Evaluation method of spindle performance degradation based on VMD and random forests

Abstract

As one of the core components of the machine tool, the reliability of spindle is very important for improving machine tool quality. To effectively evaluate the performance degradation degree of the spindle, a method of degradation evaluation of the spindle performance based on variational mode decomposition (VMD) and random forests (RFs) was proposed. Firstly, VMD is used to process the current signal to obtain several modal components. Then, the time domain and frequency domain features of each modes component are calculated as eigenvalues. Finally, the RFs algorithm is used to classify the eigenvalues. The experimental results show that VMD can decompose the signal better and avoid the phenomenon of the modal mixture. The combination of VMD and RFs can accurately and effectively evaluate the performance degradation of the spindle.

Keywords

• vibrations
• frequency-domain analysis
• machine tool spindles
• time-domain analysis
• eigenvalues and eigenfunctions
• machine tools
• feature extraction
• mechanical engineering computing
• condition monitoring
• rf algorithm
• eigenvalues
• modes component
• frequency domain features
• time domain
• modal components
• variational mode decomposition
• degradation evaluation
• performance degradation degree
• machine tool quality
• core components
• random forests
• spindle performance degradation
• vmd