Abstract
There are a large number of communication operation and maintenance equipment in the power IoT scenario. It is difficult to find out when the equipment fails. The traditional method is mainly manual maintenance, but the efficiency is low. In this paper, a neural network-based equipment fault prediction method is proposed. By collecting the time series data of the equipment and transforming it into frequency domain features by using discrete Fourier transform, the neural network model is trained. The experiment shows that the proposed method avoids the complex timing characteristics of the equipment. The problem has improved the ability of equipment failure prediction.
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Acknowledgements
This work is supported by the Science and Technology Project of Guangdong Power Grid Co., Ltd: Research on ubiquitous business communication technology and service mode in smart grid distribution and consumption network-Topic 4: Research on smart maintenance, management and control technology in smart grid distribution and consumption communication network (GDKJXM20172950).
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Li, R., Peng, Z., Yang, X., Zhang, T., Pan, C. (2021). Equipment Fault Prediction Method in Power Communication Network Based on Equipment Frequency Domain Characteristics. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_88
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DOI: https://doi.org/10.1007/978-981-15-3753-0_88
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