The supervised learning methods discussed in Chaps. 3 , 4 , and 5 form the backbone of many successful machine learning applications in vibration-based fault diagnosis and prognostics. However, their effectiveness hinges on a critical prerequisite: the availability of large, accurately labeled datasets. This means that for each vibration sample (or its derived features) in the training set, a known ground truth label (e.g., “healthy,” “inner race fault,” “RUL = 150 h”) must be provided.