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国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 信号处理

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标 题： A Comparative Study Between EMD-MCKD and EMD-MEDA Techniques for the Bearing Faults Diagnosis in Induction Motors
感应电机轴承故障诊断中EMD-MCKD与EMD-MEDA技术的比较研究
作 者： Ahmed Chennana
Abderrahmane Ahmia
Ahmed Chaouki Megherbi
Salim Sbaa
Noureddine Bessous
Yasser Damine
关键词： Induction motors  [感应电动机]
Empirical mode decomposition  [ 经验模态分解]
Maximum correlation kurtosis deconvolution  [ 最大相关性峰度反卷积]
Fault diagnosis  [ 故障诊断]
Minimum entropy deconvolution adjusted  [ 最小熵去卷积校正]
出版年： 2023
文集名： 1st International Conference on Electrical Engineering and Advanced Technology (ICEEAT 2023)
首届国际电气工程与先进技术会议（ICEEAT 2023）
会议名： 1st International Conference on Electrical Engineering and Advanced Technology (ICEEAT 2023)
首届国际电气工程与先进技术会议（ICEEAT 2023）
届 次： 1st
摘 要： The significance of induction motors has been greatly heightened due to the rapid advancements in modern industrial production. Therefore, a fault diagnosis based on vibration signals is therefore critical in industry and academia. In this context, we compared Empirical Mode Decomposition combined with Maximum Correlation Kurtosis Deconvolution (EMD-MCKD) to EMD with Minimum Entropy Deconvolution Adjusted (EMD-MEDA). In order to improve fault diagnosis, we apply to the reconstructed signal the technique of Maximum Correlation Kurtosis Deconvolution (MCKD). Simultaneously, subjected to Minimum Entropy Deconvolution Adjusted (MEDA). Finally, the outcomes are comprehensively analyzed and compared to identify the optimal technique for diagnosing bearing faults. This study is based upon experimental findings, and demonstrates the effectiveness and suitability of the EMD-MEDA technique, clearly promising in bearing fault diagnosis and extract the characteristics of the fault in the signal, by evaluating its accuracy and comparing its performance to EMD-MCKD.

由于现代工业生产迅速发展，感应电机的重要性显著提升。因此，在工业和学术领域，基于振动信号的故障诊断至关重要。在此背景下，本文比较了经验模分解结合最大相关曲率反卷积（EMD-MCKD）与最小熵反卷积调整型经验模分解（EMD-MEDA）。为提高故障诊断的准确性，我们对重构后的信号应用了最大相关曲率反卷积（MCKD）技术，并同时采用最小熵反卷积调整（MEDA）方法。最后，对结果进行综合分析与对比，以确定适用于轴承故障诊断的最佳技术。本研究基于实验数据，验证了EMD-MEDA技术的有效性和适用性，该方法在轴承故障诊断中表现出明显优势，并通过评估其准确性和与EMD-MCKD的性能比较，成功提取出信号中的故障特征。
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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