滾動軸承故障診斷與振動信號處理
發(fā)布時間：2019-04-01 20:51
【摘要】：滾動軸承作為一個基礎零部件，它的運轉可靠性直接影響到整個系統(tǒng)工作的穩(wěn)定。能夠及時較為準確的定位軸承故障部位同時診斷故障嚴重程度對于減少不必要的損失和確保人員安全起到至關重要的作用。本文主要通過實驗和仿真模擬故障同時分析故障振動信號特點來達到這一目的。 本文首先理論研究了軸承故障振動信號的產生機理和信號特性，，同時結合實際205軸承利用運動學關系推導了信號特征頻率。仿真部分建立了滾動軸承的Patran有限元模型和Adams動力學模型，通過有限元模型分析了軸承載荷分布、固有頻率及模態(tài)在故障振動信號中的表現(xiàn)形式，通過動力學模型分析了不同故障發(fā)生部位的故障特征與故障信號影響因素：故障大小、載荷、轉速、間隙。同時提出了測點優(yōu)化方案，對于改變垂向測點為45度斜向的方式來提高故障脈沖信號的測量靈敏度。 對205軸承8個不同故障做了1200組實驗信號采集，利用時頻域分析與統(tǒng)計分析兩大類方法做了定位故障與分析故障嚴重程度的診斷研究。統(tǒng)計量的應用以大量數(shù)據(jù)為背景，人為劃分故障分析區(qū)間，用峭度方法診斷故障模式，用P/R方法診斷故障程度；時頻域分析主要利用改進共振解調多次包絡的方式驗證信號的診斷準確性，診斷正確率在90%左右，同時利用譜峭度、小波包能量的方法來提取特征信號與濾除干擾信號，達到提高信號信噪比效果。
[Abstract]:Rolling bearing as a basic component, its running reliability directly affects the stability of the whole system. It is very important to locate the bearing fault location and diagnose the fault severity in time to reduce unnecessary loss and ensure the safety of personnel. The main purpose of this paper is to analyze the characteristics of the fault vibration signal through the simulation and experiment of the fault at the same time. In this paper, the generating mechanism and signal characteristics of bearing fault vibration signal are studied theoretically, and the characteristic frequency of bearing fault vibration signal is deduced according to the kinematic relation of 205 bearing. In the simulation part, the Patran finite element model and the Adams dynamic model of the rolling bearing are established. Through the finite element model, the bearing load distribution, natural frequency and modal representation in the fault vibration signal are analyzed. The dynamic model is used to analyze the fault characteristics of different fault locations and the influencing factors of fault signal: fault size, load, rotational speed and clearance. At the same time, an optimization scheme is proposed to improve the measurement sensitivity of the fault pulse signal by changing the vertical measurement point to 45 degrees oblique direction. 1200 sets of experimental signals were collected for 8 different faults of bearings. The diagnosis of locating faults and analyzing the severity of faults was carried out by using two kinds of methods: time-frequency domain analysis and statistical analysis. The application of statistics is based on a large number of data, divide the fault analysis interval artificially, diagnose the fault mode by kurtosis method, and diagnose the fault degree by Pur method. The time-frequency domain analysis mainly uses the improved resonance demodulation multiple envelope method to verify the diagnostic accuracy of the signal, and the diagnostic accuracy is about 90%. At the same time, the method of spectrum kurtosis and wavelet packet energy is used to extract the characteristic signal and filter the interference signal. The signal-to-noise ratio is improved.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH133.33
本文編號：2451897
[Abstract]:Rolling bearing as a basic component, its running reliability directly affects the stability of the whole system. It is very important to locate the bearing fault location and diagnose the fault severity in time to reduce unnecessary loss and ensure the safety of personnel. The main purpose of this paper is to analyze the characteristics of the fault vibration signal through the simulation and experiment of the fault at the same time. In this paper, the generating mechanism and signal characteristics of bearing fault vibration signal are studied theoretically, and the characteristic frequency of bearing fault vibration signal is deduced according to the kinematic relation of 205 bearing. In the simulation part, the Patran finite element model and the Adams dynamic model of the rolling bearing are established. Through the finite element model, the bearing load distribution, natural frequency and modal representation in the fault vibration signal are analyzed. The dynamic model is used to analyze the fault characteristics of different fault locations and the influencing factors of fault signal: fault size, load, rotational speed and clearance. At the same time, an optimization scheme is proposed to improve the measurement sensitivity of the fault pulse signal by changing the vertical measurement point to 45 degrees oblique direction. 1200 sets of experimental signals were collected for 8 different faults of bearings. The diagnosis of locating faults and analyzing the severity of faults was carried out by using two kinds of methods: time-frequency domain analysis and statistical analysis. The application of statistics is based on a large number of data, divide the fault analysis interval artificially, diagnose the fault mode by kurtosis method, and diagnose the fault degree by Pur method. The time-frequency domain analysis mainly uses the improved resonance demodulation multiple envelope method to verify the diagnostic accuracy of the signal, and the diagnostic accuracy is about 90%. At the same time, the method of spectrum kurtosis and wavelet packet energy is used to extract the characteristic signal and filter the interference signal. The signal-to-noise ratio is improved.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH133.33
【參考文獻】
相關期刊論文 前10條
1 劉明利;傅行軍;;一種對振動信號小波包能量算法的研究和改進[J];華東電力;2009年08期
2 李嶷,熊國良,張龍;經驗模式分解法(EMD)在滾動軸承故障診斷中的應用[J];華東交通大學學報;2005年01期
3 唐云冰;高德平;羅貴火;;航空發(fā)動機高速滾珠軸承力學特性分析與研究[J];航空動力學報;2006年02期
4 梅宏斌,吳雅,師漢民,楊叔子;滾動軸承損傷類故障的一種精確診斷模型[J];華中理工大學學報;1992年S1期
5 丁芳;高立新;崔玲麗;張建宇;胥永剛;;共振解調技術在設備故障診斷中的應用[J];機械設計與制造;2007年11期
6 梅宏斌;楊叔子;師漢民;;滾動軸承故障診斷高頻共振法的包絡頻譜分析[J];洛陽工學院學報;1992年04期
7 李國臣;李永強;;薄壁圓柱殼固有頻率的計算[J];機械科學與技術;2010年09期
8 李自光;;機械故障的油樣分析診斷[J];機械制造;1992年10期
9 徐國棟;周錫元;郭明珠;董娣;;典型振動信號時-頻分析方法研究[J];世界地震工程;2007年02期
10 馬新鋒;顧海明;李澤豪;;基于EMD和Hilbert-Huang的滾動軸承故障診斷[J];通用機械;2011年08期
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