Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

Table 1

Feature matrix of some samples.

	Class	Variance	Kurtosis	Mean	Std	Skewness	Peak	Median	RMS	CF	factor
Train; set	1	0.0214	16.1539	0.0011	0.0870	2.0854	0.5870		0.1377	8.3919	0.0873
	1	0.1115	12.4934	0.0011	0.0984	2.0647	0.6643		0.1063	8.7519	0.1421
	1	0.0075	12.2625	0.0011	0.1346	2.0755	0.8143		0.1148	8.2310	0.2199
	2	0.0340	15.0917	0.0012	0.1843	2.6764	1.2198		0.1843	6.6173	0.2248
	2	0.0098	10.7345	0.0009	0.0988	1.6925	0.6694		0.0998	6.7763	0.0661
	2	0.1396	20.1365	0.0010	0.3736	3.4306	2.6430		0.3736	7.0734	0.9875
	3	0.0886	28.7487	0.0007	0.3918	4.1160	2.5614		0.2149	6.3282	0.9030
	3	0.1843	26.8139	0.0011	0.5596	4.0948	3.2983		0.3269	4.9603	1.6723
	3	0.2343	33.8579	0.0010	0.3422	3.4560	2.4916		0.1991	3.7395	1.0920
Test; set	1	0.0274	12.0830	0.0010	0.0863	2.0656	0.9590		0.1096	8.6386	0.1770
	1	0.0559	13.7078	0.0008	0.0662	2.4363	0.7831		0.0959	6.7902	0.0352
	1	0.0088	13.0394	0.0010	0.1602	2.1901	1.2309		0.1201	6.8103	0.0270
	2	0.0934	17.2709	0.0012	0.1513	0.5766	0.6190		0.2841	9.1493	0.9153
	2	0.0059	24.5557	0.0011	0.1197	0.8212	2.0096		0.1739	9.8313	0.7793
	2	0.1178	18.0737	0.0011	0.3140	1.0114	2.2261		0.1789	10.6499	0.5463
	3	0.1387	34.2424	0.0009	0.3473	4.3834	3.3007		0.4638	9.3689	1.4088
	3	0.3126	28.7760	0.0007	0.3842	4.1692	1.8429		0.3311	10.6652	0.5623
	3	0.2959	29.1322	0.0010	0.4585	3.3189	2.3107		0.1846	4.4879	1.2974