Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (1): 220229.doi: 10.23940/IJPE.19.01.P22.220229
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Shaohua Yang, Guoliang Lu(), Aiqun Wang, and Peng Yan
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Lu Guoliang
Email:luguoliang@sdu.edu.cn
Shaohua Yang, Guoliang Lu, Aiqun Wang, and Peng Yan. HighLevel Feature Extraction based on Correlogram for State Monitoring of Rotating Machinery with Vibration Signals [J]. Int J Performability Eng, 2019, 15(1): 220229.
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Table 2
Computation of selected ten typical features"
Features  Formulations 

RMS  ${{X}_{RMS}}=\sqrt{\frac{\sum\limits_{z=1}^{Z}{{{x}_{z}}^{2}}}{Z}}$ 
Crest factor  ${{X}_{C}}=\frac{\left {{x}_{z}} \right}{{{X}_{RMS}}}$ 
Kurtosis  ${{X}_{K}}=\frac{1}{Z}\sum\limits_{z=1}^{Z}{{{(\frac{{{x}_{z}}\overline{x}}{{{X}_{SD}}})}^{4}}}$ 
Waveform  ${{X}_{W}}=\frac{{{X}_{RMS}}}{\frac{1}{Z}\sum\limits_{z=1}^{Z}{\left {{x}_{z}} \right}}$ 
Skewness  ${{X}_{SK}}=\frac{Z\sum\limits_{z=1}^{Z}{{{({{x}_{z}}\overline{x})}^{3}}}}{(Z1)(Z2){{X}_{SD}}^{3}}$ 
Mean  $\overline{x}=\frac{1}{Z}\sum\limits_{z=1}^{Z}{{{x}_{z}}}$ 
SD  ${{X}_{SD}}=\sqrt{\frac{1}{Z}\sum\limits_{z=1}^{Z}{{{({{x}_{z}}\overline{x})}^{2}}}}$ 
MSE  $MSE=\frac{1}{Z}\sum\limits_{z=1}^{Z}{{{\varepsilon }_{z}}^{2}},{{\varepsilon }_{z}}=observe{{d}_{z}}predicte{{d}_{z}}$ 
Variance  ${{X}_{V}}=\frac{1}{Z}\sum\limits_{z=1}^{Z}{{{({{x}_{z}}\overline{x})}^{2}}}$ 
MP  ${{X}_{MP}}=\max ({{x}_{z}})$ 
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