Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN

Table 6

Performance estimation result of the testing dataset for eight state-of-the-art models.

Methods	Scores				RMSE
	C1	C2	C3	C4	C1	C2	C3	C4
DCNN [35]	5.572 ± 10.297	4.835 ± 10.112	5.214 ± 10.822	5.713 ± 10.640	44.694 ± 2.558	45.206 ± 2.353	44.967 ± 2.301	44.515 ± 2.140
SVR [34]	16.997 ± 9.125	16.935 ± 9.002	17.279 ± 8.910	17.529 ± 8.812	39.012 ± 1.213	39.245 ± 1.221	38.914 ± 1.208	38.839 ± 1.194
DBN [38]	38.114 ± 5.198	38.202 ± 5.109	38.883 ± 4.950	39.163 ± 4.792	30.891 ± 1.315	31.048 ± 1.382	31.008 ± 1.213	30.427 ± 1.290
RDN [36]	31.341 ± 9.914	31.840 ± 9.757	32.019 ± 9.857	32.498 ± 9.309	29.890 ± 1.811	29.784 ± 1.887	29.560 ± 1.891	29.216 ± 1.921
MSCNN [37]	40.152 ± 7.486	40.274 ± 7.397	39.914 ± 8.152	40.889 ± 7.109	29.407 ± 1.712	28.903 ± 1.664	29.729 ± 1.677	28.597 ± 1.402
CLSTM [24]	45.331 ± 4.980	45.519 ± 4.832	45.712 ± 4.962	45.870 ± 4.954	28.245 ± 1.290	28.552 ± 1.285	28.771 ± 1.297	28.245 ± 1.314
MSCAN [39]	32.497 ± 2.244	32.575 ± 2.225	32.573 ± 2.294	32.814 ± 2.106	28.612 ± 1.104	28.504 ± 1.123	28.482 ± 1.109	28.374 ± 1.011
RF [34]	40.126 ± 2.268	40.125 ± 2.740	40.219 ± 2.442	40.370 ± 2.589	21.441 ± 1.071	21.372 ± 1.096	21.457 ± 1.101	21.371 ± 1.002
Proposed	53.962 ± 1.813	52.217 ± 1.872	52.771 ± 1.617	54.248 ± 1.712	19.374 ± 0.923	19.139 ± 0.853	19.424 ± 0.721	19.051 ± 0.804

The bold values express that the proposed method has the best performance.