面向故障診斷的異構特征融合與在線不均衡分類研究
發(fā)布時間：2018-07-27 10:16
【摘要】：作為機械設備中最常見的零件之一,滾動軸承的工作狀態(tài)直接決定了整臺設備能否正常工作,甚至關系到整條生產線能否正常運行。滾動軸承診斷技術,可以及時的發(fā)現故障,避免造成重大事故,因此,進行軸承診斷的研究具有至關重要的現實意義。傳統(tǒng)的信號處理方法常常忽略軸承信號中的重要信息,因此,利用傳統(tǒng)故障診斷技術進行分析存在一定缺陷,出現誤診和漏診現象比較頻繁。而且隨著科學技術的發(fā)展,對故障診斷的要求也越來越高,機器學習越來越多的被應用于故障診斷。本文以滾動軸承為研究對象,針對軸承數據自身所具有的特點以及目前技術存在的缺陷,以機器學習算法為基礎理論,展開研究,主要工作內容如下:(1)針對使用單一特征對軸承故障進行診斷時,所含信息具有不確定性,選擇的特征無法使用最終選擇的算法這一問題,提出了基于異構特征融合的軸承故障檢測方法。不同方法提取的異構特征具有相互補充的作用,基于異構特征融合的方法首先將多種方法提取的異構特征并成一個聯(lián)合特征集,然后把所有的特征基于組特征相關性用多目標粒子群方法將這些特征實現最優(yōu)分組,保證組內特征間距最小并且組間特征間距最大,最后利用wrapper算法在組的層次上對每組特征進行特征選擇,將選擇得到的特征作為異構融合的最終特征。該方法以支持向量機為基礎算法,對異構特征進行充分合理的融合,并在組的層次上摒棄了特征之間存在的冗余相關性。最后在美國西儲大學公布的軸承故障數據和全壽命軸承故障數據上進行仿真實驗,證明了該方法的有效性。(2)針對軸承故障數據的在線和類別不均衡的兩個特點,提出一種基于主曲線和粒劃分的在線不均衡故障診斷方法。算法包括離線和在線兩個階段,在離線階段,首先構建主曲線,將數據分布分為置信區(qū)域和非置信區(qū)域,然后通過粒劃分,分別對兩個區(qū)域內的樣本進行不同程度的擴充少類和削減多類,在線階段采用同樣的方法處理在線貫序達到的數據塊,得到重構后的均衡數據集。該算法在不改變整體數據的分布特征的前提下,有效的減少欠采樣過程中多類樣本信息的丟失。最終選擇用相空間重構的方法提取故障特征,在來自美國西儲大學的軸承故障數據和全壽命軸承故障數據上驗證了該方法的優(yōu)勢。
[Abstract]:As one of the most common parts in mechanical equipment, the working state of rolling bearings directly determines whether the whole equipment can work normally, or even whether the whole production line can run normally. Rolling bearing diagnosis technology can find fault in time and avoid serious accident. Therefore, the research of bearing diagnosis is of vital practical significance. Traditional signal processing methods often ignore the important information in bearing signals. Therefore, there are some defects in traditional fault diagnosis techniques, and misdiagnosis and missed diagnosis appear frequently. With the development of science and technology, the requirement of fault diagnosis is more and more high, and machine learning is applied to fault diagnosis more and more. This paper takes rolling bearing as the research object, aiming at the characteristics of bearing data itself and the defects of current technology, and taking the machine learning algorithm as the basic theory, the research is carried out. The main work is as follows: (1) when using single feature to diagnose bearing fault, the information contained is uncertain, and the selected feature can not use the algorithm of final selection. A bearing fault detection method based on heterogeneous feature fusion is proposed. The heterogeneous features extracted by different methods are complementary to each other. Firstly, the heterogeneous features extracted by different methods are combined into a joint feature set based on heterogeneous feature fusion. Then all the features are grouped optimally based on the group feature correlation and the multi-objective particle swarm optimization method is used to ensure the minimum feature spacing within the group and the maximum feature spacing among the groups. Finally, the wrapper algorithm is used to select the features of each group at the group level, and the selected features are regarded as the final features of heterogeneous fusion. This method is based on support vector machine (SVM), which can fuse the heterogeneous features fully and reasonably, and abandon the redundant correlation among the features at the group level. Finally, simulation experiments on bearing fault data and life bearing fault data published by the University of Western Reserve in the United States show the effectiveness of this method. (2) aiming at the two characteristics of online and class imbalance of bearing fault data, An online fault diagnosis method based on principal curve and particle partition is proposed. The algorithm consists of offline and online phases. In the off-line phase, the main curve is first constructed, the data distribution is divided into confidence region and disbelief region, and then the distribution is partitioned by grain. The samples in the two regions are expanded and reduced to different degrees. In the online stage, the same method is used to deal with the online sequential data blocks, and the reconstructed equilibrium data sets are obtained. Without changing the distribution characteristics of the whole data, the algorithm can effectively reduce the loss of multi-class sample information in the process of under-sampling. Finally, the method of phase space reconstruction is used to extract the fault features, and the advantages of this method are verified on the bearing fault data from the University of Western Reserve and the full life bearing fault data.
【學位授予單位】：河南師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;TH133.33
本文編號：2147506
[Abstract]:As one of the most common parts in mechanical equipment, the working state of rolling bearings directly determines whether the whole equipment can work normally, or even whether the whole production line can run normally. Rolling bearing diagnosis technology can find fault in time and avoid serious accident. Therefore, the research of bearing diagnosis is of vital practical significance. Traditional signal processing methods often ignore the important information in bearing signals. Therefore, there are some defects in traditional fault diagnosis techniques, and misdiagnosis and missed diagnosis appear frequently. With the development of science and technology, the requirement of fault diagnosis is more and more high, and machine learning is applied to fault diagnosis more and more. This paper takes rolling bearing as the research object, aiming at the characteristics of bearing data itself and the defects of current technology, and taking the machine learning algorithm as the basic theory, the research is carried out. The main work is as follows: (1) when using single feature to diagnose bearing fault, the information contained is uncertain, and the selected feature can not use the algorithm of final selection. A bearing fault detection method based on heterogeneous feature fusion is proposed. The heterogeneous features extracted by different methods are complementary to each other. Firstly, the heterogeneous features extracted by different methods are combined into a joint feature set based on heterogeneous feature fusion. Then all the features are grouped optimally based on the group feature correlation and the multi-objective particle swarm optimization method is used to ensure the minimum feature spacing within the group and the maximum feature spacing among the groups. Finally, the wrapper algorithm is used to select the features of each group at the group level, and the selected features are regarded as the final features of heterogeneous fusion. This method is based on support vector machine (SVM), which can fuse the heterogeneous features fully and reasonably, and abandon the redundant correlation among the features at the group level. Finally, simulation experiments on bearing fault data and life bearing fault data published by the University of Western Reserve in the United States show the effectiveness of this method. (2) aiming at the two characteristics of online and class imbalance of bearing fault data, An online fault diagnosis method based on principal curve and particle partition is proposed. The algorithm consists of offline and online phases. In the off-line phase, the main curve is first constructed, the data distribution is divided into confidence region and disbelief region, and then the distribution is partitioned by grain. The samples in the two regions are expanded and reduced to different degrees. In the online stage, the same method is used to deal with the online sequential data blocks, and the reconstructed equilibrium data sets are obtained. Without changing the distribution characteristics of the whole data, the algorithm can effectively reduce the loss of multi-class sample information in the process of under-sampling. Finally, the method of phase space reconstruction is used to extract the fault features, and the advantages of this method are verified on the bearing fault data from the University of Western Reserve and the full life bearing fault data.
【學位授予單位】：河南師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;TH133.33
【參考文獻】
相關期刊論文 前10條
1 張新明;涂強;尹欣欣;馮夢清;;嵌入趨化算子的PSO算法及其在多閾值分割中的應用[J];計算機科學;2016年02期
2 毛文濤;徐文濤;薛天宇;何玲;;一種基于特征子集區(qū)分度優(yōu)化的分組特征選擇算法[J];小型微型計算機系統(tǒng);2015年08期
3 王金婉;毛文濤;何玲;王禮云;;基于不均衡樣本重構的加權在線貫序極限學習機[J];計算機應用;2015年06期
4 DENG ChenWei;HUANG GuangBin;XU Jia;TANG JieXiong;;Extreme learning machines: new trends and applications[J];Science China(Information Sciences);2015年02期
5 程鳳偉;王文劍;郭虎升;;動態(tài)粒度SVM學習算法[J];模式識別與人工智能;2014年04期
6 郭虎升;王文劍;;基于粒度偏移因子的支持向量機學習方法[J];計算機研究與發(fā)展;2013年11期
7 王登;苗奪謙;王睿智;;一種新的基于小波包分解的EEG特征抽取與識別方法研究[J];電子學報;2013年01期
8 楊樂;張瑞;;在線序列ELM算法及其發(fā)展[J];西北大學學報(自然科學版);2012年06期
9 何星;王宏力;陸敬輝;姜偉;;灰色稀疏極端學習機在激光陀螺隨機誤差系數預測中的應用[J];紅外與激光工程;2012年12期
10 毛力;王運濤;劉興陽;李朝鋒;;基于改進極限學習機的短期電力負荷預測方法[J];電力系統(tǒng)保護與控制;2012年20期
相關博士學位論文 前2條
1 朱可恒;滾動軸承振動信號特征提取及診斷方法研究[D];大連理工大學;2013年
2 王婷;EMD算法研究及其在信號去噪中的應用[D];哈爾濱工程大學;2010年
相關碩士學位論文 前2條
1 孫美巖;基于振動信號的滾動軸承故障特征提取研究[D];東北石油大學;2015年
2 王文莉;基于高階譜的齒輪故障診斷與識別[D];武漢科技大學;2007年
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