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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 Technique Based on an Optimized MCKD and Multi-scale DSCNN
基于优化MCKD与多尺度DSCNN的轴承故障诊断技术
作 者： Hongjiao Luo
Yajun Shang
Kailin Jiang
Yiming Chen
Tian Ran Lin
关键词： Fault diagnosis  [故障诊断]
Rolling bearing  [ 滚动轴承]
Depth-wise separable network  [ 深度可分离网络]
Maximum correlated kurtosis deconvolution  [ 最大相关峰度反卷积]
Sparrow search algorithm  [ 麻雀搜索算法]
出版年： 2025
文集名： Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic, TEPEN2024-IWFDP - Volume 2
TEPEN国际故障诊断与预测研讨会论文集，TEPEN2024-IWFDP - 第2卷
会议名： 2024 TEPEN International Workshop on Fault Diagnostics and Prognostics (TEPEN-IWFDP 2024)
2024年TEPEN国际故障诊断与预测研讨会（TEPEN-IWFDP 2024）
届 次：
摘 要： To address issues such as a lack of prior knowledge, noise interference, and difficulties in early fault feature extraction, a rolling bearing fault diagnosis technique utilizing an optimized maximum correlated kurtosis deconvo-lution (MCKD) and multi-scale depth-wise separable convolutional neural Network (MDSCNN) is proposed in this study. The parameters of MCKD are optimized adaptively by using the sparrow optimization algorithm, and the optimized MCKD is employed to filter the condition monitoring (CM) signal for fault feature enhancement. The filtered signals are then used as the input to train the MDSCNN and for fault classification. The validity of the proposed algorithm is examined using a published bearing dataset where an accuracy as high as 99.87% is achieved. Furthermore, the superiority of the proposed algorithm in bearing fault diagnosis is verified by comparing the diagnostic result with those using other commonly employed techniques in a comparison study.

为解决缺乏先验知识、噪声干扰以及早期故障特征提取困难等问题，本研究提出了一种滚动轴承故障诊断技术，该技术采用优化的极大相关曲率反卷积（MCKD）与多尺度深度可分离卷积神经网络（MDSCNN）。MCKD的参数通过麻雀优化算法进行自适应优化，并利用优化后的MCKD对状态监测（CM）信号进行滤波，以增强故障特征。滤波后的信号作为训练输入，用于训练MDSCNN并实现故障分类。通过使用公开的轴承数据集验证了所提算法的有效性，达到了高达99.87%的准确率。此外，通过与其他常用方法的对比研究，进一步验证了该算法在轴承故障诊断中的优越性。
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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