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基于信號特征提取和卷積神經(jīng)網(wǎng)絡(luò )的軸承故障診斷研究
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溫州大學(xué) 電氣數字化設計技術(shù)國家地方聯(lián)合工程研究中心

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國家自然科學(xué)(61703309);浙江省教育廳科研項目(Y202219004);溫州大學(xué)大學(xué)生創(chuàng )新創(chuàng )業(yè)計劃項目(JWXC2021155)


Research on Bearing Fault Diagnosis Based on Signal Feature Extraction and Convolutional Neural Network
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    摘要:

    軸承是機械設備主要零部件之一,也是機械設備主要故障零部件之一。軸承故障問(wèn)題為機械設備的重點(diǎn),機械設備的使用受到故障軸承的直接影響。針對傳統的卷積神經(jīng)網(wǎng)絡(luò )算法軸承故障診斷效率低下問(wèn)題,本文提出了一種基于信號特征提取和卷積神經(jīng)網(wǎng)絡(luò )的優(yōu)化方法。首先對原始數據信號進(jìn)行時(shí)域和頻域的信號特征提取,獲得有效的故障特征值。之后,使用卷積神經(jīng)網(wǎng)絡(luò )對提取的特征值進(jìn)行故障診斷,完成故障分類(lèi)。本文使用美國凱斯西儲大學(xué)的滾動(dòng)軸承振動(dòng)加速度信號作為數據集,對提出的方法進(jìn)行驗證,得到的故障診斷平均準確率為74.37%,準確率的方差為0.0001;傳統的卷積神經(jīng)網(wǎng)絡(luò )算法故障診斷平均準確率為65.6%;準確率的方差為0.0019。實(shí)驗結果表明,相比傳統的卷積神經(jīng)網(wǎng)絡(luò ),提出的方法對軸承故障診斷的準確率有顯著(zhù)的提高,并且該方法的穩定性更佳,計算時(shí)間更少,綜合性能更佳。

    Abstract:

    Bearing is one of the important parts of mechanical equipment, and it is also one of the main fault parts of mechanical equipment. Bearing failure is the focus of mechanical equipment, and faulty bearings directly affect the use of mechanical equipment. Aiming at the problem of low diagnosis accuracy of bearing fault diagnosis based on traditional convolutional neural network algorithms, this paper proposes an optimization method based on signal feature extraction and convolutional neural network. Firstly, the signal characteristics in the time domain and frequency domain are extracted from the original data signal to obtain the effective fault characteristic values. Then, the convolutional neural network is used to diagnose the extracted feature values and complete the fault classification. In this paper, the rolling bearing vibration acceleration signal of Case Western Reserve University is used as a data set to verify the proposed method, and the average accuracy of the fault diagnosis is 74.37%, the variance of the accuracy is 0.0001, and the average fault diagnosis accuracy of the unoptimized algorithm is 65.6%. The variance of the accuracy is 0.0019. Experimental results show that compared with the traditional convolutional neural network, the proposed method has a significant improvement in the accuracy of bearing fault diagnosis, and the method has better stability, less calculation time and better comprehensive performance.

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謝星怡,張正江,閆正兵,李欣燦,陶莫凡,章純.基于信號特征提取和卷積神經(jīng)網(wǎng)絡(luò )的軸承故障診斷研究計算機測量與控制[J].,2023,31(10):21-27.

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歷史
  • 收稿日期:2022-11-17
  • 最后修改日期:2022-12-15
  • 錄用日期:2023-01-03
  • 在線(xiàn)發(fā)布日期: 2023-10-26
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