發(fā)布時間：2018-03-01 15:42

  本文關鍵詞： 基于變量預測模型的模式識別 LMD能量矩 多尺度高階奇異譜分析 模型融合 特征選擇 新異類檢測 旋轉(zhuǎn)機械故障診斷　出處：《湖南大學》2015年博士論文　論文類型：學位論文

【摘要】：隨著科學技術的發(fā)展,故障診斷技術逐漸成為了保障旋轉(zhuǎn)機械設備安全可靠運行的核心支持技術之一。對旋轉(zhuǎn)機械故障診斷新技術、新方法的研究具有重要的理論和實際意義。旋轉(zhuǎn)機械故障診斷技術的實質(zhì)是模式識別的問題。模式識別方法的選擇與運用對提高故障診斷的精度和穩(wěn)定性具有十分重要的作用。在旋轉(zhuǎn)機械故障診斷領域,廣泛使用的模式識別方法有神經(jīng)網(wǎng)絡、支持向量機等,但這些方法都存在著各自的局限性,且沒能充分利用特征變量之間的相互內(nèi)在關系。實際上,通過現(xiàn)代信號處理方法提取的特征值之間往往存在一定的相互內(nèi)在關系,不同的系統(tǒng)或者同一系統(tǒng)不同的狀態(tài),相互內(nèi)在關系的數(shù)學表達式存在明顯差異。基于變量預測模型模式識別(Variable Predictive Model Based Class Discriminate,VPMCD)方法是一種新的模式識別方法。VPMCD方法能充分利用各個特征值之間的相互內(nèi)在關系建立變量預測模型(Variable Predictive Model,VPM)的數(shù)學表達式,從而進行分類識別。為了將VPMCD方法應用于小樣本多分類的旋轉(zhuǎn)機械故障診斷,本文在國家自然科學基金項目的資助下(編號:51175158),對VPMCD方法的關鍵理論及其在小樣本多分類的旋轉(zhuǎn)機械故障診斷中的應用進行了深入而系統(tǒng)地研究。本文主要的研究內(nèi)容和創(chuàng)新點如下:(1)研究了VPMCD方法的基本原理和具體算法,總結了VPMCD方法的特點,將VPMCD方法與神經(jīng)網(wǎng)絡、支持向量機等方法進行了對比研究,分析結果表明:VPMCD方法在分類性能、運算速度等諸多方面具有明顯的優(yōu)勢。(2)針對原VPMCD方法中模型參數(shù)估計方法存在的不足,提出了采用加權最小二乘參數(shù)估計來代替最小二乘參數(shù)估計,從而改進VPMCD方法,仿真分析結果證表明,改進后的VPMCD方法在更少的訓練樣本下,可以取得了更高的模型擬合精度。(3)針對具體的旋轉(zhuǎn)機械故障診斷問題,結合最新的現(xiàn)代信號處理技術,提出了多種特征提取方法:LMD(Local Mean Decomposition,LMD)能量矩的特征提取方法,改進ITD(Intrinsic Time-scale Decomposition,ITD)特征提取方法,LCD(Local Characteristic-scale Decomposition,LCD)和模糊熵相結合的特征提取方法,LCD和SVD(Singular Value Decomposition,SVD)相結合的LCD-SVD特征提取方法,以及多尺度高階奇異譜特征提取方法。結合以上特征提取方法,提出了各種基于VPMCD的故障診斷模型,并通過應用實例驗證了論文提出的各種模型均能有效性地應用于旋轉(zhuǎn)機械故障診斷領域。(4)針對原VPMCD方法的模型選擇單一、信息利用不充分的問題,結合遺傳算法(Genetic algorithm,GA),提出了GA-VPMCD分類識別方法。首先采用回代(Re-substitution,RS)驗證或者交叉驗證方法,結合模型檢驗,選取驗證精度最高,且模型擬合優(yōu)度最高的模型作為弱VPM;然后,采用模型融合的思想,利用遺傳算法融合各個弱VPM的預測值得到最佳預測值;最后,依據(jù)誤差平方和最小來實現(xiàn)分類識別。結合階次包絡分析技術,將GA-VPMCD方法應用于變速滾動軸承故障診斷;結合多尺度高階奇異譜分析,將GA-VPMCD方法應用于轉(zhuǎn)子故障診斷;實驗結果表明,GA-VPMCD方法有效提高了故障診斷精度和穩(wěn)定性。(5)針對特征選擇問題,將VPMCD方法與ANN、平均影響值(Mean Impact Value,MIV)相結合,提出了ANN-MIV-VPMCD分類識別方法,并進一步提出了基于LCD-SVD和ANN-MIV-VPMCD的滾動軸承故障診斷模型。實驗結果驗證了ANN-MIV-VPMCD方法的有效性和優(yōu)越性。(6)在多數(shù)情況下,旋轉(zhuǎn)機械故障診斷面臨只有正常樣本,或者故障模式不完備、典型故障樣本缺乏。針對這個問題,提出了OC-VPMCD新異類檢測方法,并應用于旋轉(zhuǎn)機械新異類檢測。實驗分析結果表明,OC-VPMCD方法能有效地應用于旋轉(zhuǎn)機械新異類檢測。
[Abstract]:With the development of science and technology, fault diagnosis technology has gradually become the core guarantee safe and reliable operation of rotating machinery is one of the support technology. The new technology of the fault diagnosis of rotating machinery, has important theoretical and practical significance to study the new method. The essence of technology of rotating machinery fault diagnosis is the problem of pattern recognition and pattern recognition. The choice is very important to improve the fault diagnosis accuracy and stability. In the field of rotating machinery fault diagnosis, pattern recognition methods are widely used neural network, support vector machine and so on, but these methods have their own limitations, and can not make full use of the mutual relationship between the variables. In fact, there are often some inherent relation between the values through the modern signal processing feature extraction method, different systems or different systems of the same There are obvious differences between the state, the mathematical expression of internal relations. Pattern recognition based on variable prediction model (Variable Predictive Model Based Class Discriminate, VPMCD) is a new pattern recognition method.VPMCD method can make full use of each feature value between the intrinsic relationship between variables to establish prediction model (Variable Predictive Model, VPM) mathematical expressions thus, the classification and recognition. In order to fault diagnosis of rotating machinery VPMCD method applied in small sample classification, based on the National Natural Science Foundation of China (No. 51175158), the application of rotating machinery fault diagnosis key theory of VPMCD method and its classification in the small sample in depth and systematically the research. The main research content and innovation are as follows: (1) the basic principle of VPMCD method and specific algorithm, summed up the VPMC The characteristics of the D method, VPMCD method and neural network, support vector machine method are studied. The analysis results show that the classification performance of the VPMCD method, has obvious advantages in computing speed and other aspects. (2) aiming at the shortage of the original VPMCD method in the estimation of model parameters in the proposed method, using weighted least squares parameter estimation instead of least squares parameter estimation, improved VPMCD method, the simulation results show that the VPMCD card, the improved method in less training samples, the model can achieve higher precision. (3) according to the fault diagnosis of rotating machinery in detail, combined with modern signal processing technology, is put forward extraction of various features: LMD (Local Mean Decomposition, LMD) feature extraction method of energy moment, improved ITD (Intrinsic Time-scale Decomposition ITD (LCD) feature extraction method, Local Chara Cteristic-scale Decomposition, LCD) feature extraction method and combining fuzzy entropy, LCD and SVD (Singular Value Decomposition, SVD) LCD-SVD feature extraction method combining, and multi-scale singular spectra feature extraction method. The extraction method and combining with the above characteristics, put forward various fault diagnosis model based on VPMCD, and through the application instance the validation of the models proposed in this paper can be effectively applied in the field of fault diagnosis of rotating machinery. (4) according to the original VPMCD model to choose a single, the problem of insufficient information utilization, combined with genetic algorithm (Genetic algorithm GA), proposed a GA-VPMCD classification method. Firstly, using back substitution (Re-substitution, RS) validation or cross validation method, combined with the model test, select the highest accuracy and model validation, the highest goodness of fit as a weak VPM; then, using the model of integration of thinking To predict, using genetic algorithm to fuse the various weak VPM is the best predictor; finally, based on the minimum error sum of squares to achieve classification and recognition. Combining the order envelope analysis technique, the GA-VPMCD method is applied to fault diagnosis of rolling bearing transmission; combined with multiscale high-order singular spectrum analysis, the GA-VPMCD method was applied to the fault diagnosis of rotor GA-VPMCD; experimental results show that the method improves the fault diagnosis accuracy and stability. (5) according to the feature selection problem, and the ANN VPMCD method, the average value of influence (Mean Impact Value, MIV) combined with the proposed ANN-MIV-VPMCD classification method, and further puts forward the rolling bearing fault diagnosis model based on ANN-MIV-VPMCD and LCD-SVD. The experimental results verify the validity and superiority of the ANN-MIV-VPMCD method (6). In most cases, the fault diagnosis of rotating machinery facing only normal or fault samples. Incomplete models and lack of typical fault samples. Aiming at this problem, a new OC-VPMCD heterogeneous detection method is proposed and applied to the new heterogeneous detection of rotating machinery. Experimental results show that OC-VPMCD method can be applied to the new heterogeneous detection of rotating machinery effectively.

【學位授予單位】：湖南大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TH17

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1 張宇波,徐昊,姚偉亮;聲音模式識別過程及方法研究[J];長春理工大學學報;2004年03期

2 洪文學;;基于多元圖圖形幾何特征的模式識別新方法[J];燕山大學學報;2008年05期

3 張濤;洪文學;景軍;趙勇;;模式識別中的表示問題[J];燕山大學學報;2008年05期

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5 余洪祖 ,李楚霖 ,吳學謀;乏晰模式識別的二元對比平均法[J];華中工學院學報;1980年S2期

6 沈永歡 ,呂梯華 ,陳祖蔭 ,，

本文編號：1552584