Multi-label classification for simultaneous fault diagnosis of marine machinery: A comparative study. Tan, Y., Zhang, J., Tian, H., Jiang, D., Guo, L., Wang, G., & Lin, Y. Ocean Engineering, 239:109723, November, 2021. ![Multi-label classification for simultaneous fault diagnosis of marine machinery: A comparative study [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Fault diagnosis of marine machinery is of utmost importance in modern ships. The widely used machine learning techniques have made it possible to realize intelligent diagnosis by using large amounts of sensory data. However, the detection of simultaneous faults is still a challenge in the absence of simultaneous fault data. Multi-label classification has recently gained popularity in simultaneous fault diagnosis with promising results. The contribution of this work is to carry out a comparative study of several state-of-the-art multi-label classification algorithms for simultaneous fault diagnosis of marine machinery based on single fault data. The proposed method is experimentally validated with a dataset generated from a real data validated simulator of a Frigate. The experimental results show the effectiveness of the proposed method, which can provide decision support for the application of multi-label classification in the simultaneous fault diagnosis of similar marine systems.
@article{tan_multi-label_2021,
title = {Multi-label classification for simultaneous fault diagnosis of marine machinery: {A} comparative study},
volume = {239},
issn = {0029-8018},
shorttitle = {Multi-label classification for simultaneous fault diagnosis of marine machinery},
url = {https://www.sciencedirect.com/science/article/pii/S0029801821010921},
doi = {10.1016/j.oceaneng.2021.109723},
abstract = {Fault diagnosis of marine machinery is of utmost importance in modern ships. The widely used machine learning techniques have made it possible to realize intelligent diagnosis by using large amounts of sensory data. However, the detection of simultaneous faults is still a challenge in the absence of simultaneous fault data. Multi-label classification has recently gained popularity in simultaneous fault diagnosis with promising results. The contribution of this work is to carry out a comparative study of several state-of-the-art multi-label classification algorithms for simultaneous fault diagnosis of marine machinery based on single fault data. The proposed method is experimentally validated with a dataset generated from a real data validated simulator of a Frigate. The experimental results show the effectiveness of the proposed method, which can provide decision support for the application of multi-label classification in the simultaneous fault diagnosis of similar marine systems.},
language = {en},
urldate = {2021-10-11},
journal = {Ocean Engineering},
author = {Tan, Yanghui and Zhang, Jundong and Tian, Hui and Jiang, Dingyu and Guo, Lei and Wang, Gaoming and Lin, Yejin},
month = nov,
year = {2021},
keywords = {Marine machinery, Multi-label classification, Simultaneous fault diagnosis},
pages = {109723},
}
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