發(fā)布時(shí)間：2018-01-05 18:06

  本文關(guān)鍵詞：頻帶熵方法及其在滾動(dòng)軸承故障診斷中的應(yīng)用　出處：《上海交通大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 故障診斷 狀態(tài)監(jiān)測(cè) 時(shí)頻分析 頻帶熵 帶通濾波 遺傳算法 滾動(dòng)軸承

【摘要】：隨著科學(xué)技術(shù)的日益進(jìn)步與現(xiàn)代工業(yè)的飛速發(fā)展,機(jī)械設(shè)備不斷向大型、復(fù)雜、高速、高效及重載的方向發(fā)展;與此同時(shí),其工作和運(yùn)行環(huán)境也更加復(fù)雜和苛刻。這些設(shè)備一旦突然發(fā)生故障,不僅會(huì)增加企業(yè)的維護(hù)成本,降低企業(yè)的生產(chǎn)效率,還可能造成巨大的經(jīng)濟(jì)損失,甚至導(dǎo)致嚴(yán)重的人員傷亡,產(chǎn)生不良的社會(huì)影響。因此,如何對(duì)設(shè)備進(jìn)行有效的狀態(tài)監(jiān)測(cè)和故障診斷,是當(dāng)前亟需解決的問(wèn)題。 如何有效提取反映設(shè)備運(yùn)行狀態(tài)的特征,以及準(zhǔn)確判斷故障類(lèi)別,一直是故障診斷領(lǐng)域的研究熱點(diǎn),新方法和新理論的研究也層出不窮,對(duì)豐富和完善機(jī)械故障診斷技術(shù)起到了重要作用。本文以滾動(dòng)軸承為研究對(duì)象,提出頻帶熵方法,并對(duì)其在故障診斷中的應(yīng)用進(jìn)行了研究,旨在為滾動(dòng)軸承狀態(tài)監(jiān)測(cè)提供一個(gè)新指標(biāo),為故障診斷信號(hào)預(yù)處理提供一種新方法,論文主要包括以下幾個(gè)方面的內(nèi)容: (1)從理論分析與工程應(yīng)用的角度出發(fā),闡述了論文的選題背景和研究意義。分析了機(jī)械設(shè)備故障診斷方法、滾動(dòng)軸承故障診斷、時(shí)頻分析與信息熵理論等方面的國(guó)內(nèi)外研究現(xiàn)狀,確立了本文的研究?jī)?nèi)容。 (2)介紹了作為本文理論基礎(chǔ)的幾種時(shí)頻分析方法及信息熵理論,借鑒譜峭度方法提出頻帶熵概念,定義頻帶熵為某一頻率上(頻帶內(nèi))信號(hào)的復(fù)雜度,或者說(shuō)不確定性,給出了頻帶熵的基本算法,最后從濾波的角度對(duì)頻帶熵概念進(jìn)行了擴(kuò)展。 (3)介紹了滾動(dòng)軸承的振動(dòng)機(jī)理和故障特征。討論了頻帶熵指標(biāo)用于滾動(dòng)軸承狀態(tài)監(jiān)測(cè)的可能性,對(duì)其魯棒性進(jìn)行了研究,證明其對(duì)奇異點(diǎn)的不敏感性�；�于頻帶熵的上述特性,將其應(yīng)用于滾動(dòng)軸承全壽命周期數(shù)據(jù)分析,探討了頻帶熵指標(biāo)在性能退化各階段的表現(xiàn)。介紹了為上述理論提供數(shù)據(jù)支撐的滾動(dòng)軸承故障試驗(yàn)和加速疲勞壽命試驗(yàn),通過(guò)對(duì)試驗(yàn)數(shù)據(jù)的分析,表明頻帶熵可作為狀態(tài)監(jiān)測(cè)指標(biāo)的有效補(bǔ)充。 (4)針對(duì)共振解調(diào)帶通濾波中心頻率難以確定的問(wèn)題,提出了頻帶熵確定中心頻率的方法。對(duì)基于STFT的頻帶熵,討論了頻率離散點(diǎn)數(shù)、時(shí)頻分析窗長(zhǎng)、窗函數(shù)類(lèi)型等參數(shù)對(duì)頻帶熵的影響;對(duì)基于小波包變換的頻帶熵,討論了小波包分解層數(shù)和小波包函數(shù)的選擇對(duì)頻帶熵的影響。最后將兩種方法應(yīng)用于仿真和實(shí)際的滾動(dòng)軸承故障診斷。分析結(jié)果證明,頻帶熵能夠準(zhǔn)確確定信號(hào)的共振頻帶,提升帶通濾波和包絡(luò)解調(diào)后的診斷效果。 (5)提出頻帶熵與遺傳算法相結(jié)合的方法,用于共振解調(diào)帶通濾波器的優(yōu)化設(shè)計(jì)。以頻帶熵最小為遺傳算法的優(yōu)化目標(biāo),通過(guò)選擇、交叉、變異等操作,在取值范圍內(nèi)尋找最優(yōu)的中心頻率和帶寬組合,設(shè)計(jì)優(yōu)化濾波器。通過(guò)對(duì)仿真信號(hào)和不同信噪比實(shí)驗(yàn)數(shù)據(jù)的分析,證明此方法能夠有效確定濾波中心頻率和帶寬,從而提高信號(hào)的信噪比,實(shí)現(xiàn)對(duì)軸承故障的診斷。
[Abstract]:Along with the rapid development of science and technology and the rapid development of modern industry , the machinery and equipment continue to develop in the direction of large , complex , high speed , high efficiency and heavy load . At the same time , its work and operating environment are more complex and severe . Once the equipment suddenly fails , it will not only increase the maintenance cost of the enterprise , reduce the production efficiency of the enterprise , but also can cause great economic loss , even lead to serious casualties and bad social impact . Therefore , how to carry on the effective state monitoring and fault diagnosis of the equipment is a problem that needs to be solved urgently . How to efficiently extract the characteristics reflecting the running state of the equipment and accurately judge the fault category has been a hot topic in the field of fault diagnosis . The new method and the new theory have been studied in this paper . The application of the new method and the new theory has been studied in this paper . The application of the new method and the new theory in fault diagnosis has been studied . The purpose of this paper is to provide a new index for the monitoring of rolling bearing status . ( 1 ) From the angle of theoretical analysis and engineering application , the background and significance of the thesis are described . The research status of fault diagnosis method , fault diagnosis , time - frequency analysis and information entropy theory are analyzed , and the research contents of this paper are established . ( 2 ) This paper introduces several time - frequency analysis methods and information entropy theory , which is the base of this paper . The concept of frequency band entropy is proposed by using the method of spectrum . The frequency band entropy is defined as the complexity of the signal in a certain frequency ( in - band ) signal , or the uncertainty is given . The basic algorithm of frequency band entropy is given . Finally , the concept of frequency band entropy is expanded from the angle of filtering . ( 3 ) The vibration mechanism and fault characteristics of rolling bearing are introduced . The possibility of using frequency band entropy index to monitor the state of rolling bearing is discussed , and its robustness is studied . Based on the above mentioned characteristics of frequency band entropy , the performance of the frequency band entropy index in all stages of performance degradation is discussed . The fault test and accelerated fatigue life test of the rolling bearing supported by the theory are introduced . The analysis of the test data shows that the frequency band entropy can be used as the effective complement of the state monitoring index . ( 4 ) Based on the frequency band entropy of STFT , the influence of frequency discrete point number , time frequency analysis window length and window function type on frequency band entropy is discussed . The frequency band entropy based on STFT is discussed . ( 5 ) The method of combining the frequency band entropy with the genetic algorithm is proposed for the optimization design of the resonant demodulation band - pass filter . The optimal filter is designed by selecting , crossing , mutation and so on . The optimal filter is designed by selecting , crossing , mutation and so on . By analyzing the experimental data of the simulation signal and the different signal - to - noise ratio , this method can effectively determine the center frequency and bandwidth of the filter , thereby improving the signal - to - noise ratio of the signal and realizing the diagnosis of the bearing fault .

【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TH165.3

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