基于深度學(xué)習(xí)和分類集成的高速列車工況識別研究
發(fā)布時間：2018-10-08 11:53
【摘要】：中國高速鐵路快速發(fā)展,目前已成為世界高速鐵路的引領(lǐng)者。然而高速列車長時間的高速運行,使得列車走行部性能下降,這為列車的安全運行帶來了巨大的隱患。走行部是高速列車的關(guān)鍵組成部分,對保障列車的安全性和乘客的舒適度起到重要作用。通過在列車走行部上安裝傳感器,采集并分析反映其運行狀況的振動信號,是監(jiān)測列車運營狀態(tài)的主要技術(shù)之一。如何有效的從高速列車監(jiān)測數(shù)據(jù)中挖掘出有用的特征信息,并實現(xiàn)典型工況的有效識別具有重要的研究價值。列車振動信號是非平穩(wěn)、非線性信號,具有特征信息復(fù)雜、難辨識等特點。而傳統(tǒng)的工況識別方法存在特征提取不完備和識別性能不精確的問題。本文設(shè)計了一種多視圖特征提取方法,并首次引入分類集成技術(shù),提出了多視圖分類集成(Multi-view Classification Ensemble,MV-CE)的列車工況的識別方法。該方法首先提取FFT系數(shù)、小波能量、EEMD模糊熵,并對FFT系數(shù)進行Fisher比率特征選擇,從而得到列車振動信號三個視圖的特征。然后利用K最近鄰分類器和最小二乘支持向量機分別對三個視圖進行初步識別。最后通過分類熵投票策略集成多個分類器的輸出結(jié)果。通過實驗對比說明該方法可以提取出完備的特征,并驗證了具有多樣性集成模型的有效性。深度信念網(wǎng)絡(luò)(DeepBeliefNetwork,DBN)可以自動的學(xué)習(xí)原始數(shù)據(jù)的特征,為高速列車工況識別的研究開拓了新的思路。結(jié)合深度學(xué)習(xí)與分類集成技術(shù)的優(yōu)點,本文提出了一種DBN層次集成模型對高速列車工況進行識別。首先提取列車振動信號的FFT系數(shù)作為模型的可視層輸入。利用DBN自動學(xué)習(xí)信號的層次特征。然后利用每一層特征訓(xùn)練支持向量機、K最近鄰、RBF神經(jīng)網(wǎng)絡(luò)三種分類器。最后分別采用多數(shù)投票法、分類熵投票策略、勝者全取三種集成策略進行集成。實驗結(jié)果表明,該模型的識別效果高于10種對比方法,并且其性能受網(wǎng)絡(luò)層數(shù)和隱藏層單元數(shù)目變化的影響遠小于傳統(tǒng)DBN模型。列車走行部不同通道的振動信號既存在互補性又存在冗余性。為了充分利用多通道振動信號的互補信息,提出了基于相似度比率的通道篩選方法,并構(gòu)建了一種多通道深度信念網(wǎng)絡(luò)模型(Multi-channel Deep Belief Network,MDBN)進行多通道的工況識別。首先提取所有通道振動信號的FFT系數(shù)。然后,計算每個通道FFT特征的相似度比率,并選取相似度比率較大的若干通道。最后,構(gòu)建MDBN模型對所篩選的多通道數(shù)據(jù)進行特征學(xué)習(xí),利用MDBN的共聯(lián)層實現(xiàn)多通道特征的融合,并進行分類識別。實驗結(jié)果表明,MDBN的特征提取能力優(yōu)于DBN模型,并且MDBN的工況識別率高于DBN和DBN層次集成模型。
[Abstract]:With the rapid development of Chinese high-speed railway, it has become the leader of high-speed railway in the world. However, the high speed train running for a long time makes the train running performance decline, which brings a huge hidden trouble for the safe operation of the train. The running part is the key component of the high speed train and plays an important role in ensuring the safety of the train and the comfort of the passengers. It is one of the main techniques to monitor the train running state by installing the sensor on the train running part and collecting and analyzing the vibration signal which reflects the running condition of the train. How to effectively mine useful feature information from high-speed train monitoring data and realize effective recognition of typical working conditions has important research value. Train vibration signal is non-stationary and nonlinear. It has complex characteristic information and is difficult to identify. However, the traditional working condition recognition methods have the problems of incomplete feature extraction and inaccurate recognition performance. In this paper, a multi-view feature extraction method is designed, and the classification integration technique is introduced for the first time, and a multi-view classification integration (Multi-view Classification Ensemble,MV-CE) method for train operating condition identification is proposed. The method firstly extracts FFT coefficient, wavelet energy and EEMD fuzzy entropy, then selects the Fisher ratio feature of FFT coefficient, and obtains the characteristics of three views of train vibration signal. Then K nearest neighbor classifier and least square support vector machine are used to identify the three views. Finally, the output of multiple classifiers is integrated by the classification entropy voting strategy. The experimental results show that the proposed method can extract complete features and verify the effectiveness of the diversity integration model. Deep belief Network (DeepBeliefNetwork,DBN) can automatically learn the characteristics of the original data, which opens up a new idea for the study of high-speed train condition identification. Combined with the advantages of deep learning and classification integration technology, this paper presents a DBN hierarchical integration model to identify the operating conditions of high-speed trains. First, the FFT coefficient of train vibration signal is extracted as the visual layer input of the model. DBN is used to automatically learn the hierarchical features of signals. Then three kinds of classifiers of support vector machine (SVM) nearest neighbor RBF neural network are trained by each layer feature. At last, the majority voting method, the classified entropy voting strategy and the winner integration strategy are adopted respectively. The experimental results show that the recognition effect of this model is higher than that of 10 comparison methods, and its performance is much less affected by the number of network layers and the number of hidden layer units than the traditional DBN model. The vibration signals in different channels of train running are both complementary and redundant. In order to make full use of the complementary information of multi-channel vibration signals, a method of channel selection based on similarity ratio is proposed, and a multi-channel depth belief network model (Multi-channel Deep Belief Network,MDBN) is constructed to identify multi-channel operating conditions. First, the FFT coefficients of all channel vibration signals are extracted. Then, the similarity ratio of FFT features of each channel is calculated, and several channels with high similarity ratio are selected. Finally, the MDBN model is constructed to learn the features of the filtered multi-channel data, and the co-layer of MDBN is used to realize the fusion of multi-channel features, and the classification and recognition are carried out. The experimental results show that the feature extraction ability of MDBN is better than that of DBN model, and the recognition rate of MDBN is higher than that of DBN and DBN hierarchical integration model.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U270.7;TP391.41
本文編號：2256645
[Abstract]:With the rapid development of Chinese high-speed railway, it has become the leader of high-speed railway in the world. However, the high speed train running for a long time makes the train running performance decline, which brings a huge hidden trouble for the safe operation of the train. The running part is the key component of the high speed train and plays an important role in ensuring the safety of the train and the comfort of the passengers. It is one of the main techniques to monitor the train running state by installing the sensor on the train running part and collecting and analyzing the vibration signal which reflects the running condition of the train. How to effectively mine useful feature information from high-speed train monitoring data and realize effective recognition of typical working conditions has important research value. Train vibration signal is non-stationary and nonlinear. It has complex characteristic information and is difficult to identify. However, the traditional working condition recognition methods have the problems of incomplete feature extraction and inaccurate recognition performance. In this paper, a multi-view feature extraction method is designed, and the classification integration technique is introduced for the first time, and a multi-view classification integration (Multi-view Classification Ensemble,MV-CE) method for train operating condition identification is proposed. The method firstly extracts FFT coefficient, wavelet energy and EEMD fuzzy entropy, then selects the Fisher ratio feature of FFT coefficient, and obtains the characteristics of three views of train vibration signal. Then K nearest neighbor classifier and least square support vector machine are used to identify the three views. Finally, the output of multiple classifiers is integrated by the classification entropy voting strategy. The experimental results show that the proposed method can extract complete features and verify the effectiveness of the diversity integration model. Deep belief Network (DeepBeliefNetwork,DBN) can automatically learn the characteristics of the original data, which opens up a new idea for the study of high-speed train condition identification. Combined with the advantages of deep learning and classification integration technology, this paper presents a DBN hierarchical integration model to identify the operating conditions of high-speed trains. First, the FFT coefficient of train vibration signal is extracted as the visual layer input of the model. DBN is used to automatically learn the hierarchical features of signals. Then three kinds of classifiers of support vector machine (SVM) nearest neighbor RBF neural network are trained by each layer feature. At last, the majority voting method, the classified entropy voting strategy and the winner integration strategy are adopted respectively. The experimental results show that the recognition effect of this model is higher than that of 10 comparison methods, and its performance is much less affected by the number of network layers and the number of hidden layer units than the traditional DBN model. The vibration signals in different channels of train running are both complementary and redundant. In order to make full use of the complementary information of multi-channel vibration signals, a method of channel selection based on similarity ratio is proposed, and a multi-channel depth belief network model (Multi-channel Deep Belief Network,MDBN) is constructed to identify multi-channel operating conditions. First, the FFT coefficients of all channel vibration signals are extracted. Then, the similarity ratio of FFT features of each channel is calculated, and several channels with high similarity ratio are selected. Finally, the MDBN model is constructed to learn the features of the filtered multi-channel data, and the co-layer of MDBN is used to realize the fusion of multi-channel features, and the classification and recognition are carried out. The experimental results show that the feature extraction ability of MDBN is better than that of DBN model, and the recognition rate of MDBN is higher than that of DBN and DBN hierarchical integration model.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U270.7;TP391.41
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