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Early fault detection of bearings based on adaptive variational mode decomposition and local tangent space alignment

机译：基于自适应变分分解和局部切线空间对准的轴承早期故障检测

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Purpose Early fault detection of bearing plays an increasingly important role in the operation of rotating machinery. Based on the properties of early fault signal of bearing, this paper aims to describe a novel hybrid early fault detection method of bearings. Design/methodology/approach In adaptive variational mode decomposition (AVMD), an adaptive strategy is proposed to select the optimal decomposition level K of variational mode decomposition. Then, a criterion based on envelope entropy is applied to select the optimal intrinsic mode functions (OIMF), which contains most useful fault information. Afterwards, local tangent space alignment (LTSA) is used to denoising of OIMF. The envelope spectrum of the OIMF is used to analyze the fault frequency, thereby detecting the fault. Experiments are conducted in a simulated signal and two experimental vibration signals of bearings to verify the effect of the new method. Findings The results show that the proposed method yields a good capability of detecting bearing fault at an early stage. The new method can extract more useful information and can reduce noise, which can provide better detection accuracy compared with the other two methods. Originality/value An adaptive strategy based on center frequency is proposed to select the optimal decomposition level of variational mode decomposition. Envelope entropy is used to fault feature selection. Combining the advantage of the AVMD-envelope entropy and LTSA, which suits the nature of the early fault signal. So, the proposed method has better detection accuracy, which provides a good alternative for early fault detection of bearings.
机译：目的轴承的早期故障检测在旋转机械的运行中扮演着越来越重要的角色。基于轴承早期故障信号的特性，本文旨在描述一种新型的混合式轴承早期故障检测方法。设计/方法/方法在自适应变分模式分解（AVMD）中，提出了一种自适应策略来选择变分模式分解的最佳分解级别K。然后，基于包络熵的准则被应用于选择最优内在模式函数（OIMF），该函数包含最有用的故障信息。之后，使用局部切线空间对齐（LTSA）对OIMF进行去噪。 OIMF的包络频谱用于分析故障频率，从而检测故障。在轴承的模拟信号和两个实验振动信号上进行了实验，以验证新方法的效果。结果表明，该方法具有良好的早期检测轴承故障的能力。与其他两种方法相比，该新方法可以提取更多有用的信息并可以减少噪声，从而可以提供更好的检测精度。提出了一种基于中心频率的自适应策略来选择变分分解的最优分解水平。包络熵用于故障特征选择。结合了AVMD包络熵和LTSA的优点，这适合早期故障信号的性质。因此，该方法具有较好的检测精度，为轴承的早期故障检测提供了很好的选择。

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