Abstract
Traditional information equipment’s operating status perception and fault alarms mainly rely on manual and traditional automated operation and maintenance, which have disadvantages such as high cost, low efficiency, and high false alarm rate. In order to achieve accurate fault warning,this paper proposes a dynamic threshold setting mechanism, which can calculate the dynamic threshold interval under the given confidence level based on the prediction results. In order to get the accurate prediction results, the Discrete Wavelet Transform (DWT)-Autoregressive Integrated Moving Average (ARIMA)-Exponentially Weighted Firefly Algorithm(EWFA)-Extreme Learning Machine (ELM) composite model called DWAFE for short is proposed. In this model, the original time series is divided into several subsequences by discrete wavelet transform, and ARIMA model and ELM optimized by EWFA are used for processing according to different stationarity. Finally, the prediction results of each subsequence are integrated by inverse wavelet transform. In addition, we also propose the Exponential Weighted Firefly Algorithm, which greatly improves the optimization performance and convergence speed of the firefly algorithm. Experiments on the core router data of Ningxia electric power company show that this method achieves better performance than Bi-LSTM, GRU and other benchmark models, and can achieve accurate and efficient information equipment fault early warning, thus greatly reducing the human and material costs of enterprises.
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Acknowledgements
This work was supported in part by the Major Program of the National Natural Science Foundation of China (71633006); Research on Key Technologies and Application of Multi-dimensional Perception of Medical Behavior (2020AAA0109600).
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Tan, Y., Gui, J., Wang, K., Chen, Z. (2021). DWAFE: Achieve Accurate AIOps Fault Early Warning. In: Cai, Z., Li, J., Zhang, J. (eds) Theoretical Computer Science. NCTCS 2021. Communications in Computer and Information Science, vol 1494. Springer, Singapore. https://doi.org/10.1007/978-981-16-7443-3_10
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DOI: https://doi.org/10.1007/978-981-16-7443-3_10
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