非平穩(wěn)工況下滾動軸承局部故障診斷方法研究
發(fā)布時間：2019-05-25 04:39
【摘要】：旋轉(zhuǎn)機(jī)械廣泛應(yīng)用于工業(yè)生產(chǎn)中的重要設(shè)備,滾動軸承作為旋轉(zhuǎn)機(jī)械的重要組成部件之一,其運(yùn)轉(zhuǎn)是否穩(wěn)定與整個機(jī)械系統(tǒng)的安全運(yùn)行有著密切的聯(lián)系。然而,滾動軸承在其運(yùn)行過程中往往會產(chǎn)生疲勞剝落等故障。故障早期信號比較微弱,通常淹沒在背景噪聲之中。同時,設(shè)備由于升降速等非平穩(wěn)工況而引起的轉(zhuǎn)速波動造成頻率估計不準(zhǔn),這些問題嚴(yán)重制約著軸承故障的精準(zhǔn)診斷。滾動軸承振源信號的分離方法和非平穩(wěn)信號瞬時頻率估計方法的研究,將有助于準(zhǔn)確提取故障特征,進(jìn)而實現(xiàn)對軸承故障準(zhǔn)確的診斷,這對旋轉(zhuǎn)機(jī)械保持良好的工作狀態(tài)具有重要的意義。本文主要研究的內(nèi)容有:(1)研究了滾動軸承故障信號特征的分離方法。通過EEMD削弱背景噪聲;針對軸承組件傳遞路徑對故障特征分離時的干擾現(xiàn)象,利用MED消除其不利影響,運(yùn)用Teager-Kaiser能量算子解調(diào)分析方法從無關(guān)載波分量中提取出故障特征成分,根據(jù)信號頻譜特征判斷滾動軸承的故障類型。通過實驗數(shù)據(jù)進(jìn)行驗證,結(jié)果表明該方法能有效地降低無關(guān)成分干擾并分離出故障特征。(2)對改進(jìn)的NCT滾動軸承階次跟蹤方法進(jìn)行了研究。分析了旋轉(zhuǎn)機(jī)器非平穩(wěn)信號的處理手段,闡明了將非平穩(wěn)信號平穩(wěn)化處理的關(guān)鍵和要點,通過NCT方法將非平穩(wěn)信號刻畫在時頻面上,利用峰值搜索法估計出滾動軸承的瞬時頻率,采用最小二乘擬合方法對該頻率進(jìn)行擬合,設(shè)定合適的閾值,通過循環(huán)估計得到恰當(dāng)?shù)乃矔r頻率;將得到的瞬時頻率經(jīng)過計算得到鑒相時標(biāo),以此時標(biāo)為采樣時刻,實現(xiàn)信號的重采樣。通過仿真實驗,驗證了該方法無需轉(zhuǎn)速計即可獲得鑒相時標(biāo),其結(jié)果具有較高的精度。(3)研究了強(qiáng)噪聲、變轉(zhuǎn)速工況下滾動軸承故障診斷方法。提出了基于EEMD和改進(jìn)型NCT強(qiáng)干擾下的非平穩(wěn)信號處理技術(shù)。通過EEMD和MED聯(lián)合降噪,實現(xiàn)對故障信號的有效分離;對分離后的信號進(jìn)行基于改進(jìn)型NCT的瞬時頻率估計,獲得鑒相時標(biāo),利用能量算子對信號進(jìn)行解調(diào)分析;通過估計瞬時頻率計算出相應(yīng)鑒相時標(biāo),利用鑒相時標(biāo)對解調(diào)信號進(jìn)行階比分析,根據(jù)幅頻譜判斷出故障類型。實驗結(jié)果表明,在變轉(zhuǎn)速工況下,該方法能夠有效的進(jìn)行滾動軸承故障診斷。
[Abstract]:Rotating machinery is widely used in important equipment in industrial production. As one of the important components of rotating machinery, the stability of rolling bearing is closely related to the safe operation of the whole mechanical system. However, fatigue spalling and other faults often occur in the running process of rolling bearings. The early signal of the fault is weak and usually submerged in the background noise. At the same time, the frequency estimation of the equipment caused by the fluctuation of rotating speed caused by non-stationary working conditions such as lifting speed is not accurate, which seriously restricts the accurate diagnosis of bearing faults. The research on the separation method of rolling bearing vibration source signal and the instantaneous frequency estimation method of non-stationary signal will be helpful to accurately extract the fault features and realize the accurate diagnosis of bearing fault. This is of great significance to keep the rotating machinery in good working condition. The main contents of this paper are as follows: (1) the separation method of fault signal characteristics of rolling bearing is studied. The background noise is weakened by EEMD. Aiming at the interference phenomenon of bearing assembly transmission path to fault feature separation, MED is used to eliminate its adverse effects, and Teager-Kaiser energy operator demodulation analysis method is used to extract fault feature components from independent carrier components. According to the signal spectrum characteristics, the fault type of rolling bearing is judged. The experimental results show that the method can effectively reduce the interference of independent components and separate the fault characteristics. (2) the improved NCT rolling bearing order tracking method is studied. This paper analyzes the processing method of non-stationary signal of rotating machine, expounds the key and key points of leveling processing of non-stationary signal, and describes the non-stationary signal on time-frequency surface by NCT method. The instantaneous frequency of rolling bearing is estimated by peak search method, the frequency is fitted by least square fitting method, the appropriate threshold is set, and the appropriate instantaneous frequency is obtained by cyclic estimation. The instantaneous frequency is calculated to obtain the phase discrimination time scale, which is used as the sampling time to realize the resampling of the signal. The simulation results show that the method can obtain phase discrimination time scale without rotating speed meter, and the results have high accuracy. (3) the fault diagnosis method of rolling bearing under strong noise and variable speed is studied. A non-stationary signal processing technology based on EEMD and improved NCT strong interference is proposed. Through the joint noise reduction of EEMD and MED, the fault signal is separated effectively, the instantaneous frequency of the separated signal is estimated based on the improved NCT, the phase discrimination time scale is obtained, and the signal is Demodulated and analyzed by energy operator. By estimating the instantaneous frequency, the corresponding phase discrimination time scale is calculated, and the order analysis of the demodulation signal is carried out by using the phase discrimination time scale, and the fault type is judged according to the amplitude and spectrum. The experimental results show that the method can effectively diagnose the fault of rolling bearings under the condition of variable speed.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH133.33
[Abstract]:Rotating machinery is widely used in important equipment in industrial production. As one of the important components of rotating machinery, the stability of rolling bearing is closely related to the safe operation of the whole mechanical system. However, fatigue spalling and other faults often occur in the running process of rolling bearings. The early signal of the fault is weak and usually submerged in the background noise. At the same time, the frequency estimation of the equipment caused by the fluctuation of rotating speed caused by non-stationary working conditions such as lifting speed is not accurate, which seriously restricts the accurate diagnosis of bearing faults. The research on the separation method of rolling bearing vibration source signal and the instantaneous frequency estimation method of non-stationary signal will be helpful to accurately extract the fault features and realize the accurate diagnosis of bearing fault. This is of great significance to keep the rotating machinery in good working condition. The main contents of this paper are as follows: (1) the separation method of fault signal characteristics of rolling bearing is studied. The background noise is weakened by EEMD. Aiming at the interference phenomenon of bearing assembly transmission path to fault feature separation, MED is used to eliminate its adverse effects, and Teager-Kaiser energy operator demodulation analysis method is used to extract fault feature components from independent carrier components. According to the signal spectrum characteristics, the fault type of rolling bearing is judged. The experimental results show that the method can effectively reduce the interference of independent components and separate the fault characteristics. (2) the improved NCT rolling bearing order tracking method is studied. This paper analyzes the processing method of non-stationary signal of rotating machine, expounds the key and key points of leveling processing of non-stationary signal, and describes the non-stationary signal on time-frequency surface by NCT method. The instantaneous frequency of rolling bearing is estimated by peak search method, the frequency is fitted by least square fitting method, the appropriate threshold is set, and the appropriate instantaneous frequency is obtained by cyclic estimation. The instantaneous frequency is calculated to obtain the phase discrimination time scale, which is used as the sampling time to realize the resampling of the signal. The simulation results show that the method can obtain phase discrimination time scale without rotating speed meter, and the results have high accuracy. (3) the fault diagnosis method of rolling bearing under strong noise and variable speed is studied. A non-stationary signal processing technology based on EEMD and improved NCT strong interference is proposed. Through the joint noise reduction of EEMD and MED, the fault signal is separated effectively, the instantaneous frequency of the separated signal is estimated based on the improved NCT, the phase discrimination time scale is obtained, and the signal is Demodulated and analyzed by energy operator. By estimating the instantaneous frequency, the corresponding phase discrimination time scale is calculated, and the order analysis of the demodulation signal is carried out by using the phase discrimination time scale, and the fault type is judged according to the amplitude and spectrum. The experimental results show that the method can effectively diagnose the fault of rolling bearings under the condition of variable speed.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH133.33
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