齒輪箱復(fù)合故障診斷特征提取的若干方法研究
發(fā)布時(shí)間：2019-06-08 12:35
【摘要】：在機(jī)械設(shè)備中齒輪箱是最重要的動(dòng)力傳動(dòng)部件，其健康狀況直接影響著機(jī)械設(shè)備能否正常工作，若能準(zhǔn)確的預(yù)測(cè)故障的位置，就可以有效的避免故障所帶來的巨大人力和財(cái)力損失，因此研究新型復(fù)合故障診斷方法對(duì)齒輪箱的正常運(yùn)行具有舉足輕重的作用。通過振動(dòng)加速度傳感器所采集到的振動(dòng)信號(hào)通常是非平穩(wěn)信號(hào)，尤其是在工作現(xiàn)場(chǎng)采集到的信號(hào)更是受到各種背景噪聲的干擾，導(dǎo)致微弱故障特征經(jīng)常被噪聲所淹沒。此外，當(dāng)齒輪箱出現(xiàn)故障時(shí)，往往產(chǎn)生了位置不同、形式不同、程度不同的復(fù)合故障，每個(gè)故障之間相互干擾、相互影響、相互耦合。尤其是在強(qiáng)背景噪聲條件下，微弱故障還極易被噪聲淹沒，從而給故障診斷帶來了挑戰(zhàn)。因此對(duì)強(qiáng)背景噪聲下復(fù)合故障進(jìn)行診斷是當(dāng)今的技術(shù)難點(diǎn)。本論文針對(duì)以上問題，在國家自然基金（50775157）；山西省基礎(chǔ)研究項(xiàng)目(2012011012-1)；山西省高等學(xué)校留學(xué)回國人員科研資助項(xiàng)目(2011-12)的資助下，把齒輪箱作為研究對(duì)象，以近幾年比較新的降噪方法作為研究手段，同時(shí)以齒輪箱復(fù)合故障作為研究目標(biāo)，對(duì)強(qiáng)背景噪聲環(huán)境下，從復(fù)合故障振動(dòng)信號(hào)中準(zhǔn)確的提取故障特征信息，進(jìn)一步對(duì)故障特征進(jìn)行分離進(jìn)行了深入的研究。 論文主要研究結(jié)論如下： (1)用EEMD(Ensemble Empirical Mode Decomposition)對(duì)強(qiáng)噪聲的多調(diào)制源多載波頻率的仿真信號(hào)進(jìn)行分解，發(fā)現(xiàn)單一的白噪聲幅值直接影響著EEMD的分解效率。針對(duì)這個(gè)問題，論文提出了CMF(Combined ModeFunction),即將EMD(Empirical Mode Decomposition)分解得到的與原信號(hào)相關(guān)性較強(qiáng)的IMFs按高低頻進(jìn)行疊加，形成兩個(gè)新的組合模態(tài)函數(shù)ch和c L，然后通過添加不同的白噪聲幅值對(duì)c h和cL分別進(jìn)行EEMD分解，最后對(duì)敏感的IMFs分別進(jìn)行循環(huán)自相關(guān)函數(shù)解調(diào)分析，將提出的方法應(yīng)用于仿真信號(hào)和復(fù)合故障齒輪箱試驗(yàn)臺(tái)，成功提取了多故障特征，驗(yàn)證了此方法的有效性。 (2)針對(duì)強(qiáng)噪聲環(huán)境下滾動(dòng)軸承故障信號(hào)微弱、故障特征難以提取等問題，本文提出基于最小熵反褶積（Minimum entropy deconvolution，MED）和EEMD相結(jié)合的方法來提取復(fù)合故障中滾動(dòng)軸承微弱故障特征。通過對(duì)仿真信號(hào)分析發(fā)現(xiàn)：在強(qiáng)背景噪聲下EEMD對(duì)微弱信號(hào)的特征提取具有很大的局限性。為了剔除噪聲干擾，提取微弱故障的特征信息，本文選取MED作為EEMD的前置濾波器，驗(yàn)證了其強(qiáng)大的降噪功能。同時(shí)將MED與EEMD相結(jié)合的方法用于復(fù)合故障的微弱故障特征提取，即先用MED對(duì)強(qiáng)背景噪聲下風(fēng)電齒輪箱試驗(yàn)臺(tái)進(jìn)行降噪處理，然后再對(duì)降噪后的信號(hào)進(jìn)行EEMD，最后對(duì)敏感的本征模態(tài)函數(shù)(IMFs)進(jìn)行循環(huán)自相關(guān)函數(shù)解調(diào)分析。這種方法與EEMD進(jìn)行對(duì)比分析，表明了此方法有效性，從而為多故障共存并處于強(qiáng)背景噪聲下的微弱特征提取提供了一種新的方法。 (3)循環(huán)平穩(wěn)信號(hào)具有非平穩(wěn)性特點(diǎn)，因此用循環(huán)平穩(wěn)的特征來研究循環(huán)統(tǒng)計(jì)量是很有必要的。循環(huán)二階譜適用于周期性振動(dòng)信號(hào)，但通過仿真信號(hào)發(fā)現(xiàn)在強(qiáng)背景噪聲下，時(shí)域的離散化并沒有導(dǎo)致循環(huán)自相關(guān)函數(shù)在循環(huán)域內(nèi)周期化。此外，多載波頻率共存或比較接近時(shí)在高頻處不可避免的出現(xiàn)了混迭現(xiàn)象。 (4)研究了最大相關(guān)峭度反褶積（Maximum correlated Kurtosisdeconvolut on，MCKD）的降噪特點(diǎn)，同時(shí)對(duì)它的參數(shù)（位移數(shù)、周期和迭代次數(shù)）進(jìn)行了討論和分析。 (5)針對(duì)多調(diào)制源、多載波信號(hào)的循環(huán)自相關(guān)函數(shù)解調(diào)分析存在交叉項(xiàng)的干擾,這使循環(huán)自相關(guān)函數(shù)解調(diào)方法的實(shí)際應(yīng)用產(chǎn)生了局限性。本文提出了基于最大相關(guān)峭度反褶積（Maximum correlated Kurtosis deconvolution，MCKD）和循環(huán)自相關(guān)解調(diào)方法，先通過MCKD對(duì)原信號(hào)進(jìn)行降噪，，以便提取感興趣的周期成分，再對(duì)降噪后的周期信號(hào)通過循環(huán)自相關(guān)解調(diào)分析，有效地抑制了多調(diào)制源、多載波對(duì)循環(huán)平穩(wěn)結(jié)果帶來的交叉項(xiàng)干擾,提高了分析的可靠性。將該方法運(yùn)用于復(fù)合齒輪箱故障診斷中，成功地從振動(dòng)信號(hào)中分離出故障源。 針對(duì)旋轉(zhuǎn)機(jī)械在強(qiáng)噪聲背景下的復(fù)合故障診斷是當(dāng)前機(jī)械故障診斷領(lǐng)域的難點(diǎn)。本文以風(fēng)電齒輪箱為研究對(duì)象，對(duì)齒輪點(diǎn)蝕、軸承內(nèi)外圈點(diǎn)蝕等復(fù)合故障的振動(dòng)信號(hào)進(jìn)行分析。通過仿真信號(hào)和工程實(shí)例表明將EEMD、MED、MCKD、CMF、循環(huán)域解調(diào)等方法相結(jié)合，可以成功提取強(qiáng)背景噪聲下復(fù)合故障的特征頻率，實(shí)現(xiàn)了由單一故障到多故障的突破，應(yīng)用前景廣闊。
[Abstract]:in the mechanical equipment, the gear box is the most important power transmission component, the health condition of the gear box directly influences whether the mechanical equipment can work normally, and if the position of the fault is accurately predicted, the huge human and financial loss caused by the failure can be effectively avoided, Therefore, the new compound fault diagnosis method plays a very important role in the normal operation of the gear box. The vibration signal acquired by the vibration acceleration sensor is usually a non-stationary signal, especially the signal acquired at the work site is interfered by various background noise, and the weak fault characteristic is often inundated by the noise. In addition, when the gear box fails, a complex fault with different positions, different forms and different degrees is often generated, and each fault is mutually interfered, influenced and coupled with each other. In particular, in that condition of strong background noise, the weak fault is extremely easy to be inundated with noise, thus posing a challenge for fault diagnosis. Therefore, the diagnosis of composite fault under strong background noise is the difficult point of the present technology. In view of the above problems, in the national natural fund (50775157), the basic research project of Shanxi Province (2012011012-1), the research object of the gear box is taken as the research object under the support of the research and financing project (2011-12) of the research and support project of the university of higher learning in Shanxi Province (2011-12). In recent years, the new noise reduction method is used as the research means, and the compound fault of the gearbox is used as the research target, and the fault characteristic information is accurately extracted from the composite fault vibration signal under the strong background noise environment, and the fault characteristic is further separated. The main conclusions of the paper are as follows: (1) Using EEMD (EEMD) to decompose the multi-carrier frequency of the multi-modulation source with strong noise, it is found that the single white noise amplitude directly affects the division of the EEMD. In order to solve this problem, the paper presents a combined mode function (CMF), and the IMFs with strong correlation with the original signal obtained by the EMD (EMD) decomposition are combined at high and low frequencies to form two new combined modal functions. and finally, carrying out cyclic autocorrelation function demodulation analysis on the sensitive IMFs, The method is verified by the barrier feature The paper presents a method of combining minimum entropy deconvolution (MED) and EEMD to extract the micro-fault of the rolling bearing in the composite fault with the method of combination of minimum entropy deconvolution (MED) and EEMD. The analysis of the simulation signal shows that the extraction of the weak signal by the EEMD is very important in the strong background noise. In order to eliminate the noise interference and extract the characteristic information of weak fault, this paper selects MED as the pre-filter of EEMD, and verifies its power. The method for combining the MED and the EEMD is used for the weak fault feature extraction of the composite fault, namely, firstly, performing noise reduction processing on the wind power gearbox test bed under the strong background noise by the MED, and then EEMD for the last pair of sensitive intrinsic mode functions (IMFs) The method is compared with EEMD, which shows the effectiveness of this method, so as to provide a weak feature extraction for multi-fault co-existence and under strong background noise. (3) The circular stationary signal has the characteristics of non-stationarity, so the cycle statistics are studied with the characteristics of the cycle stability The cyclic second-order spectrum is suitable for periodic vibration signals, but it is found that the discretization of the time domain does not cause the cyclic autocorrelation function to be in the strong background noise by the simulation signal. in addition, that multi-carrier frequency is unavoidable at high frequency when the multi-carrier frequency coexist or is close (4) The noise reduction characteristics of the maximum correlation kurtosideconvolon (MCKD) are studied, and its parameters (the number of displacement, the period and the number of iterations) are also studied. (5) For multiple modulation sources, the cyclic autocorrelation function of the multi-carrier signal is used to demodulate and analyze the interference of the cross term, which makes the loop self-correlation function demodulation method In this paper, the maximum correlatedKurtosis (MCKD) and the cyclic autocorrelation demodulation method based on the maximum correlation are proposed in this paper. Firstly, the original signal is denoised by the MCKD, so as to extract the periodic component of interest and the periodic signal after noise reduction. Through the self-correlation demodulation analysis of the cycle, the cross terms of the multi-modulation source and the multi-carrier on the smooth result of the loop are effectively suppressed. The method is used in the fault diagnosis of the compound gear box, and successfully The fault source is separated from the vibration signal. The difficulties in the field of current mechanical fault diagnosis are as follows: the wind power gearbox is used as the research object, the pitting of the gears, the inner and outer ring points of the bearing, etc. The vibration signal of the composite fault is analyzed. By combining the simulation signal and the engineering example, the characteristic frequency of the composite fault under strong background noise can be successfully extracted by combining the methods of EEMD, MED, MCKD, CMF and cyclic domain demodulation.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TH165.3
本文編號(hào)：2495284
[Abstract]:in the mechanical equipment, the gear box is the most important power transmission component, the health condition of the gear box directly influences whether the mechanical equipment can work normally, and if the position of the fault is accurately predicted, the huge human and financial loss caused by the failure can be effectively avoided, Therefore, the new compound fault diagnosis method plays a very important role in the normal operation of the gear box. The vibration signal acquired by the vibration acceleration sensor is usually a non-stationary signal, especially the signal acquired at the work site is interfered by various background noise, and the weak fault characteristic is often inundated by the noise. In addition, when the gear box fails, a complex fault with different positions, different forms and different degrees is often generated, and each fault is mutually interfered, influenced and coupled with each other. In particular, in that condition of strong background noise, the weak fault is extremely easy to be inundated with noise, thus posing a challenge for fault diagnosis. Therefore, the diagnosis of composite fault under strong background noise is the difficult point of the present technology. In view of the above problems, in the national natural fund (50775157), the basic research project of Shanxi Province (2012011012-1), the research object of the gear box is taken as the research object under the support of the research and financing project (2011-12) of the research and support project of the university of higher learning in Shanxi Province (2011-12). In recent years, the new noise reduction method is used as the research means, and the compound fault of the gearbox is used as the research target, and the fault characteristic information is accurately extracted from the composite fault vibration signal under the strong background noise environment, and the fault characteristic is further separated. The main conclusions of the paper are as follows: (1) Using EEMD (EEMD) to decompose the multi-carrier frequency of the multi-modulation source with strong noise, it is found that the single white noise amplitude directly affects the division of the EEMD. In order to solve this problem, the paper presents a combined mode function (CMF), and the IMFs with strong correlation with the original signal obtained by the EMD (EMD) decomposition are combined at high and low frequencies to form two new combined modal functions. and finally, carrying out cyclic autocorrelation function demodulation analysis on the sensitive IMFs, The method is verified by the barrier feature The paper presents a method of combining minimum entropy deconvolution (MED) and EEMD to extract the micro-fault of the rolling bearing in the composite fault with the method of combination of minimum entropy deconvolution (MED) and EEMD. The analysis of the simulation signal shows that the extraction of the weak signal by the EEMD is very important in the strong background noise. In order to eliminate the noise interference and extract the characteristic information of weak fault, this paper selects MED as the pre-filter of EEMD, and verifies its power. The method for combining the MED and the EEMD is used for the weak fault feature extraction of the composite fault, namely, firstly, performing noise reduction processing on the wind power gearbox test bed under the strong background noise by the MED, and then EEMD for the last pair of sensitive intrinsic mode functions (IMFs) The method is compared with EEMD, which shows the effectiveness of this method, so as to provide a weak feature extraction for multi-fault co-existence and under strong background noise. (3) The circular stationary signal has the characteristics of non-stationarity, so the cycle statistics are studied with the characteristics of the cycle stability The cyclic second-order spectrum is suitable for periodic vibration signals, but it is found that the discretization of the time domain does not cause the cyclic autocorrelation function to be in the strong background noise by the simulation signal. in addition, that multi-carrier frequency is unavoidable at high frequency when the multi-carrier frequency coexist or is close (4) The noise reduction characteristics of the maximum correlation kurtosideconvolon (MCKD) are studied, and its parameters (the number of displacement, the period and the number of iterations) are also studied. (5) For multiple modulation sources, the cyclic autocorrelation function of the multi-carrier signal is used to demodulate and analyze the interference of the cross term, which makes the loop self-correlation function demodulation method In this paper, the maximum correlatedKurtosis (MCKD) and the cyclic autocorrelation demodulation method based on the maximum correlation are proposed in this paper. Firstly, the original signal is denoised by the MCKD, so as to extract the periodic component of interest and the periodic signal after noise reduction. Through the self-correlation demodulation analysis of the cycle, the cross terms of the multi-modulation source and the multi-carrier on the smooth result of the loop are effectively suppressed. The method is used in the fault diagnosis of the compound gear box, and successfully The fault source is separated from the vibration signal. The difficulties in the field of current mechanical fault diagnosis are as follows: the wind power gearbox is used as the research object, the pitting of the gears, the inner and outer ring points of the bearing, etc. The vibration signal of the composite fault is analyzed. By combining the simulation signal and the engineering example, the characteristic frequency of the composite fault under strong background noise can be successfully extracted by combining the methods of EEMD, MED, MCKD, CMF and cyclic domain demodulation.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TH165.3
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