Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis
Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis
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特征模式分解：旋转机械故障诊断的新分解理论
DOI：
10.1109/tie.2022.3156156
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发表时间：
2023-02-01
影响因子：
7.7
通讯作者：
Zhang, Dayi
中科院分区：
文献类型：
--
作者：
Miao, Yonghao;Zhang, Boyao;Zhang, Dayi
本文提出了一种新的分解理论--特征模式分解（FMD），用于机械故障的特征提取。所提出的FMD本质上是为了通过所设计的自适应有限脉冲响应（FIR）滤波器分解不同的模式。FMD利用相关峰度的优越性，同时考虑了故障信号的脉冲性和周期性。首先，通过汉宁窗初始化设计FIR滤波器组，为分解提供方向。然后，周期估计和更新过程用于锁定故障信息。最后，在模式选择过程中去除冗余和混合模式。仿真和实验结果表明，FMD具有自适应、准确地分解故障模式，对其它干扰和噪声具有较强的鲁棒性。与最流行的变分模式分解相比，FMD在机械故障特征提取方面具有优越性。
In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose of decomposing the different modes by the designed adaptive finite-impulse response (FIR) filters. Benefitting from the superiority of correlated Kurtosis, FMD takes the impulsiveness and periodicity of fault signal into consideration simultaneously. First, a designed FIR filter bank by Hanning window initialization is used to provide a direction for the decomposition. The period estimation and updating process are then used to lock the fault information. Finally, the redundant and mixing modes are removed in the process of mode selection. The superiority of the FMD is demonstrated to adaptively and accurately decompose the fault mode as well as robust to other interferences and noise using simulated and experimental data collected from bearing single and compound fault. Moreover, it has been demonstrated that FMD has superiority in feature extraction of machinery fault compared with the most popular variational mode decomposition.