Abstract The Hilbert–Huang transform (HHT) can adaptively delineate complex non-linear, non-stationary signals when used as the Hilbert–Huang marginal spectrum through empirical mode decomposition (EMD) and the Hilbert transform, to highlight local features of signals. Characterized by high resolution, the Hilbert marginal spectrum has been widely applied in mechanical signal processing and fault diagnosis. In the research, an HHT based on the improved EMD was proposed to analyze the cutting force, vibration acceleration (AC), and acoustic emission (AE) signals during tool wear in the milling process. At first, the collected signals were subjected to range analysis, which revealed that tool wear was closely related to the signals collected during the cutting process. Then, EMD was applied to the signals, followed by variance analysis after calculating the energies of each intrinsic mode function (IMF) component. Afterwards, the IMF components significantly influenced by wear degree, while slightly influenced by the three cutting factors (cutting velocity, feed per tooth, and cutting depth), were selected as IMF sensitive to the degree of wear. The HHT was finally applied to the sensitive IMF components of signals containing major tool wear information, thus obtaining the Hilbert marginal spectra of the signals, which were able to reflect the changes in signal amplitude with frequency. On the basis of the Hilbert marginal spectrum, the method defined the feature energy function which was then used as the eigenvector for predicting tool wear in milling processes. The analysis of signals in four tool wear states indicated that the method can extract salient tool wear features.

中文翻译：

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基于希尔伯特边际谱的铣刀磨损特征提取方法

摘要 Hilbert-Huang 变换(HHT) 通过经验模态分解(EMD) 和Hilbert 变换作为Hilbert-Huang 边缘谱可以自适应地描绘复杂的非线性、非平稳信号，突出信号的局部特征。希尔伯特边际谱具有高分辨率的特点，在机械信号处理和故障诊断中得到了广泛的应用。在研究中，提出了一种基于改进 EMD 的 HHT 来分析铣削过程中刀具磨损过程中的切削力、振动加速度 (AC) 和声发射 (AE) 信号。首先对采集到的信号进行范围分析，结果表明刀具磨损与切削过程中采集到的信号密切相关。然后，将 EMD 应用于信号，然后在计算每个本征模式函数 (IMF) 分量的能量后进行方差分析。之后，IMF 分量受磨损程度的影响显着，而受三个切削因素（切削速度、每齿进给量和切削深度）的影响较小，被选为对磨损程度敏感的 IMF。最后将HHT应用于包含主要刀具磨损信息的信号的敏感IMF分量，从而获得信号的希尔伯特边际谱，能够反映信号幅度随频率的变化。该方法基于希尔伯特边缘谱定义了特征能量函数，然后将其用作预测铣削过程中刀具磨损的特征向量。