Table 4 Extracted features for bearing RUL prediction.

From: Light convolutional neural network by neural architecture search and model pruning for bearing fault diagnosis and remaining useful life prediction

Number

Time domain features

Number

Frequency domain features

1–3

Mean

19–24

\(A_m^{BPFO_i}{(t)}\) (i = 1,2,3)

4–6

Standard deviation

25–30

\(A_m^{BPFI_i}{(t)}\) (i = 1,2,3)

7–9

Sknewness

31–32

\(\sum _{i=1}^{3}A_m^{BPFI_i}(t)\)

10–12

Kurtosis

33–34

\(\sum _{i=1}^{3}A_m^{BPFO_i}(t)\)

13–15

Peak-to-peak value

35–36

\(\sum _{t=0}^{t}\sum _{i=1}^{3}A_m^{BPFI_i}(t)\)

16–18

Root mean square

37–38

\(\sum _{t=0}^{t}\sum _{i=1}^{3}A_m^{BPFO_i}(t)\)