基于小波—包絡(luò)的重載貨運列車滾動軸承振動故障診斷
發(fā)布時間：2018-09-14 09:17
【摘要】：滾動軸承作為重載貨運列車的重要部件,其工作狀態(tài)直接影響到貨車列車的提速和安全運行。滾動軸承發(fā)生故障時,一般為磨損故障和損傷類故障。磨損故障是由于軸承磨損造成零件間隙逐漸變大,振動加強。這是一類漸變性的故障,振動波形變化缺乏規(guī)律,具有較強的隨機性,但通頻帶的振動幅值變化量往往能清晰地反映磨損的嚴重程度。損傷類故障是指當軸承元件滾過表面損傷點時,就會產(chǎn)生一個突變的沖擊脈沖力,引起軸承和機械設(shè)備的共振,這是損傷類故障的基本特征。磨損類故障一般是一個比較長的發(fā)展過程,可以采取定期的軸承振動總量監(jiān)測,進行趨勢分析作狀態(tài)預報。而損傷類故障是一種具有較強突發(fā)性而又比較危險,早期故障癥狀又比較難識別的一類故障,并且是進行故障診斷時重點研究方向。事實上,滾動軸承產(chǎn)生故障時,由于軸承元件受到剛度非線性、接觸摩擦、零件間隙和外載荷等的影響,其振動信號往往會表現(xiàn)為非平穩(wěn)特征,因此,如何從非平穩(wěn)振動信號中提取滾動軸承故障特征信息,是滾動軸承的故障診斷中的重中之重。 針對滾動軸承故障振動信號的非平穩(wěn)特性,結(jié)合小波變換、Hilbert變換、包絡(luò)譜細化分析的優(yōu)點,提出先將壓電式加速度傳感器采集的軸承振動信號進行db10小波濾波重構(gòu),對濾波重構(gòu)信號進行Hilbert變換得到包絡(luò)信號,最后對包絡(luò)譜細化得到振動幅值譜圖,根據(jù)細化后的幅值譜圖判別軸承故障的小波-包絡(luò)分析方法,并通過仿真和實驗分析滾動軸承的故障,驗證理論分析的正確性。所提出的基于小波-包絡(luò)分析的重載貨運列車滾動軸承振動故障診斷方法同傳統(tǒng)的時頻分析、小波變換、共振解調(diào)方法相比有所突破,診斷的速度和準確度都有所提高,對國內(nèi)重載貨運列車滾動軸承故障診斷的發(fā)展有一定的意義。 經(jīng)過在滾動軸承振動實驗臺采集的實驗軸承的振動信號數(shù)據(jù)的分析和處理,能從具有強干擾和噪聲的軸承振動加速度信號中提取故障特征,實現(xiàn)了滾動軸承故障診斷。
[Abstract]:As an important part of heavy-haul freight train, rolling bearing has a direct impact on the speed and safety of the train. Rolling bearing failure, generally for wear and damage type of fault. Wear failure is caused by bearing wear caused by the gradual increase in clearance, vibration strengthening. This is a kind of gradually changing fault. The variation of vibration waveform is lack of regularity and has strong randomness. However, the variation of vibration amplitude in passband can clearly reflect the serious degree of wear. The damage type fault refers to when the bearing element rolls over the surface damage point, will produce a sudden impact pulse force, causes the bearing and the mechanical equipment resonance, this is the damage class fault basic characteristic. Generally, the wear type fault is a relatively long developing process, which can be monitored regularly by the total vibration of bearing, and the trend analysis can be used to forecast the state of the bearing. The damage type fault is a kind of fault which has strong sudden and dangerous, and the early fault symptom is difficult to identify, and it is the research direction of fault diagnosis. As a matter of fact, when rolling bearings fail, the vibration signals are often non-stationary because the bearing elements are affected by stiffness nonlinearity, contact friction, part clearance and external load, etc. How to extract fault feature information of rolling bearing from non-stationary vibration signal is the most important in fault diagnosis of rolling bearing. Aiming at the non-stationary characteristic of rolling bearing fault vibration signal, combining the advantages of wavelet transform Hilbert transform and envelope spectrum thinning analysis, the db10 wavelet filter reconstruction of bearing vibration signal collected by piezoelectric acceleration sensor is put forward. The envelope signal is obtained by the Hilbert transform of the filtered reconstructed signal. Finally, the amplitude spectrum of vibration is obtained by thinning the envelope spectrum, and the wavelet envelope analysis method is used to distinguish the fault of bearing according to the refined amplitude spectrum. The fault of rolling bearing is analyzed by simulation and experiment to verify the correctness of theoretical analysis. Compared with the traditional time-frequency analysis, wavelet transform and resonance demodulation, the method of vibration fault diagnosis of rolling bearing of heavy-haul freight train based on wavelet envelope analysis has a breakthrough, and the speed and accuracy of diagnosis are improved. It has certain significance for the development of rolling bearing fault diagnosis of heavy haul freight train in China. Through the analysis and processing of the vibration signal data collected from the rolling bearing vibration test rig, the fault characteristics of the bearing vibration acceleration signal with strong interference and noise can be extracted, and the fault diagnosis of the rolling bearing can be realized.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3
本文編號：2242266
[Abstract]:As an important part of heavy-haul freight train, rolling bearing has a direct impact on the speed and safety of the train. Rolling bearing failure, generally for wear and damage type of fault. Wear failure is caused by bearing wear caused by the gradual increase in clearance, vibration strengthening. This is a kind of gradually changing fault. The variation of vibration waveform is lack of regularity and has strong randomness. However, the variation of vibration amplitude in passband can clearly reflect the serious degree of wear. The damage type fault refers to when the bearing element rolls over the surface damage point, will produce a sudden impact pulse force, causes the bearing and the mechanical equipment resonance, this is the damage class fault basic characteristic. Generally, the wear type fault is a relatively long developing process, which can be monitored regularly by the total vibration of bearing, and the trend analysis can be used to forecast the state of the bearing. The damage type fault is a kind of fault which has strong sudden and dangerous, and the early fault symptom is difficult to identify, and it is the research direction of fault diagnosis. As a matter of fact, when rolling bearings fail, the vibration signals are often non-stationary because the bearing elements are affected by stiffness nonlinearity, contact friction, part clearance and external load, etc. How to extract fault feature information of rolling bearing from non-stationary vibration signal is the most important in fault diagnosis of rolling bearing. Aiming at the non-stationary characteristic of rolling bearing fault vibration signal, combining the advantages of wavelet transform Hilbert transform and envelope spectrum thinning analysis, the db10 wavelet filter reconstruction of bearing vibration signal collected by piezoelectric acceleration sensor is put forward. The envelope signal is obtained by the Hilbert transform of the filtered reconstructed signal. Finally, the amplitude spectrum of vibration is obtained by thinning the envelope spectrum, and the wavelet envelope analysis method is used to distinguish the fault of bearing according to the refined amplitude spectrum. The fault of rolling bearing is analyzed by simulation and experiment to verify the correctness of theoretical analysis. Compared with the traditional time-frequency analysis, wavelet transform and resonance demodulation, the method of vibration fault diagnosis of rolling bearing of heavy-haul freight train based on wavelet envelope analysis has a breakthrough, and the speed and accuracy of diagnosis are improved. It has certain significance for the development of rolling bearing fault diagnosis of heavy haul freight train in China. Through the analysis and processing of the vibration signal data collected from the rolling bearing vibration test rig, the fault characteristics of the bearing vibration acceleration signal with strong interference and noise can be extracted, and the fault diagnosis of the rolling bearing can be realized.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3
【引證文獻】
相關(guān)碩士學位論文 前2條
1 陳京;便攜式設(shè)備故障監(jiān)測診斷系統(tǒng)關(guān)鍵技術(shù)研究[D];北京化工大學;2012年
2 孫鵬沖;基于諧波小波和加速度包絡(luò)的城軌列車軸承故障診斷研究[D];北京交通大學;2012年
，本文編號：2242266
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