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国外文献 > 1.轴承 > 5. 监测与诊断 > 故障诊断

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标 题： A Bearing Fault Diagnosis Method based on Improved LSTM-cascade CatBoost
基于改进LSTM-级联CatBoost的轴承故障诊断方法
作 者： Weicong Jin
Weizhi Liu
Wenxuan Zhang
Xia Fang
关键词： Fault diagnosis  [故障诊断]
Bearing  [ 轴承]
Long short-term memory  [ 长短期记忆]
GcForest  [ GcForest]
CatBoost  [ CatBoost]
出版年： 2022
文集名： 9th International Conference on Dependable Systems and Their Applications (DSA 2022)
第九届可靠系统及其应用国际会议（DSA 2022）
会议名： 9th International Conference on Dependable Systems and Their Applications (DSA 2022)
第九届可靠系统及其应用国际会议（DSA 2022）
届 次： 9th
摘 要： Bearing faults are widely concerned in the field of fault diagnosis, it has numerous excellent detection algorithms currently. In this paper, a new model called LSTM-Cascade CatBoost is applied, which can directly classify bearing vibration signals in the case of multiple granularities and high dimensions without signal processing. The model is based on gcForest, which can automatically adjust its complexity to the size of the dataset and it uses LSTM to improve its feature extraction ability. CatBoost is used as the base classifier of cascade forest to improve classification accuracy. Experimental results show that this model has high accuracy in CWRU and XJTU-SY datasets. Besides, it not only demonstrates that the feature extraction ability of LSTM is significantly better than that of multi-grained scanning, but CatBoost as a base classifier can further improve the accuracy of a cascade forest.

轴承故障在故障诊断领域备受关注，目前已有众多优秀的检测算法。本文提出一种名为LSTM-Cascade CatBoost的新模型，该模型无需信号处理即可直接对多粒度、高维的轴承振动信号进行分类。该模型基于gcForest，能够根据数据集规模自动调整其复杂度，并利用LSTM提升特征提取能力。同时，采用CatBoost作为级联森林的基础分类器，进一步提高分类准确率。实验结果表明，该模型在CWRU和XJTU-SY数据集上均表现出较高的准确率。此外，不仅证明了LSTM的特征提取能力显著优于多粒度扫描方法，而且CatBoost作为基础分类器还能进一步提升级联森林的分类精度。
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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