本文關鍵詞： 走行部故障監(jiān)測數(shù)據(jù) 局部特征尺度分解 集合局部特征尺度分解 信息熵 流形學習　出處：《西南交通大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著計算機技術與人工智能的發(fā)展,特征提取作為完整模式識別系統(tǒng)中的重要環(huán)節(jié),已引起越來越多的重視。在面對高復雜度、非平穩(wěn)信號時,選擇合適的特征識別方法是能否挖掘有效信息的關鍵。由于提取到的高維特征空間中存在著相關性和冗余信息,利用流形學習這種非線性降維的機器學習方法可以達到維數(shù)簡約的目的,挖掘原始數(shù)據(jù)的內(nèi)在本質(zhì),實現(xiàn)數(shù)據(jù)的可視化。本文以列車走行部故障診斷與雷達輻射源信號識別為背景,探討特征提取與降維在基于監(jiān)測數(shù)據(jù)的信號處理領域的應用,并開展了以下研究工作:1.論文采用局部特征尺度分解(LCD)與信息熵結合的特征提取方法,以軸承故障標準數(shù)據(jù)集為研究對象,對信號數(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LCD分解方法的不足,提出一種利用噪聲輔助改進的集合局部特征尺度分解(ELCD)方法,仿真驗證改進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,采用流形學習的LLTSA算法對原始高維向量進行降維,然后運用Fisher比率分別對LLTSA降維前后的特征進行評價。實驗結果表明:降維后特征對分類的貢獻率更大,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相結合的特征分析方法,橫向減振器部分故障工況識別率更高。3.針對不同雷達輻射源由于調(diào)制方式不同和噪聲影響引起瞬時頻率變化中統(tǒng)計參數(shù)的差異,開展了基于ELCD和流形學習的雷達信號識別方法研究。首先,對輻射源信號做多重相位差分法求時頻曲線,并制定調(diào)制識別的層次決策分類器模型識別信號的調(diào)制類型;然后,對雷達信號進行ELCD分解并提取Renyi熵,將調(diào)制識別結果、Renyi熵和PDW參數(shù)組成特征向量;最后,采用S-ISOMAP降維處理,對降維后的特征采用SVM分類識別。實驗表明:1)所提取的特征能有效描述不同信號的脈內(nèi)調(diào)變規(guī)律,總體分類率正確率為98.8%;2)運用S-ISOMAP算法對原始特征向量降維,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.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.4

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