本文關鍵詞： 自適應 線調頻基 原子分解 時域同步平均 循環(huán)頻率 神經(jīng)網(wǎng)絡 齒輪 滾動軸承 故障診斷　出處：《湖南大學》2011年碩士論文　論文類型：學位論文

【摘要】：生產(chǎn)過程中發(fā)生的設備故障會導致設備停機或機器損壞，進而導致生產(chǎn)中斷，因而在制造業(yè)尤其是流程制造業(yè)中，對機械設備的狀態(tài)監(jiān)測與故障診斷具有重要的理論意義和實用價值。從機械設備的故障振動信號中提取故障特征信息，是機械設備故障診斷的關鍵。 在變轉速工況下，機械設備的振動信號往往包含了更多的設備運轉信息和故障信息，系統(tǒng)缺陷能更容易地被發(fā)現(xiàn)。然而以等采樣頻率采集的振動信號在轉速波動工況下往往表現(xiàn)出強烈的非平穩(wěn)性和低信噪比性，導致目前常用的信號處理技術無法從中準確提取故障特征信息。本文在國家高技術研究發(fā)展計劃(863計劃)項目“大型風力發(fā)電機組狀態(tài)監(jiān)控與故障診斷技術研究”(項目編號：2009AA04Z414)和國家自然科學基金項目“多尺度線調頻基稀疏信號分解方法及其在機械故障診斷中的應用研究”(項目批準號：50875078)資助下，針對現(xiàn)有信號處理方法時頻聚集性不夠，抗噪性能不強等缺點，研究提出了一種新的信號處理方法—自適應線調頻基原子分解(adaptive chirplet atomicdecomposition, ACAD)方法，并將其應用于轉速變化的齒輪和滾動軸承的故障診斷中。本文的主要研究工作有： (1)針對多尺度線調頻基稀疏信號分解方法算法效率低、分解分量幅值失真等問題，研究提出了ACAD方法，并證明了該方法具有良好的分解精度、較好的抗噪性能和較高的分解效率，非常適合于多分量非平穩(wěn)信號的分析處理。 (2)針對變轉速工況下低信噪比的故障齒輪振動信號調制邊頻帶難以識別的問題，研究提出了基于ACAD的時域同步平均方法。ACAD方法可以有效地提取齒輪的嚙合頻率曲線，從而獲得齒輪轉速曲線，再對振動信號進行角域重采樣，可滿足時域同步平均對信號平穩(wěn)性要求。仿真和實驗分析證明了基于ACAD的時域同步平均方法能清晰獲取齒輪的故障調制階次，非常適合于轉速劇烈波動情況下的齒輪故障診斷。 (3)提出了基于ACAD階次包絡和循環(huán)頻率的變轉速齒輪故障診斷方法。包絡譜和循環(huán)頻率分析方法是一種有效的齒輪幅值和相位調制頻率提取方法，但在變轉速工況下齒輪振動信號往往表現(xiàn)出劇烈的非平穩(wěn)性，由于故障而產(chǎn)生的調制頻率成分也會隨著轉速變化而變化，不滿足FFT對信號的平穩(wěn)性要求，包絡譜和循環(huán)頻率分析無法提取齒輪的故障信息�；贏CAD階次包絡和循環(huán)頻率的變轉速齒輪故障診斷方法先利用ACAD從齒輪振動信號中提取嚙合頻率，從而獲得齒輪轉速曲線，根據(jù)獲得的轉頻曲線再對原始信號進行角域重采樣。對重采樣信號進行Hilbert變換分別提取其包絡和相位。對包絡信號進行FFT變換獲取幅值調制頻率，對相位信號進行循環(huán)頻率獲取相位調制頻率，從而實現(xiàn)齒輪的故障診斷。 (4)將ACAD方法與神經(jīng)網(wǎng)絡結合應用于變轉速工況下滾動軸承的故障識別。采用ACAD方法從滾動軸承振動信號的包絡中提取故障特征頻率及其倍頻分量，再從這些特征故障分量中提取能量、方差等時域特征參數(shù)作為神經(jīng)網(wǎng)絡的輸入?yún)?shù)來識別滾動軸承的故障模式。應用實例證明該方法可以準確有效地對滾動軸承的工作狀態(tài)和故障類型進行分類。 本文研究了適合處理多分量非平穩(wěn)信號的ACAD方法，并在其基礎上提出了基于ACAD的時域同步平均方法、基于ACAD的階次包絡和循環(huán)頻率方法和基于ACAD的神經(jīng)網(wǎng)絡方法，，這些方法能有效應用于變轉速工況下齒輪和滾動軸承的故障診斷。仿真算例和應用實例表明，ACAD方法在機械故障診斷中具有良好的應用前景。
[Abstract]:Equipment failure in the production process will lead to downtime or damage, leading to production disruptions, and especially in the manufacturing process in the manufacturing industry, and has important theoretical significance and practical value of state monitoring and fault diagnosis for mechanical equipment. The fault feature extraction from vibration signals of the mechanical equipment is the key fault diagnosis of mechanical equipment.
Under variable speed condition, the vibration signals of mechanical equipment often contains more equipment operation and fault information system defects can more easily be found. However, as the sampling frequency acquisition of vibration signal in the speed fluctuation conditions often show a strong non stationarity and low signal-to-noise ratio, leading to the current signal the commonly used processing technology is unable to accurately extract the fault feature information. Based on the national high technology research and development program (863 Program) project "large wind turbine condition monitoring and fault diagnosis technology research" (project number: 2009AA04Z414) and the application of the National Natural Science Fund Project "Multi-scale Chirplet and sparse signal decomposition method and in mechanical fault diagnosis" (Project No.: 50875078) supported by the existing signal processing method of time-frequency anti noise performance is not strong enough, etc. A new signal processing method, adaptive adaptive chirplet atomicdecomposition (ACAD), is proposed and applied to fault diagnosis of gear and rolling bearings with variable speed.
(1) based on Multi-scale Chirplet and sparse signal decomposition algorithm low efficiency problem decomposition component amplitude distortion, the research put forward the ACAD method, and proves that this method has good accuracy of decomposition, the decomposition efficiency and better anti noise performance, very suitable for the analysis on the multi-component non-stationary signals.
(2) aiming at the fault of gear vibration signal modulation variable speed under the condition of low SNR sidebands are difficult to identify problems, put forward the research method of ACAD.ACAD synchronous averaging method based on time domain can effectively extract the meshing frequency curve of gear, gear and speed curve is obtained, and then the vibration signal of angle domain resampling, can to meet the time synchronous average signal stationarity. Simulation analysis and experimental results show that the ACAD based on the time-domain synchronous average method can clearly obtain the gear fault modulation order, gear fault diagnosis is very suitable for the circumstance of drastic speed fluctuation.
(3) the variable speed gear fault diagnosis method of ACAD order envelope and cycle frequency. Based on the envelope spectrum and frequency analysis method is an effective method for detection of circular gear amplitude and phase modulation frequency, but in the condition of variable speed gear vibration signals often exhibit non-stationary intensity, modulation frequency components the fault will also change with the speed change, does not meet the FFT stationary signal, envelope spectrum and cycle frequency analysis to extract fault information of the gear. The ACAD order envelope and cycle frequency variable speed gear fault diagnosis method based on the first use of ACAD extracted from the vibration signal of the gear meshing frequency, thus the gear speed curve is obtained, according to the rotation frequency of the obtained curve and the original signal is resampled in angle domain. The sampling signal Hilbert transform were extracted from the envelope and phase of the envelope. The amplitude modulation frequency is obtained by the FFT transform, and the phase modulation frequency is obtained by the cycle frequency of the phase signal, thus the fault diagnosis of the gear is realized.
(4) the ACAD fault identification method and neural network combined with the application of rolling bearing in variable speed conditions. Extracting fault characteristic frequency and frequency component from the envelope of the rolling bearing vibration signal using ACAD method to extract energy from these characteristics of fault component, variance domain parameters as input parameters of neural network to identify fault pattern of rolling bearing. The application example proves that the method can effectively and accurately classify the working condition of roller bearings and fault types.
In this paper the ACAD method for processing the multi-component non-stationary signals, and proposed a ACAD based on the time-domain synchronous average method on the basis of it, based on the order envelope and cycle frequency and method of ACAD based on ACAD neural network method, these methods can be effectively applied to variable speed under the condition of gear and rolling bearing fault the diagnosis. The simulation and application examples demonstrate that the ACAD method has a good application prospect in mechanical fault diagnosis.

【學位授予單位】：湖南大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

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