A Bearing Fault Diagnosis Model based on Minimum Average Composite Entropy and Parallel Attention Mechanism Convolutional Neural Network

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Fig. 7.

Fig. 8.

Fig. 9.

Fig. 10.

Fig. 11.

Fig. 12.

Fig. 13.

Fig. 14.

Fig. 15.

Fig. 16.

Fig. 17.

Fig. 18.

Dataset classification_

Fault location	Failure diameter [mm]	Tag	Dataset
			Training set	Test set
Regular	——	1	70	30
Inner ring	——	2	70	30
Outer ring	90°	3A	70	30
	135°	3B	70	30
Regular	——	4	70	30
Compound failure	outer 90°	5A	70	30
	outer 135°	5B	70	30

Experimental results of 5 dB complex noise comparison_

Model	MACE + PFACNN	RVMD+ DCNN	RVMD+ CNN	E+CNN BiGRU	E+CNN SVM
Accuracy [%]	91.3	80.1	52.1	54.2	48.3
Recall rate [%]	91.6	80.5	52.7	54.9	48.8
F1 [%]	91.4	80.3	52.5	54.6	48.6

Results of ablation experiment_

Module			Model	Accuracy [%]
1	2	3
×	×	√	A	91.2
×	√	√	B	93.7
√	×	√	C	97.4
√	√	×	D	95.3
√	√	√	E	98.9

Experimental parameters_

Inner diameter [mm]	Pitch diameter [mm]	Thickness [mm]	Outer diameter [mm]	Rolling diameter [mm]	Contact angle [°]
25	39	15	52	8	0

Fault diagnosis results_

Category	Accuracy [%]	Category	Accuracy [%]
1	100	6	100
2	100	7	100
3	100	8	99.3
4	100	9	99.3
5	100	10	100

Results of 0 dB white noise comparison test_

Model	MACE + PFACNN	IF+ CNN	M+ CNN	E+CNN	E+ CNNSVM
Accuracy [%]	89.2	64.5	51.6	67.8	72.4
Recall rate [%]	89.8	64.9	52.1	68.2	72.9
F1 [%]	89.7	64.6	51.8	67.7	72.6

Generalization experiment results – Accuracy [%]_

Model	MACE + PFACNN	E+CNN	E+SVM	IF+CNN	M+ DCNN
3A-3B	97.98	91.14	91.12	89.99	90.11
3B-3A	93.64	92.13	88.96	90.96	89.11
5A-5B	93.87	90.11	89.13	86.57	90.40
5B-5A	92.01	89.41	90.11	88.76	89.13
Mean	94.37	90.69	89.83	89.07	89.68