Baoping Tang publications per year

The chart shows the history of publications by Baoping Tang between 2002 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Baoping Tang published across 24 years, from 2002 to 2025, averaging 9.7 papers a year. Output peaked at 31 publications in 2024. 56 of the 232 publications appeared in the last two years.

No. of publications

10 20 30

2002 2025

232 publications in total across all disciplines

View publications per year as a table

Baoping Tang: publications per year, 2002 to 2025
Year	Publications
2002	2
2003	2
2004	1
2005	4
2006	2
2007	1
2008	1
2009	2
2010	6
2011	4
2012	3
2013	6
2014	10
2015	11
2016	9
2017	9
2018	6
2019	16
2020	15
2021	21
2022	22
2023	23
2024	31
2025	25
Total	232

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Baoping Tang publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Baoping Tang sits on this spectrum.

No. of scientists

50 100 150

47–56 publications 659+

This scientist: 162 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

View publications distribution as a table

Number of Mechanical and Aerospace Engineering scientists by publication count, Research.com 2026 ranking edition. Based on 3,445 ranked scientists.
Publications	Scientists	This scientist
47–56	10
57–66	23
67–76	32
77–86	62
87–96	67
97–106	91
107–116	113
117–126	115
127–136	130
137–146	140
147–156	155
157–166	132	162
167–176	133
177–186	130
187–196	140
197–206	115
207–216	125
217–226	117
227–236	99
237–246	92
247–256	100
257–266	95
267–276	88
277–286	77
287–296	74
297–306	74
307–316	62
317–326	70
327–336	59
337–346	58
347–356	45
357–366	44
367–376	36
377–386	41
387–396	32
397–406	23
407–416	28
417–426	27
427–436	25
437–446	23
447–456	23
457–466	20
467–476	12
477–486	24
487–496	18
497–506	12
507–516	13
517–526	21
527–536	12
537–546	8
547–556	16
557–566	3
567–576	11
577–586	6
587–596	5
597–606	6
607–616	7
617–626	7
627–636	10
637–646	4
647–656	3
657–658	2
659+	100

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Baoping Tang D-index placement in Mechanical and Aerospace Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Mechanical and Aerospace Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Baoping Tang sits on this spectrum.

No. of scientists

50 100 150

30 D-Index 93+

This scientist: 45 D-Index — 57th percentile

57% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

View D-Index distribution as a table

Number of Mechanical and Aerospace Engineering scientists by D-index, Research.com 2026 ranking edition. Based on 3,445 ranked scientists.
D-Index	Scientists	This scientist
30	83
31	113
32	144
33	153
34	189
35	158
36	139
37	127
38	130
39	126
40	104
41	100
42	107
43	101
44	103
45	79	45
46	88
47	70
48	83
49	44
50	64
51	56
52	50
53	48
54	58
55	52
56	48
57	42
58	34
59	42
60	37
61	42
62	44
63	22
64	33
65	29
66	23
67	29
68	24
69	19
70	34
71	26
72	19
73	18
74	19
75	14
76	19
77	8
78	18
79	16
80	12
81	17
82	11
83	16
84	7
85	9
86	8
87	6
88	6
89	7
90	10
91	4
92	4
93+	100

Download as CSV

Best Publications

Deep Residual Shrinkage Networks for Fault Diagnosis

Minghang Zhao;Shisheng Zhong;Xuyun Fu;Baoping Tang

1237
Citations
Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes

Minghang Zhao;Myeongsu Kang;Baoping Tang;Michael Pecht

366
Citations
The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review

Unknown

285
Citations
Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution

Unknown

253
Citations
Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units

Unknown

234
Citations
A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox

Unknown

189
Citations
Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine

Unknown

187
Citations
Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis

Minghang Zhao;Shisheng Zhong;Xuyun Fu;Baoping Tang

183
Citations
Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks

Unknown

176
Citations
Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis

Minghang Zhao;Myeongsu Kang;Baoping Tang;Michael Pecht

172
Citations
Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD

Unknown

152
Citations
Multi-level wavelet packet fusion in dynamic ensemble convolutional neural network for fault diagnosis

Unknown

130
Citations
Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine

Unknown

125
Citations
A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels

Unknown

125
Citations
Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary

Unknown

118
Citations
Status and problems of wind turbine structural health monitoring techniques in China

Unknown

117
Citations
An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes

Unknown

109
Citations
Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction

Unknown

109
Citations
Deep convolution domain-adversarial transfer learning for fault diagnosis of rolling bearings

Unknown

109
Citations
Order spectrogram visualization for rolling bearing fault detection under speed variation conditions

Yi Wang;Yi Wang;Peter W. Tse;Baoping Tang;Yi Qin

93
Citations

Frequent Co-Authors

David He University of Illinois at Chicago

External Links

Personal website of Baoping Tang

If you think any of the details on this page are incorrect, let us know.

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D-Index	Scientists	This scientist
30	83
31	113
32	144
33	153
34	189
35	158
36	139
37	127
38	130
39	126
40	104
41	100
42	107
43	101
44	103
45	79	45
46	88
47	70
48	83
49	44
50	64
51	56
52	50
53	48
54	58
55	52
56	48
57	42
58	34
59	42
60	37
61	42
62	44
63	22
64	33
65	29
66	23
67	29
68	24
69	19
70	34
71	26
72	19
73	18
74	19
75	14
76	19
77	8
78	18
79	16
80	12
81	17
82	11
83	16
84	7
85	9
86	8
87	6
88	6
89	7
90	10
91	4
92	4
93+	100

D-Index	Scientists	This scientist
30	83
31	113
32	144
33	153
34	189
35	158
36	139
37	127
38	130
39	126
40	104
41	100
42	107
43	101
44	103
45	79	45
46	88
47	70
48	83
49	44
50	64
51	56
52	50
53	48
54	58
55	52
56	48
57	42
58	34
59	42
60	37
61	42
62	44
63	22
64	33
65	29
66	23
67	29
68	24
69	19
70	34
71	26
72	19
73	18
74	19
75	14
76	19
77	8
78	18
79	16
80	12
81	17
82	11
83	16
84	7
85	9
86	8
87	6
88	6
89	7
90	10
91	4
92	4
93+	100

Baoping Tang

D-Index & Metrics

Baoping Tang publications per year

Baoping Tang publication distribution in Mechanical and Aerospace Engineering in 2026

Baoping Tang D-index placement in Mechanical and Aerospace Engineering in 2026

Best Publications

Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes

The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review

Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution

Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units

A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox

Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine

Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis

Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks

Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis

Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD

Multi-level wavelet packet fusion in dynamic ensemble convolutional neural network for fault diagnosis

Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine

A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels

Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary

Status and problems of wind turbine structural health monitoring techniques in China

An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes

Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction

Deep convolution domain-adversarial transfer learning for fault diagnosis of rolling bearings

Order spectrogram visualization for rolling bearing fault detection under speed variation conditions

Frequent Co-Authors

External Links

Related Online Degrees & Career Pathways

Best Scientists Citing Baoping Tang

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D-Index	Scientists	This scientist
30	83
31	113
32	144
33	153
34	189
35	158
36	139
37	127
38	130
39	126
40	104
41	100
42	107
43	101
44	103
45	79	45
46	88
47	70
48	83
49	44
50	64
51	56
52	50
53	48
54	58
55	52
56	48
57	42
58	34
59	42
60	37
61	42
62	44
63	22
64	33
65	29
66	23
67	29
68	24
69	19
70	34
71	26
72	19
73	18
74	19
75	14
76	19
77	8
78	18
79	16
80	12
81	17
82	11
83	16
84	7
85	9
86	8
87	6
88	6
89	7
90	10
91	4
92	4
93+	100