【摘要】：旋轉(zhuǎn)機(jī)械在機(jī)械設(shè)備中占有舉足輕重的地位，它們大多數(shù)為生產(chǎn)企業(yè)中的關(guān)鍵設(shè)備，因此，保證旋轉(zhuǎn)機(jī)械的安全可靠運行對企業(yè)和國民經(jīng)濟(jì)有重要的意義。軸承、齒輪等旋轉(zhuǎn)零部件在機(jī)械系統(tǒng)中起著支撐、定位和傳遞動力等作用，是旋轉(zhuǎn)設(shè)備的關(guān)鍵部件，當(dāng)它們出現(xiàn)局部故障時，在運行中振動信號中會出現(xiàn)瞬態(tài)沖擊響應(yīng)成分，對旋轉(zhuǎn)機(jī)械瞬態(tài)特征的表示和提取關(guān)系到故障診斷的可靠性與準(zhǔn)確性，是機(jī)械設(shè)備故障診斷的關(guān)鍵問題。本文以軸承和齒輪兩種典型的零部件為研究對象，針對變轉(zhuǎn)速下旋轉(zhuǎn)機(jī)械瞬態(tài)特征表示與提取的問題提出了基于時頻特征融合的時頻特征表示方法、時頻特征重采樣方法、周期特征的極坐標(biāo)同步增強(qiáng)檢測方法和變轉(zhuǎn)速下瞬態(tài)特征的自動檢測方法，并分別就有關(guān)問題進(jìn)行了深入的理論研究和應(yīng)用研究。 介紹了軸承和齒輪常見的失效形式以及滾動軸承的運動學(xué)，并進(jìn)行了軸承的局部故障實驗設(shè)置，對恒定轉(zhuǎn)速和變轉(zhuǎn)速的振動信號進(jìn)行了采集，保證理論研究工作都建立在實驗驗證的基礎(chǔ)上。 現(xiàn)有的兩類時頻分析方法，線性時頻分析和雙線性時頻分析，線性時頻分析沒有交叉項但聚集性差，雙線性時頻分析聚集性好但存在交叉項，基于此基礎(chǔ)提出了一種基于線性時頻分析和雙線性時頻分析融合的策略，此方法類似于邏輯與，對時頻特征融合，得到了一種新的時頻特征表征方法。對仿真信號以及軸承和齒輪實測振動信號的應(yīng)用驗證了該方法對軸承和齒輪故障特征提取的有效性和適用性。 在變轉(zhuǎn)速下，瞬態(tài)成分不再是等周期的，而是和轉(zhuǎn)速有關(guān)。在實際應(yīng)用中，對于變轉(zhuǎn)速下振動信號的處理，階比分析是一種非常有效的手段，對時域信號重采樣使其在階域上滿足傅里葉分析的要求。由于旋轉(zhuǎn)部件局部故障引起的瞬態(tài)成分在高頻處趨向于振蕩阻尼振動，對瞬態(tài)成分重采樣會不可避免的引起失真�？紤]到瞬態(tài)成分在時頻域上是能量分布，不存在明顯的振蕩，因此提出了時頻特征重采樣方法，，其本質(zhì)是將對信號時域上的重采樣擴(kuò)展到時頻域上對時頻特征值的重采樣，可以將非等周期的時頻特征轉(zhuǎn)化成等周期的時頻特征。 對于變轉(zhuǎn)速下地局部故障，通常會存在某一瞬態(tài)角度特征周期，而且通過時頻特征重采樣，瞬態(tài)特征將會等周期的表示在角度頻率域，然而，這仍然很難獲得角頻域上的角度特征周期和故障類型之間的關(guān)系。本文提出了周期特征的極坐標(biāo)同步增強(qiáng)檢測方法。將時頻特征重采樣和周期特征的極坐標(biāo)同步增強(qiáng)應(yīng)用到軸承的外圈、內(nèi)圈和滾動體三種典型故障中，可以根據(jù)極坐標(biāo)上的特征分布有效的識別故障類型。 針對變轉(zhuǎn)速下機(jī)械設(shè)備故障診斷問題，提出了一種變轉(zhuǎn)速下旋轉(zhuǎn)機(jī)械瞬態(tài)特征的自動檢測方法，是對時頻特征融合策略、時頻特征重采樣和周期特征的極坐標(biāo)同步增強(qiáng)的結(jié)合。首先，對變轉(zhuǎn)下測得的振動信號應(yīng)用Wigner-ville分布和小波尺度譜這兩種不同的時頻分析方法，在這兩種時頻表示的基礎(chǔ)上應(yīng)用融合策略，可以將變轉(zhuǎn)下的瞬態(tài)特征很好的表示在時頻面上。然后，對得到的時頻特征矩陣應(yīng)用時頻特征重采樣，將非等周期的瞬態(tài)特征變成了角頻域上的等周期瞬態(tài)特征。最后，應(yīng)用周期特征的極坐標(biāo)同步增強(qiáng)，可以得到變轉(zhuǎn)下振動信號瞬態(tài)特征的增強(qiáng)表示。仿真信號以及軸承實測振動信號的應(yīng)用驗證了該方法的有效性和適用性。 本論文依托于“變工況下旋轉(zhuǎn)設(shè)備輕微局部故障的特征增強(qiáng)檢測與診斷方法研究”國家自然科學(xué)基金青年基金項目（批準(zhǔn)號：50905121）。
[Abstract]:Rotating machinery plays an important role in machinery and equipment. Most of them are key equipment in manufacturing enterprises. Therefore, it is of great significance for enterprises and national economy to ensure the safe and reliable operation of rotating machinery. The key components of the equipment, when they have local faults, will appear transient impulse response components in the vibration signals in operation. The representation and extraction of the transient characteristics of rotating machinery is related to the reliability and accuracy of fault diagnosis, and is the key problem of mechanical equipment fault diagnosis. Aiming at the problem of instantaneous feature representation and extraction of rotating machinery under variable speed, the time-frequency feature representation method based on time-frequency feature fusion, the time-frequency feature resampling method, the polar coordinate synchronization enhancement detection method of periodic feature and the automatic detection method of instantaneous feature under variable speed are proposed. Deep theoretical research and applied research.
The common failure modes of bearings and gears and the kinematics of rolling bearings are introduced. The local fault experiment settings of bearings are carried out. The vibration signals of constant and variable speed are collected to ensure that the theoretical research work is based on the experimental verification.
There are two kinds of time-frequency analysis methods, linear time-frequency analysis and bilinear time-frequency analysis. Linear time-frequency analysis has no cross-terms but poor aggregation. Bilinear time-frequency analysis has good aggregation but cross-terms. Based on this, a fusion strategy based on linear time-frequency analysis and bilinear time-frequency analysis is proposed. This method is similar to logic and bilinear time-frequency analysis. A new time-frequency feature representation method is proposed for time-frequency feature fusion. The validity and applicability of this method for bearing and gear fault feature extraction are verified by the application of simulation signal and bearing and gear vibration signal.
In practical application, order analysis is a very effective method for vibration signal processing under variable rotational speed. The time domain signal is resampled to meet the requirements of Fourier analysis in order domain. The resampling of transient components will inevitably lead to distortion when they tend to oscillate and damp vibration at high frequencies. Considering that the transient components are energy-distributed in time-frequency domain and there is no obvious oscillation, a time-frequency characteristic resampling method is proposed. The essence of this method is to extend the resampling of signals in time-domain to the time-frequency domain and to the time-frequency characteristic values. The resampling method can transform the non equal period time-frequency characteristics into equal cycle time-frequency characteristics.
For local faults with variable speed, there is usually a transient angle characteristic period, and by resampling the time-frequency characteristics, the transient characteristics will be expressed in the angle-frequency domain. However, it is still difficult to obtain the relationship between the angle characteristic period and the fault type in the angle-frequency domain. The time-frequency feature resampling and periodic feature polar coordinate synchronization enhancement are applied to three typical faults of bearing, i.e. outer ring, inner ring and rolling element. Fault types can be effectively identified according to the distribution of features in polar coordinates.
Aiming at the fault diagnosis of rotating machinery under variable rotating speed, an automatic detection method for the transient characteristics of rotating machinery under variable rotating speed is proposed, which combines time-frequency feature fusion strategy, time-frequency feature resampling and polar coordinate synchronization enhancement of periodic features. Firstly, Wigner-ville distribution and wavelet ruler are applied to the vibration signals measured under variable rotating speed. Based on these two time-frequency representations, the fusion strategy can be used to represent the transient characteristics in the time-frequency plane. Then, the time-frequency characteristic resampling is applied to the obtained time-frequency characteristic matrix, and the non-periodic transient characteristics are transformed into the equal-periodic transient characteristics in the angular-frequency domain. Finally, the polar coordinate synchronous enhancement of periodic characteristics can be used to enhance the transient characteristics of vibration signals under variable rotation. The effectiveness and applicability of this method are verified by the application of simulation signals and bearing vibration signals.
This paper relies on the project of National Natural Science Foundation of China (Grant No. 50905121) of "Research on Feature Enhanced Detection and Diagnosis Method for Slight Local Faults of Rotating Equipment under Variable Working Conditions".
【學(xué)位授予單位】：蘇州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3

【引證文獻(xiàn)】

相關(guān)博士學(xué)位論文 前1條

1 郭瑜;基于時—頻分析的虛擬式旋轉(zhuǎn)機(jī)械特征分析儀系統(tǒng)的研究[D];重慶大學(xué);2003年

相關(guān)碩士學(xué)位論文 前2條

1 陳祥芹;振動信號多元統(tǒng)計分析特征提取及傳動系統(tǒng)關(guān)鍵部件故障診斷應(yīng)用[D];蘇州大學(xué);2014年

2 李剛;基于虛擬儀器技術(shù)的逆變器監(jiān)測系統(tǒng)設(shè)計[D];青島大學(xué);2014年

本文編號：2199782