Читать книгу Industry 4.1 - Группа авторов - Страница 59

SFs extracted from time‐domain signals are very intuitive to understand how the signals change in the past or at a specific time. Statistical SFs and correlation‐based SFs are two main methods used to describe these changes in practical manufacturing applications.

In signal processing, SFs of the statistical description can essentially identify changes from the shape of the waveform; while SFs of cross‐correlation and autocorrelation, based on the Pearson product‐moment correlation coefficient [13], investigate similarity relationship between time‐varying signals. The feature extraction methods of statistical SFs, cross‐correlation SFs, and autocorrelation SFs are presented as follows.