Signal Processing Algorithms Like Ensemble Empirical Mode Decomposition and Statistical Analysis-Based Tool Chatter Severity Prediction
Yogesh Shrivastava, Prashant Kumar Shrivastava, Durgesh Nandan
Abstract
The identification of faults in machinery is a very emerging trend. In the last few decades, regenerative tool chatter and its adverse effects have been explored by many researchers. However, a lot of work has to be done within this domain. A new methodology has been presented in the present work to determine the chatter severity while machining. The methodology has three stages. In the first stage, numerous experiments have been carried out, and associated signals have been captured. Thereafter, in the second stage, preprocessing of the recorded signals have been done using “ensemble empirical mode decomposition” to filter out the contaminations from the signals. The intrinsic mode functions have been further evaluated using statistical indicators viz. chatter index and absolute mean amplitude. In the third stage, these statistical indicators have been examined concerning the input parameters to identify the variation in the responses and chatter severity. The proposed methodology seems helpful for the researchers to identify the chatter features concerning variation in input parameters.