Application of spindle power signals in tool condition monitoring based on HHT algorithm

  • Bin ShenEmail author
  • Yufei Gui
  • Biao Chen
  • Zichao Lin
  • Qi Liu
  • Qizheng Liu


In the present study, we present the application of spindle power signals in tool condition monitoring (TCM) under different cutting conditions based on the Hilbert-Huang transform (HHT) algorithm. We extracted two features from the original collected data using the HHT algorithm to detect the tool wear and conducted six sets of cutting experiments to verify the feasibility of this tool condition monitoring method. The results show that these features are highly correlated with the wear state of cutting tools, regardless of the cutting parameters, workpiece materials, and machining methods. The calculated correlation coefficients between the extracted features and the actual tool wear reach 0.79–0.98. This demonstrates that the HHT algorithm is suitable for extracting features from the spindle power signals to construct the online tool condition monitoring system.


Tool condition monitoring (TCM) Spindle power signal Hilbert-Huang transform (HHT) 


Funding information

This study received financial support from the Ministry of Industry and Information Technology of China (Grant No. CDGC01-KT0505) and the National Science and Technology Major Project of China (Grant Nos. 2018ZX04011001 and 2018ZX04005001-002).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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