Journal article
Active Broad-Transfer Learning Algorithm for Class-imbalanced Fault Diagnosis
IEEE transactions on instrumentation and measurement, Vol.72, pp.1-16
2023
DOI: 10.1109/TIM.2022.3227995
Abstract
Knowledge transfer with class-imbalanced data is a challenge in predictive maintenance and fault diagnosis. Deep learning algorithms have provided promising results in fault diagnosis. However, their prediction performance is affected by class-imbalanced data in cross-domain tasks. Broad learning algorithms present promising performance in handling class-imbalanced domain-adaptation (CIDA) problems. In the presence of a domain shift, active broad transfer for class-imbalanced learning (ABTCI), an active broad-transfer learning algorithm for CIDA, is proposed. First, the ABTCI algorithm extracts the time–frequency features and feeds them into a recurrent cell to capture spatial–temporal features. Subsequently, it augments the feature space using a sparse autoencoder and an orthogonal mapping projector. By solving the ridge regression problem, the classifier is initialized. Next, the algorithm samples the target data with reliable pseudo-labels and synthesizes new data using random intraclass interpolation among the minor classes containing source and target knowledge. Finally, the classifier is updated using an incremental continuous learning strategy. The performance of the ABTCI algorithm is validated using three datasets, which include 20 class-balanced and 27 class-imbalanced transfer tasks. The performance of the proposed algorithm benchmarked against other deep-learning algorithms is promising.
Details
- Title: Subtitle
- Active Broad-Transfer Learning Algorithm for Class-imbalanced Fault Diagnosis
- Creators
- Guokai Liu - Huazhong University of Science and TechnologyWeiming Shen - Huazhong University of Science and TechnologyLiang Gao - Huazhong University of Science and TechnologyAndrew Kusiak - Department of Industrial and System Engineering, 4627 Seamans Center, The University of Iowa, Iowa City, IA, USA
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on instrumentation and measurement, Vol.72, pp.1-16
- DOI
- 10.1109/TIM.2022.3227995
- ISSN
- 0018-9456
- eISSN
- 1557-9662
- Grant note
- DOI: 10.13039/501100012166, name: National Key Research and Development Program of China, award: 2022YFE0114200; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 52188102; DOI: 10.13039/501100004543, name: China Scholarship Council, award: 201906160078
- Language
- English
- Electronic publication date
- 12/09/2022
- Date published
- 2023
- Academic Unit
- Nursing; Industrial and Systems Engineering
- Record Identifier
- 9984353739202771
Metrics
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