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If the machining parameters (such as feed rate, spindle speed, depth of cut, etc.) are fixed and the precision after machining is within specifications, then the machining operations are a kind of a quasi‐static condition [8]. Under this condition, Yang et al. [8] summarized that the nine most common SFs for various types of sensor signals are derived from all the elements of .

For the de‐noised time‐domain signals that contain N elements (data length = N), these nine SFs are represented and defined as in Table 2.3, including:

 Arithmetic mean or average (avg)

 Standard derivation (std)

 Maximal magnitude (max)

 Minimal magnitude (min)

 Peak‐to‐peak amplitude for presenting difference of peaks (ptp)

 Kurtosis for measuring the peakedness of signals (kurt)

 Skewness for measuring the asymmetry of signals (skew)

 Root mean square value for indicating the weighting effect of variances (RMS)

 Crest factor for representing how extreme the peaks are in a waveform (CF)

Table 2.3 Definition of time‐domain SFs.

SF	Formula	Description
avg
std
max
min
ptp
kurt
skew
RMS
CF

As such, these nine SFs can be used as a feature set based on expert knowledge. Suppose that one vibration sensor and three current sensors are installed as the sensor fusion example illustrated in Figure 2.13, then there are 36 SFs in total because each sensor has nine SFs.

These 36 SFs may be adopted as the input variables of any intelligent system. However, redundancy, irrelevancy, and/or dependency may exist among these 36 SFs, which may deteriorate the model accuracy; and the more SFs, the more training samples are needed [4]. Conventional feature selection methods [6–8] can be applied to automatically search for key SFs to reduce the number of SFs during the model‐building and model‐refreshing processes so as to improve the model accuracy. However, due to the dynamic nature of these methods, the content of key SFs could vary after applying automatic search in each model refreshing, which might not be appropriate for implementation considerations.

For easy implementation, a fixed and concise set of SFs is required to represent the significance of the entire manufacturing process. Therefore, an expert‐knowledge‐based (EK‐based) selection procedure to find a fixed and concise set of SFs is illustrated below.