Table 6.

Performance results of the CNN model for the two datasets, using random cross validation executed 5 times (we report the mean and standard deviation of all executions).

	Accuracy (%)1	Precision (%)1	Recall (%)1	f1-score (%)1
A) Original dataset	$\begin{array}{c}98.43\hfill \\ (\sigma =0.88)\hfill \end{array}$	$\begin{array}{c}98.40\hfill \\ (\sigma =0.89)\hfill \end{array}$	$\begin{array}{c}98.40\hfill \\ (\sigma =0.89)\hfill \end{array}$	$\begin{array}{c}98.40\hfill \\ (\sigma =0.89)\hfill \end{array}$
(B) Bearing fault dataset [55]	$\begin{array}{c}91.81\hfill \\ (\sigma =5.88)\hfill \end{array}$	$\begin{array}{c}93.60\hfill \\ (\sigma =4.22)\hfill \end{array}$	$\begin{array}{c}91.60\hfill \\ (\sigma =5.77)\hfill \end{array}$	$\begin{array}{c}91.20\hfill \\ (\sigma =6.69)\hfill \end{array}$

¹Classification metrics in the test set. In the training set, the CNN model has achieved a score of 100.00% ($\sigma =0.00$) in all metrics.