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Feature extraction [6–8] is the process to generate a smaller linear or nonlinear combination set to represent the original high‐dimensional data set. Thus, the de‐noised sensing signals need to be transformed into meaningful signal features (SFs), which can adequately describe the physical meaning of the signal and maintain relevant information of the machining operations [6]. However, monitoring machining conditions based on a single SF is not enough [7]. To properly describe machining precisions, a set of multiple SFs is required to provide further insight into coordination [8].

This section introduces feature extraction approaches that are commonly used in the time, frequency, and time–frequency domains. Feature selection is a process used to define a small and concise feature subset through the removal of redundant features from a feature set and it is introduced in Section 2.3.3.1(A.1). In addition, an Autoencoder (AEN) as a popular ANN‐based feature extraction method [11, 12] is introduced in Section 2.3.3.4.