本文選題：局部均值分解 + 端點效應�。� 參考：《中北大學》2017年碩士論文

【摘要】：針對大型設備逐漸趨于復雜化、一體化、智能化,錯綜復雜的設備之間的關聯(lián)與耦合作用愈來愈強,極大影響了設備運行狀態(tài)監(jiān)測與故障診斷的有效性,繼而給機械故障診斷領域帶來了巨大挑戰(zhàn)。論文以滾動軸承故障特征提取與智能診斷系統(tǒng)研究為主要研究內(nèi)容,將局部均值分解(Local Means Decomposition,LMD)作為核心技術,結(jié)合非線性動力學理論與人工智能分類技術對上述背景下的滾動軸承故障診斷系統(tǒng)展開研究。針對局部均值分解存在端點效應與模態(tài)混疊現(xiàn)象,論文對LMD算法上稍作改進,首先采用基于局部波形積分匹配方法來抑制端點效應,該方法通過三點積分曲線法在信號內(nèi)部搜索最佳匹配波形,在局部信號端點處采用擴展波形抑制端點效應。針對LMD存在模態(tài)混疊問題,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中頻率成分最小的信號,再向原始信號中加入等幅值的高斯白噪聲,對混合信號進行反復LMD分解,將得到的分量瞬時頻域與信號最小截止頻率對比,以此作為分量迭代終止條件。通過仿真與實驗數(shù)據(jù)分析,驗證所提方法不僅能夠改善LMD在端點效應與模態(tài)混疊的問題,對于低頻偽分量的抑制也有較好的效果。為了簡化故障診斷流程,論文在改進的LMD算法的基礎上,采用模糊熵對故障特征進行量化處理,從多個角度對原始信號進行深度剖析,提取全面表征故障特征的特征向量,結(jié)合具有極強的非線性分類能力的概率神經(jīng)網(wǎng)絡(probabilistic neural network,PNN)實現(xiàn)故障模式識別。最后論文在對LMD自時頻信號分析處理方法研究的基礎上,利用人機交互能力強的LabView與超強運算分析能力的Matlab進行混合編程,研究開發(fā)一套具有高效、準確的滾動軸承智能診斷系統(tǒng),搭建一套集在線數(shù)據(jù)采集、數(shù)據(jù)分析與故障診斷于一體的滾動軸承故障診斷。
[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.
【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH133.33

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