Abstract
The modern equipment is developing in the direction of complexity and integration, and the traditional diagnosis methods can’t adapt to the changes of equipment. With the development of deep learning, deep learning algorithms are gradually used in the field of fault diagnosis. Most of these algorithms ignore the correlation between data when extracting features from multi-source data. In response to the above problem, this paper proposes a fault diagnosis algorithm based on Bi-directional Long Short-Term Memory, which can extract the correlation features between multi-source data. Through the experiment on the simulated operating data of the heating furnace of steel rolling, the algorithm of this paper has a high fault recognition rate.
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Acknowledgements
The authors would like to express appreciation to mentors in Shanghai University and Huayu-intelligent Equipment Technology Co., Ltd for their valuable comments and other helps. Thanks for the pillar program supported by Shanghai Science and Technology Committee of China (No. 19511105200).
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Yin, B., Li, X., Liu, L., Wu, F. (2021). Research on Fault Diagnosis Algorithm Based on Bi-directional Long Short-Term Memory. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation X. IWAMA 2020. Lecture Notes in Electrical Engineering, vol 737. Springer, Singapore. https://doi.org/10.1007/978-981-33-6318-2_34
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DOI: https://doi.org/10.1007/978-981-33-6318-2_34
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