A systematic review of data-driven approaches to fault diagnosis and early warning
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DOI: 10.1007/s10845-022-02020-0
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- Jia Luo & Jinying Huang & Hongmei Li, 2021. "A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 407-425, February.
- Ali Rohan, 2022. "Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
- Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
- Li, Yanting & Jiang, Wenbo & Zhang, Guangyao & Shu, Lianjie, 2021. "Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data," Renewable Energy, Elsevier, vol. 171(C), pages 103-115.
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Keywords
Industrial big data; Prognostics and health management; Deep learning; Industry 4.0; Data visualization; Industrial Internet of Things;All these keywords.
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