本文選題：滾動(dòng)軸承 + 局域均值分解　； 參考：《燕山大學(xué)》2015年碩士論文

【摘要】：滾動(dòng)軸承廣泛應(yīng)用于工業(yè)生產(chǎn)中,其作為機(jī)械設(shè)備的核心部件,運(yùn)行狀態(tài)直接影響到機(jī)械設(shè)備的可靠性及穩(wěn)定性。因此,對滾動(dòng)軸承進(jìn)行故障診斷對于機(jī)械設(shè)備的運(yùn)行維護(hù)具有重要意義。故障信息的特征提取是軸承故障診斷的關(guān)鍵,本文針對滾動(dòng)軸承故障診斷,運(yùn)用局域均值分解(Local Mean Decomposition,LMD)、形態(tài)濾波和近似熵理論,分別從信號濾波去噪和信號序列復(fù)雜度的角度出發(fā),對滾動(dòng)軸承振動(dòng)信號提取方法進(jìn)行了實(shí)驗(yàn)研究,為滾動(dòng)軸承故障診斷的特征提取提供了理論依據(jù)。論文的主要研究工作如下:(1)針對滾動(dòng)軸承振動(dòng)信號的非平穩(wěn)性特點(diǎn),以及實(shí)際故障特征信號難以提取的問題,研究了局域均值分解方法,該方法能將復(fù)雜的非平穩(wěn)信號分解成一系列調(diào)幅調(diào)頻函數(shù),實(shí)現(xiàn)信號中不同調(diào)制頻率成分的分離,能有效的分離出故障成分,可應(yīng)用到實(shí)際滾動(dòng)軸承振動(dòng)信號的分解中。(2)針對實(shí)際軸承振動(dòng)信號噪聲干擾嚴(yán)重的問題,研究了形態(tài)濾波算法在振動(dòng)信號處理中的應(yīng)用,并研究了基于信號極值點(diǎn)確定形態(tài)濾波結(jié)構(gòu)元素長度的自適應(yīng)形態(tài)濾波方法,該方法能在保持原信號面貌的基礎(chǔ)上,最大程度的抑制沖擊脈沖噪聲的影響,并將濾波后的信號進(jìn)行局域均值分解提取故障特征信息,仿真實(shí)驗(yàn)表明將形態(tài)濾波與局域均值分解相結(jié)合能有效提取信號中的故障特征信息。(3)最后從描述信號復(fù)雜度的角度出發(fā),采用基于LMD的多尺度近似熵方法,對軸承振動(dòng)信號進(jìn)行故障特征提取,該方法能有效區(qū)分不同的故障類型,比近似熵具有更強(qiáng)的抗干擾能力,獲取更多的故障特征信息,仿真實(shí)驗(yàn)和實(shí)例分析表明,該方法可以應(yīng)用到軸承故障特征提取中,判斷軸承的運(yùn)行狀態(tài)。
[Abstract]:Rolling bearings are widely used in industrial production. As the core components of mechanical equipment, the running state of rolling bearings directly affects the reliability and stability of mechanical equipment.Therefore, the fault diagnosis of rolling bearings is of great significance to the operation and maintenance of mechanical equipment.The feature extraction of fault information is the key of bearing fault diagnosis. In this paper, local mean decomposition (LMD), morphological filtering and approximate entropy theory are used to diagnose rolling bearing faults.From the angle of signal filter denoising and signal sequence complexity, the vibration signal extraction method of rolling bearing is studied experimentally, which provides a theoretical basis for feature extraction of rolling bearing fault diagnosis.The main research work of this paper is as follows: (1) aiming at the non-stationary characteristic of rolling bearing vibration signal and the difficulty of extracting the actual fault characteristic signal, the local mean decomposition method is studied.This method can decompose the complex non-stationary signal into a series of amplitude modulation and frequency modulation functions, realize the separation of different modulation frequency components in the signal, and can effectively separate out the fault component.It can be applied to the decomposition of actual rolling bearing vibration signal. Aiming at the problem of serious noise interference of actual bearing vibration signal, the application of morphological filtering algorithm in vibration signal processing is studied.The filtered signal is decomposed into local mean to extract the fault feature information.Simulation results show that the combination of morphological filtering and local mean decomposition can effectively extract fault feature information from the signal. Finally, from the point of describing the complexity of the signal, the multi-scale approximate entropy method based on LMD is adopted.Fault feature extraction of bearing vibration signal shows that this method can distinguish different fault types effectively, has stronger anti-interference ability than approximate entropy, and obtains more fault feature information. The simulation experiment and example analysis show that the proposed method can effectively distinguish different fault types, and obtain more fault feature information than approximate entropy.This method can be applied to the bearing fault feature extraction to judge the bearing running state.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TH133.33

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本文編號：1733636