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In view of selecting the SFs of a vibration sensor (i.e. accelerometer), since the machining quality is affected directly by the tool states, SFs that can accurately monitor tool status should be selected. In high‐speed machining operations, a serious increase in cutting energy generated due to tool breakage or flank wear will amplify vibration magnitude that can be detected by the max, RMS, and avg of the vibration signal. These three SFs are crucial to the detection of vibration amplitude and energy variance between workpieces and tools.

A rolling bearing is one of the most important components widely embedded in machine tools. Therefore, abnormal statuses of rolling bearings may also cause breakdowns in rotating systems and result in serious machining failures. skew and kurt are two useful SFs to detect rolling bearing faults at an early stage. Besides, kurt, which works very well in the whole range from slow to very fast rolling speed, is sensitive enough to provide rich information of incipient faults for characterizing the impact existing in the rolling bearings. Further, std, a kind of precision‐related SF responsible for investigating any small changes during machining, can indicate good correlation with real precision values of workpieces.

Then, the essential and concise SFs of electric‐current signals are investigated. RMS of spindle current can correctly represent dynamic cutting‐force variation for monitoring tool fracture and precision prediction. When dealing with the alternating current (AC), CF is applied for detecting whether an electrical system has the ability to generate a particular current output. In addition, avg can also be used as an SF of tool flute breakage or tool‐wear estimations.

Finally, with the same reason as for vibration signals, max is used for detecting any abnormal current peaks during machining. Accelerometer and current sensors are selected here to demonstrate how to choose essential and concise SFs. By the same token, the essential and concise SFs of other sensors, such as dynamometers, acoustic‐emission sensors, and thermometers, can also be identified. In summary, the six vibration SFs selected include: max, RMS, avg, skew, kurt, and std; while the four current SFs chosen are RMS, avg, max, and CF.