本文關(guān)鍵詞： 走行部故障監(jiān)測(cè)數(shù)據(jù) 局部特征尺度分解 集合局部特征尺度分解 信息熵 流形學(xué)習(xí)　出處：《西南交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著計(jì)算機(jī)技術(shù)與人工智能的發(fā)展,特征提取作為完整模式識(shí)別系統(tǒng)中的重要環(huán)節(jié),已引起越來(lái)越多的重視。在面對(duì)高復(fù)雜度、非平穩(wěn)信號(hào)時(shí),選擇合適的特征識(shí)別方法是能否挖掘有效信息的關(guān)鍵。由于提取到的高維特征空間中存在著相關(guān)性和冗余信息,利用流形學(xué)習(xí)這種非線性降維的機(jī)器學(xué)習(xí)方法可以達(dá)到維數(shù)簡(jiǎn)約的目的,挖掘原始數(shù)據(jù)的內(nèi)在本質(zhì),實(shí)現(xiàn)數(shù)據(jù)的可視化。本文以列車走行部故障診斷與雷達(dá)輻射源信號(hào)識(shí)別為背景,探討特征提取與降維在基于監(jiān)測(cè)數(shù)據(jù)的信號(hào)處理領(lǐng)域的應(yīng)用,并開(kāi)展了以下研究工作:1.論文采用局部特征尺度分解(LCD)與信息熵結(jié)合的特征提取方法,以軸承故障標(biāo)準(zhǔn)數(shù)據(jù)集為研究對(duì)象,對(duì)信號(hào)數(shù)據(jù)做LCD分解并提取ISC分量的多種信息熵特征組成故障特征向量,仿真驗(yàn)證了 LCD信息熵特征在故障特征提取分析的有效性和可行性。2.針對(duì)LCD分解方法的不足,提出一種利用噪聲輔助改進(jìn)的集合局部特征尺度分解(ELCD)方法,仿真驗(yàn)證改進(jìn)的算法能有效抑制模態(tài)混疊現(xiàn)象并具有高效的算法效率。針對(duì)走行部橫向減振器部分失效工況數(shù)據(jù),提取ELCD分解多種信息熵組成特征向量。由于原始特征向量包含有大量的冗余信息,采用流形學(xué)習(xí)的LLTSA算法對(duì)原始高維向量進(jìn)行降維,然后運(yùn)用Fisher比率分別對(duì)LLTSA降維前后的特征進(jìn)行評(píng)價(jià)。實(shí)驗(yàn)結(jié)果表明:降維后特征對(duì)分類的貢獻(xiàn)率更大,即LLTSA降維算法能夠在最大程度保留本質(zhì)特征,采用ELCD和LLTSA相結(jié)合的特征分析方法,橫向減振器部分故障工況識(shí)別率更高。3.針對(duì)不同雷達(dá)輻射源由于調(diào)制方式不同和噪聲影響引起瞬時(shí)頻率變化中統(tǒng)計(jì)參數(shù)的差異,開(kāi)展了基于ELCD和流形學(xué)習(xí)的雷達(dá)信號(hào)識(shí)別方法研究。首先,對(duì)輻射源信號(hào)做多重相位差分法求時(shí)頻曲線,并制定調(diào)制識(shí)別的層次決策分類器模型識(shí)別信號(hào)的調(diào)制類型;然后,對(duì)雷達(dá)信號(hào)進(jìn)行ELCD分解并提取Renyi熵,將調(diào)制識(shí)別結(jié)果、Renyi熵和PDW參數(shù)組成特征向量;最后,采用S-ISOMAP降維處理,對(duì)降維后的特征采用SVM分類識(shí)別。實(shí)驗(yàn)表明:1)所提取的特征能有效描述不同信號(hào)的脈內(nèi)調(diào)變規(guī)律,總體分類率正確率為98.8%;2)運(yùn)用S-ISOMAP算法對(duì)原始特征向量降維,所得低維特征的分類效果更好。
[Abstract]:With the development of computer technology and artificial intelligence, feature extraction, as an important part of the complete pattern recognition system, has attracted more and more attention in the face of high complexity and non-stationary signals. Choosing the appropriate feature recognition method is the key to mining the effective information because of the correlation and redundancy in the extracted feature space. The machine learning method of nonlinear dimensionality reduction by manifold learning can achieve the goal of dimensionality reduction and mine the intrinsic essence of the original data. Based on the background of train line fault diagnosis and radar emitter signal recognition, this paper discusses the application of feature extraction and dimension reduction in the field of signal processing based on monitoring data. The following research work is carried out: 1. The paper adopts the feature extraction method which combines local feature scale decomposition (LCD) with information entropy, and takes the standard data set of bearing faults as the research object. The signal data is decomposed by LCD and a variety of information entropy features of the ISC component are extracted to form the fault feature vector. Simulation results verify the effectiveness and feasibility of LCD information entropy feature in fault feature extraction and analysis. 2. Aiming at the shortage of LCD decomposition method. A new method of local feature scale decomposition (ELCD) based on noise assisted improvement is proposed in this paper. Simulation results show that the improved algorithm can effectively suppress modal aliasing and has high efficiency. ELCD decomposes a variety of information entropy to form a feature vector. Because the original feature vector contains a lot of redundant information, the LLTSA algorithm of manifold learning is used to reduce the dimension of the original high-dimensional vector. Then the Fisher ratio is used to evaluate the characteristics of LLTSA before and after dimensionality reduction. The experimental results show that the contribution rate of dimensionally reduced features to classification is greater. That is, the LLTSA dimensionality reduction algorithm can retain the essential features to the maximum extent, and adopts the feature analysis method which combines ELCD and LLTSA. For different radar emitter due to different modulation mode and noise influence, the statistical parameters of instantaneous frequency change are different. 3. The radar signal recognition method based on ELCD and manifold learning is studied. Firstly, the time-frequency curve of emitter signal is obtained by multi-phase difference method. A hierarchical decision classifier model for modulation recognition is developed to identify the modulation types of signals. Then, the radar signal is decomposed by ELCD and the Renyi entropy is extracted, and the modulation recognition result is composed of the Renyi entropy and the PDW parameter. Finally, S-ISOMAP is used to reduce the dimension, and the feature after dimension reduction is identified by SVM classification. The experiment shows that the extracted feature can effectively describe the pulse modulation law of different signals. The total classification rate is 98.8%. 2) using S-ISOMAP algorithm to reduce the dimension of the original feature vector, the classification effect of the low dimensional feature is better.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.4

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 曾憲華;羅四維;;局部保持的流形學(xué)習(xí)算法對(duì)比研究[J];計(jì)算機(jī)工程與應(yīng)用;2008年29期

2 劉志勇;;基于保距與保拓?fù)涞牧餍螌W(xué)習(xí)算法[J];長(zhǎng)江大學(xué)學(xué)報(bào)(自然科學(xué)版)理工卷;2010年02期

3 閆志敏;劉希玉;;流形學(xué)習(xí)及其算法研究[J];計(jì)算機(jī)技術(shù)與發(fā)展;2011年05期

4 楊海紅;;流形學(xué)習(xí)中鄰域大小的選擇算法[J];山西煤炭管理干部學(xué)院學(xué)報(bào);2011年01期

5 周華;蔡超;丁明躍;;基于流形學(xué)習(xí)和流形高階近似的圖像距離度量[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年03期

6 石陸魁;張軍;宮曉騰;;基于鄰域保持的流形學(xué)習(xí)算法評(píng)價(jià)模型[J];計(jì)算機(jī)應(yīng)用;2012年09期

7 談超;關(guān)佶紅;周水庚;;增量與演化流形學(xué)習(xí)綜述[J];智能系統(tǒng)學(xué)報(bào);2012年05期

8 徐蓉;姜峰;姚鴻勛;;流形學(xué)習(xí)概述[J];智能系統(tǒng)學(xué)報(bào);2006年01期

9 羅四維;趙連偉;;基于譜圖理論的流形學(xué)習(xí)算法[J];計(jì)算機(jī)研究與發(fā)展;2006年07期

10 周紅;吳煒;滕奇志;楊曉敏;李e，

本文編號(hào)：1444058