本文選題：齒輪傳動(dòng)系統(tǒng) + 故障特征提取��； 參考：《華北電力大學(xué)》2013年博士論文

【摘要】：本文以齒輪傳動(dòng)系統(tǒng)故障振動(dòng)信號(hào)為研究對(duì)象,在分析振動(dòng)機(jī)理、故障模式基礎(chǔ)上,從高斯性、非高斯性角度劃分振動(dòng)信號(hào)的各種成分,然后利用振動(dòng)信號(hào)的雙譜幅值提取到非高斯性強(qiáng)度、雙譜熵這兩類(lèi)故障特征,最后討論了新高階統(tǒng)計(jì)量特征的優(yōu)化與實(shí)用問(wèn)題,主要研究成果如下： (1)通過(guò)研究齒輪傳動(dòng)系統(tǒng)主要零部件的失效模式、振動(dòng)機(jī)理、故障表現(xiàn),將振動(dòng)信號(hào)劃分為與故障部件有關(guān)的非高斯確定性成分、與故障部件無(wú)關(guān)的非高斯確定性成分、高斯隨機(jī)成分、對(duì)稱(chēng)非高斯隨機(jī)成分、非對(duì)稱(chēng)非高斯隨機(jī)成分。當(dāng)齒輪或軸承發(fā)生故障時(shí),其振動(dòng)信號(hào)中的確定性成分、隨機(jī)成分會(huì)產(chǎn)生明顯變化,于是信號(hào)的非高斯性也隨之改變,尤其是因故障而增強(qiáng)的邊帶成分,它會(huì)導(dǎo)致信號(hào)的非高斯性、二次非線性增強(qiáng),信號(hào)的雙譜分析結(jié)果對(duì)此十分敏感。另外,雙譜能抑制掉振動(dòng)信號(hào)中的高斯隨機(jī)成分、對(duì)稱(chēng)非高斯隨機(jī)成分,保留非對(duì)稱(chēng)非高斯隨機(jī)成分信息,這有利于降低噪聲、非故障隨機(jī)振動(dòng)的干擾。 (2)當(dāng)齒輪傳動(dòng)系統(tǒng)發(fā)生故障時(shí),振動(dòng)信號(hào)的非高斯成分在雙頻域內(nèi)的分布強(qiáng)度與形態(tài)均隨之改變�；陔p譜幅值信息,分別提取到量化描述振動(dòng)信號(hào)非高斯成分強(qiáng)弱變化的非高斯性強(qiáng)度特征值,以及量化描述振動(dòng)信號(hào)非高斯成分在雙頻域內(nèi)分布形態(tài)變化的雙譜熵特征值。按照雙頻域內(nèi)的不同定義區(qū)間,共提取到六種特征值,分別是基于主定義域區(qū)間的NGIPD、HB-PD,基于任意二維區(qū)間part的NGIpart、HB-part,和基于多分區(qū)的NGIregion(k)、HB-region(k)。這些新的高階統(tǒng)計(jì)量特征既保留了雙譜分析的優(yōu)點(diǎn),而且彌補(bǔ)了雙譜幅值切片等常規(guī)高階統(tǒng)計(jì)量特征值的不足。不同定義區(qū)間的非高斯性強(qiáng)度、雙譜熵特征值各有特點(diǎn)：NGIPD與HB-PD分別包含了雙譜幅值的全部強(qiáng)度、分布形態(tài)信息,不含雙譜對(duì)稱(chēng)冗余信息；當(dāng)二維定義域區(qū)間part內(nèi)包含有較豐富故障信息時(shí),NGIpart與HB-part表征故障的能力較好,但需要人為觀察并設(shè)定合適的雙頻域二維區(qū)間；振動(dòng)信號(hào)的故障信息在雙頻域內(nèi)分布不均,特征值NGIregion(k)、HB-region(k)在某些分區(qū)內(nèi)故障特征提取效果較好,某些分區(qū)效果較差,這正體現(xiàn)了雙頻域分區(qū)特征值對(duì)雙譜內(nèi)容、故障信息具有較強(qiáng)的“聚焦能力"。 (3)針對(duì)非高斯性強(qiáng)度、雙譜熵特征值存在的不足,進(jìn)行了特征壓縮與信號(hào)濾波這兩類(lèi)故障特征優(yōu)化。雙譜分區(qū)特征值會(huì)產(chǎn)生高維特征空間,給實(shí)際應(yīng)用造成麻煩,利用主分量分析、核函數(shù)主分量分析從線性及非線性角度進(jìn)行特征壓縮,可將絕大部分故障信息濃縮于低維主分量空間或低維核主分量空間,尤其在利用Fisher準(zhǔn)則進(jìn)行特征優(yōu)選預(yù)處理后,新的主分量、核主分量特征值具有較高的故障區(qū)分能力。由于齒輪嚙合頻率及其諧波成分通常能量較強(qiáng),且易受非故障因素影響,于是采用Gabor濾波與信號(hào)重構(gòu),在時(shí)頻域空間精準(zhǔn)濾除振動(dòng)信號(hào)的嚙合頻率及其諧波成分,結(jié)果表明,重構(gòu)信號(hào)的非高斯性強(qiáng)度特征對(duì)齒輪故障敏感程度提高,有利于后續(xù)故障診斷。另外,為研究新特征值在齒輪傳動(dòng)系統(tǒng)狀態(tài)監(jiān)測(cè)與故障報(bào)警中的使用方法與效果,以原始振動(dòng)信號(hào)的HB-PD、Gabor濾波重構(gòu)信號(hào)的NGIPD為例進(jìn)行故障特征趨勢(shì)分析,并根據(jù)“3σ準(zhǔn)則”設(shè)定故障閾值,不論報(bào)警時(shí)間還是診斷準(zhǔn)確率,都取得理想效果,為工程實(shí)際應(yīng)用提供了新思路與新方法。
[Abstract]:This paper takes the fault vibration signal of the gear transmission system as the research object. On the basis of the analysis of the vibration mechanism and the fault mode, the various components of the vibration signal are divided from the Gauss nature and the non Gauss angle. Then the two types of fault features of the non Gauss strength and the bispectrum entropy are extracted by the bispectral amplitude of the vibration signal. Finally, the new high order statistics are discussed. The main research results are as follows:
(1) by studying the failure mode, vibration mechanism and fault performance of the main parts of the gear transmission system, the vibration signals are divided into non Gauss deterministic components related to the fault parts, non Gauss deterministic components independent of the fault parts, Gauss random components, symmetric non Gauss random components and asymmetric non Gauss random components. When or when a bearing fails, the deterministic component in the vibration signal will change obviously, so the non Gauss character of the signal is also changed, especially the edge band component enhanced by the fault. It will lead to the non Gauss character of the signal, the two nonlinear enhancement, and the bispectrum analysis of the signal is very sensitive. In addition, bispectrum The Gauss random component in the vibration signal can be suppressed, the asymmetric non Gauss random component is symmetric, and the asymmetric non Gauss random component information is retained, which helps to reduce the noise and the interference of the non fault random vibration.
(2) when the gear transmission system fails, the intensity and shape of the non Gauss component of the vibration signal are changed in the dual frequency domain. Based on the bispectrum amplitude information, the non Gauss intensity characteristic values are extracted to quantify the non Gauss intensity changes of the vibration signals, and the quantized description of the non Gauss components of the vibration signals in two pairs is quantized. The bipectral entropy eigenvalues of the morphological changes in the frequency domain. According to the different definition intervals within the dual frequency domain, six eigenvalues are extracted, which are based on the NGIPD, HB-PD, NGIpart, HB-part, and HB-region (k) based on arbitrary two-dimensional interval part, respectively. These new higher-order statistics features are guaranteed. The advantages of bispectral analysis are retained, and the inadequacy of the eigenvalues of conventional high-order statistics, such as bispectral amplitude slices, is made up. The characteristics of the non Gauss entropy of different defined intervals have their own characteristics: NGIPD and HB-PD contain all the intensity of the bispectral amplitude, the distribution form of information, and no bispectral redundancy information; When the packet part contains a lot of fault information, the ability of NGIpart and HB-part to characterize the fault is better, but it is necessary to observe and set suitable two frequency domain. The fault information of the vibration signal is distributed unevenly in the dual frequency domain, the eigenvalue NGIregion (k), and HB-region (k) is better in extracting the fault features in some partitions. The effect of some partitions is poor, which reflects the dual frequency domain partition eigenvalue has strong "focusing ability" for bispectrum content and fault information.
(3) in view of the inadequacy of the non Gauss intensity and the existence of the eigenvalues of the bispectrum entropy, the feature compression and signal filtering are optimized for the two types of fault features. The eigenvalues of the bispectrum partition can produce high dimensional feature space and cause trouble to the practical application. By using the principal component analysis, the kernel function principal component analysis is compressed from the linear and nonlinear angles. Most of the fault information is concentrated in the low dimensional principal component space or the low dimensional kernel principal component space. The new principal component and the kernel principal component eigenvalue have high fault differentiation ability, especially when the Fisher criterion is used to perform the feature optimization preprocessing. As a result, Gabor filtering and signal reconstruction are used to accurately filter the meshing frequency and harmonic components of the vibration signal in the time and frequency space. The results show that the non Gauss strength characteristics of the reconstructed signal can improve the sensitivity of the gear fault and be beneficial to the follow-up fault diagnosis. In addition, the new eigenvalue is studied for the state monitoring of the gear transmission system. With the method and effect of using the fault alarm, the fault feature trend is analyzed with the NGIPD of the HB-PD and Gabor filtering reconstruction signal of the original vibration signal, and the fault threshold is set according to the "3 Sigma" criterion. No matter the alarm time or the diagnostic accuracy, the ideal effect is obtained. The new idea and new method are provided for the practical application of the project.

【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TH132.41;TH165.3

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