旋轉(zhuǎn)機(jī)械故障診斷與預(yù)測(cè)方法及其應(yīng)用研究
發(fā)布時(shí)間：2019-06-14 03:50
【摘要】：研究旋轉(zhuǎn)機(jī)械的故障診斷與預(yù)測(cè)技術(shù),對(duì)于保障機(jī)械設(shè)備運(yùn)行的安全性和穩(wěn)定性具有十分重要的意義。旋轉(zhuǎn)機(jī)械的振動(dòng)信號(hào)具有非穩(wěn)定性和非線性,同時(shí),在強(qiáng)背景噪聲工作環(huán)境下,旋轉(zhuǎn)機(jī)械的微弱故障特征很容易被噪聲淹沒(méi),此外,當(dāng)機(jī)械系統(tǒng)出現(xiàn)故障時(shí),往往會(huì)產(chǎn)生位置不同的復(fù)合故障,故障之間相互耦合,從而給旋轉(zhuǎn)機(jī)械故障精確診斷帶來(lái)了挑戰(zhàn),因此,強(qiáng)噪下微弱、復(fù)合故障診斷是當(dāng)今機(jī)械故障診斷領(lǐng)域的難點(diǎn)。論文將旋轉(zhuǎn)機(jī)械作為研究對(duì)象,研究形態(tài)學(xué)濾波、局域均值分解、多元經(jīng)驗(yàn)?zāi)B(tài)分解和噪聲輔助多元經(jīng)驗(yàn)?zāi)B(tài)分解等時(shí)頻方法及其在旋轉(zhuǎn)機(jī)械的微弱、復(fù)合故障診斷中的應(yīng)用,為機(jī)械故障診斷、性能退化狀態(tài)識(shí)別和趨勢(shì)預(yù)測(cè)提供新的有效手段。主要內(nèi)容如下：1、提出了一種基于LMD和形態(tài)濾波的軸承故障診斷方法。設(shè)計(jì)并搭建了鐵路貨車輪對(duì)滾動(dòng)軸承測(cè)試系統(tǒng),并對(duì)軸承典型故障振動(dòng)信號(hào)進(jìn)行分析,仿真實(shí)驗(yàn)與軸承故障試驗(yàn)結(jié)果驗(yàn)證了該方法的有效性。針對(duì)形態(tài)濾波器尺度選擇缺乏自適應(yīng)的問(wèn)題,提出了基于遺傳算法的自適應(yīng)形態(tài)濾波方法,仿真和試驗(yàn)的分析結(jié)果表明,自適應(yīng)形態(tài)學(xué)濾波器對(duì)于信號(hào)降噪處理和沖擊特征提取兩方面均有明顯的效果。2、針對(duì)EMD和LMD等時(shí)頻分析方法無(wú)法處理旋轉(zhuǎn)機(jī)械多通道振動(dòng)信號(hào)的缺點(diǎn)和旋轉(zhuǎn)機(jī)械早期微弱故障、復(fù)合故障的特征提取問(wèn)題,提出了基于改進(jìn)的多元經(jīng)驗(yàn)?zāi)B(tài)分解的旋轉(zhuǎn)機(jī)械早期故障診斷方法。該方法利用多元經(jīng)驗(yàn)?zāi)B(tài)分解將多通道振動(dòng)信號(hào)分解得到一系列多元IMF分量,將峭度準(zhǔn)則和互信息引入IMF的選取,進(jìn)一步消除混入的噪聲和偽分量的影響。仿真信號(hào)和旋轉(zhuǎn)機(jī)械故障信號(hào)的分析結(jié)果表明,改進(jìn)的MEMD方法在多通道信號(hào)分解的精確性和魯棒性等方面具有明顯的優(yōu)越性和有效性,為旋轉(zhuǎn)機(jī)械微弱故障、復(fù)合故障診斷和多通道振動(dòng)信息融合分析提供了新的思路和手段。3、NAMEMD是一種新的非線性信號(hào)自適應(yīng)時(shí)頻分解方法,該方法克服了MEMD和EEMD的模態(tài)混疊等問(wèn)題,但是經(jīng)過(guò)研究發(fā)現(xiàn),NAMEMD方法并不能完全抑制MEMD的模態(tài)混疊現(xiàn)象,得到的IMF仍存在模態(tài)混疊,需要后續(xù)處理。為了抑制NAMEMD方法分解中的模態(tài)混疊現(xiàn)象,提出了改進(jìn)的NAMEMD方法。采用基于排列熵的隨機(jī)性檢測(cè)技術(shù)及時(shí)地檢測(cè)異常信號(hào)和噪聲信號(hào),再對(duì)剩余信號(hào)進(jìn)行NAMEMD分解,通過(guò)仿真信號(hào)驗(yàn)證了所提出方法的有效性,在此基礎(chǔ)上,針對(duì)強(qiáng)噪下機(jī)械故障特征提取的問(wèn)題,提出了基于改進(jìn)的NAMEMD形態(tài)學(xué)與Teager能量算子解調(diào)的旋轉(zhuǎn)機(jī)械故障診斷方法,并通過(guò)仿真信號(hào)和旋轉(zhuǎn)機(jī)械故障信號(hào)將所提出的方法與EEMD和NAMEMD進(jìn)行了對(duì)比,結(jié)果表明改進(jìn)的NAMEMD方法消除了EEMD集成平均過(guò)程中因添加白噪聲的時(shí)頻特性差異帶來(lái)的模態(tài)混疊,分解結(jié)果相對(duì)于EEMD具有較準(zhǔn)確的IMF頻譜分布和更好的降噪效果,分解結(jié)果更為精確。所提方法在抑制模態(tài)混疊、增強(qiáng)降噪效果和提高分解精確性上要優(yōu)于EEMD和NAMEMD方法,結(jié)果驗(yàn)證了所提方法的有效性和優(yōu)越性。4、在分析樣本熵和排列熵原理的基礎(chǔ)上,針對(duì)軸承振動(dòng)信號(hào)的非線性特征,提出了基于NAMEMD和排列熵的軸承故障智能診斷方法。首先對(duì)振動(dòng)信號(hào)進(jìn)行NAMEMD分解,然后對(duì)前5個(gè)有意義的IMF分量進(jìn)行排列熵計(jì)算,并將其作為特征向量輸入訓(xùn)練好的SVM分類器,有效地實(shí)現(xiàn)軸承四種典型狀態(tài)類型的識(shí)別,準(zhǔn)確率高。5、將NAMEMD自適應(yīng)分解與基于非線性動(dòng)力學(xué)參數(shù)的信號(hào)復(fù)雜性的排列熵理論相結(jié)合,提出了基于改進(jìn)NAMEMD和排列熵的旋轉(zhuǎn)機(jī)械退化狀態(tài)檢測(cè)方法,該方法首先將多分量的振動(dòng)信號(hào)自適應(yīng)地分解得到一系列信噪比較高的IMF分量,利用對(duì)突變信號(hào)敏感的排列熵算法分別對(duì)各IMF進(jìn)行排列熵分析,進(jìn)行軸承運(yùn)行狀態(tài)及演化過(guò)程的準(zhǔn)確識(shí)別。建立了滾動(dòng)軸承振動(dòng)信號(hào)和退化狀態(tài)之間的聯(lián)系。通過(guò)仿真試驗(yàn)和滾動(dòng)軸承全壽命試驗(yàn)數(shù)據(jù),證明了建立的狀態(tài)指標(biāo)能夠準(zhǔn)確、完整地反映滾動(dòng)軸承的退化狀態(tài)趨勢(shì),實(shí)現(xiàn)了滾動(dòng)軸承全壽命周期狀態(tài)的有效識(shí)別。所提方法具有較強(qiáng)的魯棒性,為機(jī)械設(shè)備的性能退化狀態(tài)檢測(cè)提供了一種新的有效途徑。6、針對(duì)滾動(dòng)軸承退化狀態(tài)趨勢(shì)預(yù)測(cè)問(wèn)題,提出了基于NAMEMD、PE和SVR的滾動(dòng)軸承故障演化狀態(tài)趨勢(shì)預(yù)測(cè)模型,實(shí)現(xiàn)滾動(dòng)軸承性能退化趨勢(shì)的準(zhǔn)確預(yù)測(cè),評(píng)估在未來(lái)一段時(shí)間內(nèi)的軸承狀態(tài)的變化趨勢(shì),從而達(dá)到加強(qiáng)機(jī)械設(shè)備運(yùn)行安全性與穩(wěn)定性的目的。通過(guò)軸承全壽命試驗(yàn),證明了所提方法的準(zhǔn)確性和有效性,具有較高的預(yù)測(cè)精度和魯棒性,對(duì)工程實(shí)踐具有重要的指導(dǎo)意義。
[Abstract]:The research of the fault diagnosis and prediction of the rotating machinery is of great significance to the safety and stability of the operation of the mechanical equipment. the vibration signal of the rotating machine has the non-stability and the non-linearity, and at the same time, under the working environment of strong background noise, the weak fault characteristic of the rotating machine is easy to be flooded by noise, The mutual coupling between faults brings the challenge to the accurate diagnosis of the rotary mechanical failure. Therefore, the weak and complex fault diagnosis in the field of mechanical fault diagnosis is a difficult problem in the field of mechanical fault diagnosis. As a research object, the paper studies the time-frequency methods such as morphological filtering, local mean decomposition, multi-element empirical mode decomposition and noise-assisted multi-element empirical mode decomposition, and its application in the weak and complex fault diagnosis of rotating machinery, and it is a mechanical fault diagnosis. Performance degradation state identification and trend prediction provide new and effective means. The main content is as follows:1. A method of bearing fault diagnosis based on LMD and morphological filtering is presented. The rolling bearing test system of the railway wagon wheel is designed and constructed, and the typical fault vibration signal of the bearing is analyzed, and the simulation experiment and the bearing failure test result verify the effectiveness of the method. A self-adaptive morphological filtering method based on genetic algorithm is proposed for morphological filter scale selection. The results of simulation and experiment show that the adaptive morphological filter has obvious effect on signal noise reduction and impact feature extraction. In order to solve the shortcomings of the multi-channel vibration signal of the rotating machinery and the weak fault of the rotating machinery in the time-frequency analysis method such as the EMD and the LMD, the problem of the feature extraction of the composite fault is solved, and the early fault diagnosis method of the rotating machinery based on the improved multi-element empirical mode decomposition is proposed. According to the method, the multi-channel vibration signal is decomposed to obtain a series of multi-element IMF components by using the multi-element empirical mode decomposition, and the similarity criterion and the mutual information are introduced into the selection of the IMF, and the influence of the mixed noise and the pseudo component is further eliminated. The result of the analysis of the simulation signal and the rotating mechanical failure signal shows that the improved MEMD method has obvious advantages and effectiveness in the aspects of the accuracy and the robustness of the multi-channel signal decomposition, and is a weak fault of the rotating machinery, The composite fault diagnosis and the multi-channel vibration information fusion analysis provide a new idea and means.3. The NEMEMD is a new method of self-adaptive time-frequency decomposition of nonlinear signals, which overcomes the problems of the mode aliasing of the EMD and the EEMD, but has been found by the research, The NEMEMD method can not completely suppress the mode aliasing of the MEMD, and the obtained IMF still has the mode aliasing, and the subsequent processing is required. In order to suppress the mode aliasing in the decomposition of the NAMEMD method, an improved NEMEMD method is proposed. By adopting the random detection technology based on the arrangement entropy, the abnormal signal and the noise signal are detected in time, the residual signal is subjected to NAMOEMD decomposition, the validity of the proposed method is verified through the simulation signal, on the basis, the problem of the feature extraction of the mechanical failure under the strong noise is solved, A rotary mechanical fault diagnosis method based on improved NEMEMD morphology and Teager energy operator demodulation is proposed, and the proposed method is compared with the EEMD and the NAEMEMD by means of the simulation signal and the rotating mechanical failure signal. The results show that the improved NEMEMD method eliminates the mode aliasing caused by the difference of the time-frequency characteristics of the addition of white noise in the EEMD integration averaging process, and the decomposition result has a more accurate IMF spectral distribution and better noise reduction effect with respect to the EEMD, and the decomposition result is more accurate. The proposed method is superior to the EEMD and NEMEMD method in suppressing the mode aliasing, enhancing the noise reduction effect and improving the decomposition accuracy, and the validity and the superiority of the proposed method are verified. An intelligent diagnosis method for bearing failure based on NAEMEMD and permutation entropy is presented. The method comprises the following steps of: firstly, performing NAMOEMD decomposition on a vibration signal, and then arranging and entropy calculating the first five meaningful IMF components, and using the SVM classifier as a feature vector to input a trained SVM classifier, so that the identification of four typical state types of the bearing is effectively realized, and the accuracy is high. Based on the combination of the adaptive decomposition of the NAEMEMD and the arrangement entropy theory of the signal complexity based on the nonlinear dynamic parameters, a method for detecting the degradation state of the rotating machinery based on the improved NEMEMD and the arrangement entropy is proposed. The method comprises the following steps of: firstly, adaptively decomposing a multi-component vibration signal to obtain a series of IMF components with higher signal-to-noise ratio, and arranging and entropy analyzing the IMF according to an arrangement entropy algorithm which is sensitive to the abrupt signal, and carrying out accurate identification of the running state and the evolution process of the bearing. The relationship between the vibration signal and the degraded state of the rolling bearing is established. Through the simulation test and the whole life test data of the rolling bearing, it is proved that the established state index can accurately and completely reflect the degradation state tendency of the rolling bearing and realize the effective identification of the whole life cycle state of the rolling bearing. The proposed method has strong robustness, and provides a new effective method for the performance degradation state detection of mechanical equipment.6. Aiming at the problem of the trend prediction of the degradation state of the rolling bearing, the fault evolution state trend prediction model of the rolling bearing based on the NEMEMD, PE and SVR is put forward. The invention realizes the accurate prediction of the performance degradation trend of the rolling bearing, and evaluates the change tendency of the bearing state over a period of time, so as to achieve the purpose of strengthening the operation safety and the stability of the mechanical equipment. Through the full life test of the bearing, the accuracy and the effectiveness of the proposed method are proved, and the method has higher prediction accuracy and robustness, and has important guiding significance for engineering practice.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TH17
本文編號(hào)：2499074
[Abstract]:The research of the fault diagnosis and prediction of the rotating machinery is of great significance to the safety and stability of the operation of the mechanical equipment. the vibration signal of the rotating machine has the non-stability and the non-linearity, and at the same time, under the working environment of strong background noise, the weak fault characteristic of the rotating machine is easy to be flooded by noise, The mutual coupling between faults brings the challenge to the accurate diagnosis of the rotary mechanical failure. Therefore, the weak and complex fault diagnosis in the field of mechanical fault diagnosis is a difficult problem in the field of mechanical fault diagnosis. As a research object, the paper studies the time-frequency methods such as morphological filtering, local mean decomposition, multi-element empirical mode decomposition and noise-assisted multi-element empirical mode decomposition, and its application in the weak and complex fault diagnosis of rotating machinery, and it is a mechanical fault diagnosis. Performance degradation state identification and trend prediction provide new and effective means. The main content is as follows:1. A method of bearing fault diagnosis based on LMD and morphological filtering is presented. The rolling bearing test system of the railway wagon wheel is designed and constructed, and the typical fault vibration signal of the bearing is analyzed, and the simulation experiment and the bearing failure test result verify the effectiveness of the method. A self-adaptive morphological filtering method based on genetic algorithm is proposed for morphological filter scale selection. The results of simulation and experiment show that the adaptive morphological filter has obvious effect on signal noise reduction and impact feature extraction. In order to solve the shortcomings of the multi-channel vibration signal of the rotating machinery and the weak fault of the rotating machinery in the time-frequency analysis method such as the EMD and the LMD, the problem of the feature extraction of the composite fault is solved, and the early fault diagnosis method of the rotating machinery based on the improved multi-element empirical mode decomposition is proposed. According to the method, the multi-channel vibration signal is decomposed to obtain a series of multi-element IMF components by using the multi-element empirical mode decomposition, and the similarity criterion and the mutual information are introduced into the selection of the IMF, and the influence of the mixed noise and the pseudo component is further eliminated. The result of the analysis of the simulation signal and the rotating mechanical failure signal shows that the improved MEMD method has obvious advantages and effectiveness in the aspects of the accuracy and the robustness of the multi-channel signal decomposition, and is a weak fault of the rotating machinery, The composite fault diagnosis and the multi-channel vibration information fusion analysis provide a new idea and means.3. The NEMEMD is a new method of self-adaptive time-frequency decomposition of nonlinear signals, which overcomes the problems of the mode aliasing of the EMD and the EEMD, but has been found by the research, The NEMEMD method can not completely suppress the mode aliasing of the MEMD, and the obtained IMF still has the mode aliasing, and the subsequent processing is required. In order to suppress the mode aliasing in the decomposition of the NAMEMD method, an improved NEMEMD method is proposed. By adopting the random detection technology based on the arrangement entropy, the abnormal signal and the noise signal are detected in time, the residual signal is subjected to NAMOEMD decomposition, the validity of the proposed method is verified through the simulation signal, on the basis, the problem of the feature extraction of the mechanical failure under the strong noise is solved, A rotary mechanical fault diagnosis method based on improved NEMEMD morphology and Teager energy operator demodulation is proposed, and the proposed method is compared with the EEMD and the NAEMEMD by means of the simulation signal and the rotating mechanical failure signal. The results show that the improved NEMEMD method eliminates the mode aliasing caused by the difference of the time-frequency characteristics of the addition of white noise in the EEMD integration averaging process, and the decomposition result has a more accurate IMF spectral distribution and better noise reduction effect with respect to the EEMD, and the decomposition result is more accurate. The proposed method is superior to the EEMD and NEMEMD method in suppressing the mode aliasing, enhancing the noise reduction effect and improving the decomposition accuracy, and the validity and the superiority of the proposed method are verified. An intelligent diagnosis method for bearing failure based on NAEMEMD and permutation entropy is presented. The method comprises the following steps of: firstly, performing NAMOEMD decomposition on a vibration signal, and then arranging and entropy calculating the first five meaningful IMF components, and using the SVM classifier as a feature vector to input a trained SVM classifier, so that the identification of four typical state types of the bearing is effectively realized, and the accuracy is high. Based on the combination of the adaptive decomposition of the NAEMEMD and the arrangement entropy theory of the signal complexity based on the nonlinear dynamic parameters, a method for detecting the degradation state of the rotating machinery based on the improved NEMEMD and the arrangement entropy is proposed. The method comprises the following steps of: firstly, adaptively decomposing a multi-component vibration signal to obtain a series of IMF components with higher signal-to-noise ratio, and arranging and entropy analyzing the IMF according to an arrangement entropy algorithm which is sensitive to the abrupt signal, and carrying out accurate identification of the running state and the evolution process of the bearing. The relationship between the vibration signal and the degraded state of the rolling bearing is established. Through the simulation test and the whole life test data of the rolling bearing, it is proved that the established state index can accurately and completely reflect the degradation state tendency of the rolling bearing and realize the effective identification of the whole life cycle state of the rolling bearing. The proposed method has strong robustness, and provides a new effective method for the performance degradation state detection of mechanical equipment.6. Aiming at the problem of the trend prediction of the degradation state of the rolling bearing, the fault evolution state trend prediction model of the rolling bearing based on the NEMEMD, PE and SVR is put forward. The invention realizes the accurate prediction of the performance degradation trend of the rolling bearing, and evaluates the change tendency of the bearing state over a period of time, so as to achieve the purpose of strengthening the operation safety and the stability of the mechanical equipment. Through the full life test of the bearing, the accuracy and the effectiveness of the proposed method are proved, and the method has higher prediction accuracy and robustness, and has important guiding significance for engineering practice.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TH17
【引證文獻(xiàn)】
相關(guān)博士學(xué)位論文 前2條
1 王寶祥;基于運(yùn)動(dòng)形態(tài)分解與多變量EMD的高速自動(dòng)機(jī)動(dòng)態(tài)監(jiān)測(cè)與故障診斷研究[D];中北大學(xué);2018年
2 彭延峰;自適應(yīng)最稀疏時(shí)頻方法及其在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用[D];湖南大學(xué);2017年
相關(guān)碩士學(xué)位論文 前10條
1 馬江飛;基于FPGA的齒輪箱故障診斷方法研究[D];西安理工大學(xué);2018年
2 顧婉瑩;基于激光自混合干涉的速度測(cè)量及其應(yīng)用[D];東北石油大學(xué);2018年
3 劉小勇;基于深度學(xué)習(xí)的機(jī)械設(shè)備退化狀態(tài)建模及剩余壽命預(yù)測(cè)研究[D];哈爾濱工業(yè)大學(xué);2018年
4 于小娟;基于EEMD的谷物測(cè)產(chǎn)信號(hào)去噪處理方法研究[D];東北農(nóng)業(yè)大學(xué);2018年
5 葉緒丹;基于變分模態(tài)分解的滾動(dòng)軸承早期微弱故障診斷研究[D];安徽工業(yè)大學(xué);2018年
6 陳博;高速列車車輪多邊形的檢測(cè)與識(shí)別方法研究[D];西南交通大學(xué);2018年
7 卓仁雄;基于CEEMDAN和GWO-SVM的電機(jī)滾動(dòng)軸承故障診斷[D];南華大學(xué);2018年
8 薩仁朝格圖;智能裝備機(jī)械故障物聯(lián)網(wǎng)監(jiān)測(cè)診斷服務(wù)平臺(tái)[D];大連理工大學(xué);2018年
9 曾柯;基于SPSO優(yōu)化TWSVM及Bayesian更新指數(shù)模型的軸承剩余壽命預(yù)測(cè)[D];重慶大學(xué);2018年
10 徐國(guó)權(quán);基于多特征的機(jī)車軸承振動(dòng)故障診斷[D];北京信息科技大學(xué);2018年
，本文編號(hào)：2499074
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