本文選題：局部均值分解 + 端點(diǎn)效應(yīng)　； 參考：《中北大學(xué)》2017年碩士論文

【摘要】：針對(duì)大型設(shè)備逐漸趨于復(fù)雜化、一體化、智能化,錯(cuò)綜復(fù)雜的設(shè)備之間的關(guān)聯(lián)與耦合作用愈來(lái)愈強(qiáng),極大影響了設(shè)備運(yùn)行狀態(tài)監(jiān)測(cè)與故障診斷的有效性,繼而給機(jī)械故障診斷領(lǐng)域帶來(lái)了巨大挑戰(zhàn)。論文以滾動(dòng)軸承故障特征提取與智能診斷系統(tǒng)研究為主要研究?jī)?nèi)容,將局部均值分解(Local Means Decomposition,LMD)作為核心技術(shù),結(jié)合非線(xiàn)性動(dòng)力學(xué)理論與人工智能分類(lèi)技術(shù)對(duì)上述背景下的滾動(dòng)軸承故障診斷系統(tǒng)展開(kāi)研究。針對(duì)局部均值分解存在端點(diǎn)效應(yīng)與模態(tài)混疊現(xiàn)象,論文對(duì)LMD算法上稍作改進(jìn),首先采用基于局部波形積分匹配方法來(lái)抑制端點(diǎn)效應(yīng),該方法通過(guò)三點(diǎn)積分曲線(xiàn)法在信號(hào)內(nèi)部搜索最佳匹配波形,在局部信號(hào)端點(diǎn)處采用擴(kuò)展波形抑制端點(diǎn)效應(yīng)。針對(duì)LMD存在模態(tài)混疊問(wèn)題,提出基于總體均值分解與頻率截止方法抑制模態(tài)混疊,該方法采用功率譜分析求得原始信號(hào)中頻率成分最小的信號(hào),再向原始信號(hào)中加入等幅值的高斯白噪聲,對(duì)混合信號(hào)進(jìn)行反復(fù)LMD分解,將得到的分量瞬時(shí)頻域與信號(hào)最小截止頻率對(duì)比,以此作為分量迭代終止條件。通過(guò)仿真與實(shí)驗(yàn)數(shù)據(jù)分析,驗(yàn)證所提方法不僅能夠改善LMD在端點(diǎn)效應(yīng)與模態(tài)混疊的問(wèn)題,對(duì)于低頻偽分量的抑制也有較好的效果。為了簡(jiǎn)化故障診斷流程,論文在改進(jìn)的LMD算法的基礎(chǔ)上,采用模糊熵對(duì)故障特征進(jìn)行量化處理,從多個(gè)角度對(duì)原始信號(hào)進(jìn)行深度剖析,提取全面表征故障特征的特征向量,結(jié)合具有極強(qiáng)的非線(xiàn)性分類(lèi)能力的概率神經(jīng)網(wǎng)絡(luò)(probabilistic neural network,PNN)實(shí)現(xiàn)故障模式識(shí)別。最后論文在對(duì)LMD自時(shí)頻信號(hào)分析處理方法研究的基礎(chǔ)上,利用人機(jī)交互能力強(qiáng)的LabView與超強(qiáng)運(yùn)算分析能力的Matlab進(jìn)行混合編程,研究開(kāi)發(fā)一套具有高效、準(zhǔn)確的滾動(dòng)軸承智能診斷系統(tǒng),搭建一套集在線(xiàn)數(shù)據(jù)采集、數(shù)據(jù)分析與故障診斷于一體的滾動(dòng)軸承故障診斷。
[Abstract]:In view of the complex, integrated, intelligent and complicated equipment, the relationship and coupling between the equipments is becoming stronger and stronger, which greatly affects the effectiveness of the monitoring and fault diagnosis of the equipment operating state. Then it brings great challenge to the field of mechanical fault diagnosis. In this paper, the fault feature extraction and intelligent diagnosis system of rolling bearing are the main research contents, and the local mean decomposition (LMD) is taken as the core technology. Combined with nonlinear dynamics theory and artificial intelligence classification technology, the rolling bearing fault diagnosis system under the above background is studied. Aiming at the existence of endpoint effect and modal aliasing in local mean decomposition, the LMD algorithm is improved in this paper. Firstly, the local waveform integral matching method is used to suppress the endpoint effect. In this method, the best matching waveform is searched inside the signal by three-point integral curve method, and the extended waveform is used to suppress the endpoint effect at the end point of the local signal. Aiming at the problem of modal aliasing in LMD, a method based on population mean decomposition and frequency cutoff is proposed to suppress modal aliasing. The power spectrum analysis is used to obtain the signal with the smallest frequency component in the original signal. The Gao Si white noise with equal amplitude is added to the original signal, and the mixed signal is decomposed repeatedly. The instantaneous frequency domain of the component is compared with the minimum cut-off frequency of the signal, which is used as the iterative termination condition of the component. The simulation and experimental data analysis show that the proposed method can not only improve the end-point effect and modal aliasing of LMD, but also have a good effect on the suppression of low-frequency pseudo-components. In order to simplify the process of fault diagnosis, based on the improved LMD algorithm, the fuzzy entropy is used to quantify the fault features, and the original signals are analyzed in depth from many angles, and the feature vectors representing the fault features are extracted. Fault pattern recognition is realized with probabilistic neural network (probabilistic neural) which has strong nonlinear classification ability. Finally, on the basis of the research on the analysis and processing method of LMD self-time-frequency signal, a set of high efficiency is developed by using LabView, which has strong human-computer interaction ability, and Matlab, which has super ability of operation and analysis. An accurate intelligent diagnosis system for rolling bearings is established. A set of on-line data acquisition, data analysis and fault diagnosis are built for fault diagnosis of rolling bearings.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TH133.33

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