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基于RNN的故障預測算法及在GIS上的應用
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河海大學(xué)物聯(lián)網(wǎng)工程學(xué)院

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TP18

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Fault Prediction Algorithm Based on RNN and Its Application of GIS
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    摘要:

    隨著(zhù)工業(yè)化程度的提高,設備的故障預測的重要性日趨提高。提出了一種基于循環(huán)神經(jīng)網(wǎng)絡(luò )(RNN)的故障預測算法,通過(guò)數據訓練,充分發(fā)掘了RNN對時(shí)間序列數據的擬合能力。RNN故障預測模型由數據處理模塊和神經(jīng)網(wǎng)絡(luò )識別模塊組成。在數據處理模塊中,采用數學(xué)函數分配的方法建立了RNN 模型的訓練樣本和測試樣本。在神經(jīng)網(wǎng)絡(luò )識別模塊中,針對當前故障預測技術(shù)中異常點(diǎn)難以確定的問(wèn)題,應用了一種逐步逼近的神經(jīng)網(wǎng)絡(luò )訓練方法。最后利用氣體絕緣開(kāi)關(guān)(GIS)故障數據對該算法進(jìn)行了驗證,結果表明,該方法可以在故障發(fā)生前檢測到故障發(fā)生趨勢,進(jìn)而實(shí)現故障預測,并且能在逐步訓練中確定異常點(diǎn)的位置。

    Abstract:

    With the development of industrialization, the importance of equipment fault prediction is increasing day by day. A fault prediction algorithm based on Recurrent Neural Network (RNN) is proposed. The fitting ability of RNN to time series data is fully explored through data training. The fault prediction model of the RNN is composed of a data processing module and a neural network recognition module. In the data processing module, the method of mathematical function assignment is used to build the training and test samples of the RNN model. In the Neural network recognition module, due to the problem that the change point in current fault prediction technology is difficult to determine, a neural network training method based on successive approximation is used. At last,the RNN fault prediction algorithm is verified by Gas insulated switchgear(GIS) fault data. The results show that the trend of fault can be detected by this method before it occurs, the fault prediction can be realized, and the change point can be located in the gradual approximation training.

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張子賢,李敏,苗紅霞,孫寧.基于RNN的故障預測算法及在GIS上的應用計算機測量與控制[J].,2020,28(12):27-31.

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  • 收稿日期:2020-05-06
  • 最后修改日期:2020-05-16
  • 錄用日期:2020-05-18
  • 在線(xiàn)發(fā)布日期: 2020-12-15
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