In Chap. 1 , we laid the groundwork for applying Machine Learning (ML) to vibration problems. We discussed the fundamentals of vibration signals, the typical ML workflow, and introduced basic data handling (sensors, acquisition, initial preprocessing like filtering and normalization) and foundational feature engineering concepts (basic time and frequency domain features). While those foundational concepts are essential for understanding the field, real-world vibration signals captured from industrial machinery are often far more complex than idealized textbook examples.