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基于深度學(xué)習(xí)的故障診斷方法綜述

文成林,  呂菲亞

文成林, 呂菲亞. 基于深度學(xué)習(xí)的故障診斷方法綜述[J]. 電子與信息學(xué)報(bào), 2020, 42(1): 234-248. doi: 10.11999/JEIT190715
引用本文: 文成林, 呂菲亞. 基于深度學(xué)習(xí)的故障診斷方法綜述[J]. 電子與信息學(xué)報(bào), 2020, 42(1): 234-248. doi: 10.11999/JEIT190715
Chenglin WEN, Feiya Lü. Review on Deep Learning Based Fault Diagnosis[J]. Journal of Electronics & Information Technology, 2020, 42(1): 234-248. doi: 10.11999/JEIT190715
Citation: Chenglin WEN, Feiya Lü. Review on Deep Learning Based Fault Diagnosis[J]. Journal of Electronics & Information Technology, 2020, 42(1): 234-248. doi: 10.11999/JEIT190715

基于深度學(xué)習(xí)的故障診斷方法綜述

doi: 10.11999/JEIT190715 cstr: 32379.14.JEIT190715
  • 1.

    杭州電子科技大學(xué)自動(dòng)化學(xué)院 杭州 310018

  • 2.

    安陽(yáng)師范學(xué)院軟件學(xué)院 安陽(yáng) 455000

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(U1509203, 61751304, 61573137, 61673160)，浙江省重點(diǎn)項(xiàng)目(LZ16F030002)
詳細(xì)信息
    作者簡(jiǎn)介:

    文成林：男，1963年生，教授，主要研究方向?yàn)楣收显\斷，多目標(biāo)跟蹤，信息融合等

    呂菲亞：女，1991年生，博士，講師，主要研究方向?yàn)楣收显\斷，機(jī)器學(xué)習(xí)，信息融合等

    通訊作者:

    呂菲亞　lvfeiya0215@126.com

  • 1)本文所討論的故障實(shí)時(shí)診斷與預(yù)測(cè)技術(shù)均假定故障可被感知并能被分離，可被感知是指故障在一定程度上影響系統(tǒng)的狀態(tài)和輸出，能被分離是指依據(jù)現(xiàn)有信息可以指示故障發(fā)生部位和發(fā)生機(jī)理。2)機(jī)理分析方法指是通過對(duì)系統(tǒng)內(nèi)部原因/機(jī)理的分析研究，從而找出其發(fā)展變化規(guī)律的一種科學(xué)研究方法，依賴于因果關(guān)系的提取與表征，適用于輸入、輸出及狀態(tài)變量較少的系統(tǒng)[6]，包括分析方法和統(tǒng)計(jì)方法。3)特征工程指的是把原始數(shù)據(jù)轉(zhuǎn)變?yōu)槟Ｐ偷挠?xùn)練數(shù)據(jù)的過程，目的是獲取更好的訓(xùn)練數(shù)據(jù)特征，包括特征構(gòu)建、特征提取、特征選擇3個(gè)部分。4)雖然神經(jīng)網(wǎng)絡(luò)可以以任意精度逼近非線性函數(shù)[18]，但是面對(duì)復(fù)雜工業(yè)過程的高維、非高斯分布、非線性、時(shí)變、多模態(tài)等特性，傳統(tǒng)的神經(jīng)網(wǎng)絡(luò)方法多是從逼近論的角度擬合監(jiān)測(cè)數(shù)據(jù)并進(jìn)行特征提取，受限于網(wǎng)絡(luò)結(jié)構(gòu)訓(xùn)練算法和計(jì)算復(fù)雜度的影響，通常只是設(shè)置2到3個(gè)隱層，降低了逼近的精度。
  • 5)假設(shè)特征圖長(zhǎng)寬相同.

  • 中圖分類號(hào): TP274

Review on Deep Learning Based Fault Diagnosis

  • 1.

    Institute of Automation, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

  • 2.

    School of Software Engineering, Anyang Normal University, Anyang 455000, China

Funds: The National Natural Science Foundation of China (U1509203, 61751304, 61573137, 61673160), Zhejiang Provincial Foundation (LZ16F030002)
  • 摘要:

    海量高維度的過程測(cè)量信息給傳統(tǒng)的故障診斷算法帶來極大的計(jì)算復(fù)雜度和建模復(fù)雜度，且傳統(tǒng)診斷算法存在難以利用高階量進(jìn)行在線估計(jì)的不足。鑒于深度學(xué)習(xí)技術(shù)強(qiáng)大的數(shù)據(jù)表示學(xué)習(xí)和分析能力，基于深度學(xué)習(xí)的故障診斷引起了工業(yè)界和學(xué)術(shù)界的廣泛關(guān)注，并促使智能過程控制更加自動(dòng)化和有效。該文從方法上將基于深度學(xué)習(xí)的故障診斷技術(shù)分為：基于棧式自編碼的故障診斷方法、基于深度置信網(wǎng)絡(luò)的故障診斷方法、基于卷積神經(jīng)網(wǎng)絡(luò)的故障診斷方法及基于循環(huán)神經(jīng)網(wǎng)絡(luò)的故障診斷方法4類，分別進(jìn)行了回顧和總結(jié)，最后從數(shù)據(jù)預(yù)處理、深度網(wǎng)絡(luò)設(shè)計(jì)和決策3個(gè)層面對(duì)這一領(lǐng)域進(jìn)行展望，提出了“集成創(chuàng)新”、“數(shù)據(jù)+知識(shí)”和“多技術(shù)融合”等故障診斷思想，闡明基于深度學(xué)習(xí)技術(shù)進(jìn)行復(fù)雜系統(tǒng)的故障診斷仍具有巨大潛力。

    Abstract:

    The massive high-dimensional measurements accumulated by distributed control systems bring great computational and modeling complexity to the traditional fault diagnosis algorithms, which fail to take advantage of the higher-order information for online estimation. In view of its powerful ability of representation learning, deep learning based fault diagnosis is extensively studied, both in academia and in industry, making intelligent process control more automated and effective. In this paper, deep learning based fault diagnosis is reviewed and summarized as four parts, i.e., stacked auto-encoder based fault diagnosis, deep belief network based fault diagnosis, convolutional neural network based fault diagnosis, and recurrent neural network based fault diagnosis. Furthermore, some necessity and potential trends, "integrated innovation", "data + knowledge" and "information fusion", are discussed from the view of data preprocessing, network design and decision.

    • HTML全文

  • 圖  1  數(shù)據(jù)驅(qū)動(dòng)的故障診斷框架

    圖  2  基于深度學(xué)習(xí)的故障診斷研究思路匯總

    圖  3  基于深度學(xué)習(xí)的故障診斷方法分類

    圖  4  棧式自編碼網(wǎng)絡(luò)的結(jié)構(gòu)

    圖  5  基于受限玻爾茲曼機(jī)的深度網(wǎng)絡(luò)結(jié)構(gòu)

    圖  6  卷積神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)

    圖  7  循環(huán)神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)

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  • 收稿日期:  2019-09-17
  • 修回日期:  2019-12-02
  • 網(wǎng)絡(luò)出版日期:  2019-12-10
  • 刊出日期:  2020-01-21

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