本文選題：隨機鄰域嵌入 + Manhattan距離　； 參考：《東南大學(xué)》2015年碩士論文

【摘要】：特征提取是機械故障診斷的基礎(chǔ),如何有效地獲取故障特征信息是故障診斷領(lǐng)域的研究重點和熱點。隨著數(shù)據(jù)挖掘技術(shù)的不斷發(fā)展,數(shù)據(jù)降維算法被引入到故障診斷領(lǐng)域用于信號的特征提取。本文針對故障診斷過程故障數(shù)據(jù)高維數(shù)、非線性化、復(fù)雜性等特點,研究基于隨機鄰域嵌入的機械故障特征提取方法,相關(guān)工作如下：針對歐氏距離在高維數(shù)據(jù)空間中不能提供較大的相對距離差,無法明顯體現(xiàn)高維數(shù)據(jù)對象之間差異性的問題,提出一種基于Manhattan距離的隨機鄰域嵌入(Manhattan-SNE)算法。采用Manhattan距離衡量高維數(shù)據(jù)對象之間的相異度,得到高維空間和低維空間數(shù)據(jù)對象之間相似度的條件概率。UCI數(shù)據(jù)集和仿真故障信號分類識別驗證所提改進算法的有效性。針對隨機鄰域嵌入算法無法利用現(xiàn)實數(shù)據(jù)中少量樣本標(biāo)記信息的問題,提出一種基于拉普拉斯正則化度量學(xué)習(xí)的半監(jiān)督隨機鄰域嵌入(semi-supervised SNE, ss-SNE)算法。采用拉普拉斯正則化度量學(xué)習(xí)對距離矩陣進行半監(jiān)督學(xué)習(xí),利用已標(biāo)記數(shù)據(jù)提供的信息,重新刻畫數(shù)據(jù)點之間的距離,從而實現(xiàn)SNE算法的半監(jiān)督改進。與其他半監(jiān)督降維算法的對比分析表明所提改進算法的優(yōu)越性。隨機鄰域嵌入算法是一種批處理方法,無法獲取高維空間到低維嵌入空間的映射函數(shù),由此導(dǎo)致該算法無法對新增數(shù)據(jù)進行增量式處理。本文構(gòu)造一種隨機鄰域嵌入算法的增量形式(增量SNE算法),尋找新增樣本點的K近鄰,使得新增樣本的K近鄰的分布形式和K近鄰對應(yīng)的低維映射的分布形式盡可能匹配,實現(xiàn)新增樣本的學(xué)習(xí)。最后,將上述方法應(yīng)用于實際齒輪箱故障診斷,結(jié)果表明上述方法能夠有效提高故障診斷精度,驗證了算法在實際故障診斷應(yīng)用中的可行性。
[Abstract]:Feature extraction is the basis of mechanical fault diagnosis. How to obtain fault feature information effectively is the focus and hotspot in fault diagnosis field. With the development of data mining technology, data dimensionality reduction algorithm is introduced to fault diagnosis for feature extraction. Aiming at the characteristics of high dimension, nonlinearity and complexity of fault data in fault diagnosis process, this paper studies a method of mechanical fault feature extraction based on random neighborhood embedding. The related work is as follows: aiming at the problem that Euclidean distance can not provide large relative distance difference in high-dimensional data space and can not manifest the difference between high-dimensional data objects, a Manhattan distance-based random neighborhood embedding algorithm is proposed. Manhattan distance is used to measure the dissimilarity between high-dimensional data objects and low-dimensional spatial data objects. The conditional probability of similarity between high-dimensional and low-dimensional spatial data objects. UCI dataset and simulation fault signal classification are used to verify the effectiveness of the proposed improved algorithm. Aiming at the problem that the random neighborhood embedding algorithm can not make use of a small number of samples in real data, a semi-supervised random neighborhood embedding (semi-supervised SNE-SNE) algorithm based on Laplace regularization metric learning is proposed. The distance matrix is semi-supervised by Laplace regularization metric learning, and the distance between data points is redescribed by using the information provided by marked data, so that the semi-supervised improvement of SNE algorithm is realized. The comparison with other semi-supervised dimensionality reduction algorithms shows the superiority of the improved algorithm. The random neighborhood embedding algorithm is a batch processing method, which can not obtain the mapping function from high-dimensional space to low-dimensional embedded space, which leads to the algorithm being unable to process the new data incrementally. In this paper, we construct an incremental form of random neighborhood embedding algorithm (incremental SNE algorithm) to find the K-nearest neighbor of the new sample point, so that the distribution form of the K-nearest neighbor of the new sample and the distribution form of the low-dimensional mapping corresponding to the K-nearest neighbor are matched as much as possible. The learning of new samples is realized. Finally, the above method is applied to the actual gearbox fault diagnosis. The results show that the above method can effectively improve the accuracy of fault diagnosis, and verify the feasibility of the algorithm in practical fault diagnosis.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TH17;TP277

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本文編號：2104543