發(fā)布時間：2019-04-12 10:02

【摘要】：滾動軸承是傳動機械的核心部件，其運行狀態(tài)直接影響到整臺設(shè)備的精度、可靠性及壽命等性能。由于其本身的結(jié)構(gòu)特點及工作環(huán)境等因素，滾動軸承極易出現(xiàn)故障。軸承故障的特征向量和識別模式之間呈復(fù)雜的非線性關(guān)系，在軸承早期微弱故障和復(fù)合故障的定量診斷與預(yù)示中，如何從非平穩(wěn)、非線性振動信號中提取有效的故障信息就成了關(guān)鍵，對這一問題進行研究在機械故障診斷中具有重要的理論及現(xiàn)實意義。論文主要研究內(nèi)容如下： 首先，在對滾動軸承故障機理和故障形式及成因進行全面分析的基礎(chǔ)上，模擬滾動軸承主要故障，通過滾動軸承振動檢測與診斷試驗系統(tǒng)實現(xiàn)對正常和故障狀態(tài)下的振動信號的采集，并對所得到信號進行時域參數(shù)特征統(tǒng)計和時頻域處理，以分析滾動軸承不同狀態(tài)下的振動特性。 其次，研究了基于隨機共振的滾動軸承早期故障識別方法，分析了單穩(wěn)隨機共振模型下的變尺度級聯(lián)效應(yīng)，通過正常狀態(tài)以及外圈早期故障的仿真和實測數(shù)據(jù)，驗證了隨機共振在抑制軸承背景噪聲、早期故障特征提取方面的可行性和實用性。 再次，提出了隨機共振（SR）消噪下的總體平均經(jīng)驗?zāi)Ｊ椒纸猓‥EMD）的滾動軸承特征提取方法，探討了EEMD方法在自適應(yīng)分解、抗模式混疊方面的優(yōu)勢，并結(jié)合包絡(luò)解調(diào)技術(shù)，將其成功應(yīng)用于滾動軸承早期單點故障及耦合故障的特征提取。 最后，在SR-EEMD方法所構(gòu)建故障特征向量的基礎(chǔ)上，利用BP和RBF兩種神經(jīng)網(wǎng)絡(luò)模型分別對滾動軸承狀態(tài)樣本集進行訓(xùn)練和預(yù)測，再通過遺傳算法對RBF網(wǎng)絡(luò)進行參數(shù)優(yōu)化，提高了網(wǎng)絡(luò)性能。
[Abstract]:Rolling bearing is the core component of transmission machinery, and its running state directly affects the precision, reliability and life of the whole equipment. Because of its structural characteristics and working environment, rolling bearings are prone to fault. There is a complex nonlinear relationship between the characteristic vector and the recognition pattern of bearing fault. In the quantitative diagnosis and prediction of weak and compound faults in the early stage of bearing, how to solve the problem from non-stationary to non-stationary? It is very important to extract effective fault information from nonlinear vibration signals. The research on this problem is of great theoretical and practical significance in mechanical fault diagnosis. The main contents of this paper are as follows: firstly, the main faults of rolling bearings are simulated on the basis of a comprehensive analysis of the fault mechanism, fault form and cause of failure. Through the rolling bearing vibration detection and diagnosis test system, the vibration signals under normal and fault conditions are collected, and the time-domain parameter characteristic statistics and time-frequency domain processing of the obtained signals are carried out. In order to analyze the vibration characteristics of rolling bearings under different conditions. Secondly, the early fault identification method of rolling bearing based on stochastic resonance is studied, and the variable scale cascade effect under the monostable stochastic resonance model is analyzed. The simulation and measured data of the normal state and the early fault of the outer ring are carried out. The feasibility and practicability of stochastic resonance in suppressing bearing background noise and extracting early fault features are verified. Thirdly, the general average empirical mode decomposition (EEMD) method of rolling bearing feature extraction based on stochastic resonance (SR) de-noising is proposed, and the advantages of EEMD method in adaptive decomposition and anti-mode mixing are discussed, and the envelope demodulation technique is combined with the method of self-adaptive decomposition and anti-mode aliasing. It is successfully applied to feature extraction of early single point fault and coupling fault of rolling bearing. Finally, based on the fault eigenvector constructed by SR-EEMD method, two neural network models, BP and RBF, are used to train and predict the sample set of rolling bearing state, and then the parameters of RBF network are optimized by genetic algorithm. Improved network performance.
【學(xué)位授予單位】：中國計量學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TH133.33;TH165.3

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