An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis Article Swipe
Shaobo Li
,
Guokai Liu
,
Xianghong Tang
,
Jianguang Lu
,
Jianjun Hu
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/s17081729
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/s17081729
Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s17081729
- https://www.mdpi.com/1424-8220/17/8/1729/pdf?version=1507519467
- OA Status
- gold
- Cited By
- 204
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2741289421
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2741289421Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s17081729Digital Object Identifier
- Title
-
An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault DiagnosisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-07-28Full publication date if available
- Authors
-
Shaobo Li, Guokai Liu, Xianghong Tang, Jianguang Lu, Jianjun HuList of authors in order
- Landing page
-
https://doi.org/10.3390/s17081729Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/17/8/1729/pdf?version=1507519467Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/17/8/1729/pdf?version=1507519467Direct OA link when available
- Concepts
-
Convolutional neural network, Fault (geology), Bearing (navigation), Computer science, Fusion, Artificial intelligence, Deep learning, Artificial neural network, Pattern recognition (psychology), Geology, Seismology, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
204Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 15, 2024: 24, 2023: 29, 2022: 25, 2021: 29Per-year citation counts (last 5 years)
- References (count)
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49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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