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機器學(xué)習在故障檢測與診斷領(lǐng)域應用綜述
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國防科技大學(xué) 空天科學(xué)學(xué)院,湖南 長(cháng)沙 41005

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TP206.1

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Overview of Application on Fault Detection and Diagnosis Based forMachine Learning
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    摘要:

    機器學(xué)習已經(jīng)成為當前技術(shù)發(fā)展熱點(diǎn),由于機器學(xué)習具有快速處理大量數據、分析提取有效信息等優(yōu)點(diǎn),因此在故障檢測與診斷技術(shù)中受到了越來(lái)越多的關(guān)注。本文系統介紹了機器學(xué)習和故障檢測與診斷的概念、分類(lèi),深入了解了基于PCA和隨機森林的故障檢測方法和國內研究現狀,以及基于決策樹(shù)、支持向量機以及神經(jīng)網(wǎng)絡(luò )的故障診斷方法和國內外研究現狀,其中重點(diǎn)介紹了卷積神經(jīng)網(wǎng)絡(luò )和遞歸神經(jīng)網(wǎng)絡(luò )的應用,并對機器學(xué)習算法在故障檢測與診斷應用前景進(jìn)行了展望。

    Abstract:

    Machine Learning has been a popular and well-investigated topic technology, especially because machine learning has the advantages of fast processing large amounts of data, analyzing and extracting effective information and so on, it has more and more attention to fault detection and diagnosis. This paper systematically analyzes the concepts and classifications of machine learning and fault detection and diagnosis, and deeply comprehends the method of fault detection and diagnosis based on machine learning. Then it focuses on the fault detection methods including PCA and random forests and the research status at home and abroad; it also introduces fault diagnosis methods including decision trees, support vector machines and neural networks and the research status at home and abroad, especially convolutional neural networks and recurrent neural networks, discusses machine learning of future applications in fault detection and diagnosis.

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翟嘉琪,楊希祥,程玉強,李亮.機器學(xué)習在故障檢測與診斷領(lǐng)域應用綜述計算機測量與控制[J].,2021,29(3):1-9.

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  • 收稿日期:2020-07-14
  • 最后修改日期:2020-08-14
  • 錄用日期:2020-08-14
  • 在線(xiàn)發(fā)布日期: 2021-03-24
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