inherent restrictions in traditional identification models such as high dependence on prior knowledge and insufficient feature extraction, aiming at the limitation, a prediction method of rolling bearing damage size based on deep learning is proposed. In this paper, a combined model of deep convolutional long-short-term memory network is developed, which can sufficiently extract the multi-dimensional and time-series characteristics of bearing vibration signal, and realize the intelligent and efficient diagnosis of bearing fault. On the basis of theoretical analysis, the rolling bearing fault tests under various damage sizes and rotational velocities are carried out by using the accelerated fatigue testing machine for rolling bearings, and the traditional and novel methods are compared based on the test data. The results show that the prediction accuracy of the combined network can reach 99.94% and 98.67%, respectively under normal and noisy conditions, which is higher than the single deep convolution network, long-short-term memory network and other models. The comparison results amply demonstrate the superiority of the proposed method.
根据振动监测数据识别损伤尺寸在滚动轴承故障演化、预测与诊断研究中具有重要意义。传统识别模型存在诸多局限，例如对先验知识依赖性强、特征提取不足等。为克服这些限制，本文提出一种基于深度学习的滚动轴承损伤尺寸预测方法。本文开发了一种深度卷积长短期记忆网络组合模型，能够充分提取轴承振动信号的多维及时序特征，实现轴承故障的智能高效诊断。基于理论分析，利用加速疲劳试验机对滚动轴承进行了不同损伤程度和转速下的故障测试，并基于测试数据对比了传统方法与新方法的性能。结果表明，联合网络在正常和噪声条件下预测准确率分别达到99.94%和98.67%，高于单个深度卷积网络、长短期记忆网络等其他模型。对比结果充分证明了所提出方法的优越性。 |
| 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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