【摘要】：作為機(jī)械設(shè)備中最常見的零件之一,滾動(dòng)軸承的工作狀態(tài)直接決定了整臺(tái)設(shè)備能否正常工作,甚至關(guān)系到整條生產(chǎn)線能否正常運(yùn)行。滾動(dòng)軸承診斷技術(shù),可以及時(shí)的發(fā)現(xiàn)故障,避免造成重大事故,因此,進(jìn)行軸承診斷的研究具有至關(guān)重要的現(xiàn)實(shí)意義。傳統(tǒng)的信號(hào)處理方法常常忽略軸承信號(hào)中的重要信息,因此,利用傳統(tǒng)故障診斷技術(shù)進(jìn)行分析存在一定缺陷,出現(xiàn)誤診和漏診現(xiàn)象比較頻繁。而且隨著科學(xué)技術(shù)的發(fā)展,對(duì)故障診斷的要求也越來越高,機(jī)器學(xué)習(xí)越來越多的被應(yīng)用于故障診斷。本文以滾動(dòng)軸承為研究對(duì)象,針對(duì)軸承數(shù)據(jù)自身所具有的特點(diǎn)以及目前技術(shù)存在的缺陷,以機(jī)器學(xué)習(xí)算法為基礎(chǔ)理論,展開研究,主要工作內(nèi)容如下:(1)針對(duì)使用單一特征對(duì)軸承故障進(jìn)行診斷時(shí),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(gòu)特征融合的軸承故障檢測(cè)方法。不同方法提取的異構(gòu)特征具有相互補(bǔ)充的作用,基于異構(gòu)特征融合的方法首先將多種方法提取的異構(gòu)特征并成一個(gè)聯(lián)合特征集,然后把所有的特征基于組特征相關(guān)性用多目標(biāo)粒子群方法將這些特征實(shí)現(xiàn)最優(yōu)分組,保證組內(nèi)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(duì)每組特征進(jìn)行特征選擇,將選擇得到的特征作為異構(gòu)融合的最終特征。該方法以支持向量機(jī)為基礎(chǔ)算法,對(duì)異構(gòu)特征進(jìn)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(guān)性。最后在美國西儲(chǔ)大學(xué)公布的軸承故障數(shù)據(jù)和全壽命軸承故障數(shù)據(jù)上進(jìn)行仿真實(shí)驗(yàn),證明了該方法的有效性。(2)針對(duì)軸承故障數(shù)據(jù)的在線和類別不均衡的兩個(gè)特點(diǎn),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(gè)階段,在離線階段,首先構(gòu)建主曲線,將數(shù)據(jù)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(duì)兩個(gè)區(qū)域內(nèi)的樣本進(jìn)行不同程度的擴(kuò)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(dá)到的數(shù)據(jù)塊,得到重構(gòu)后的均衡數(shù)據(jù)集。該算法在不改變整體數(shù)據(jù)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(gòu)的方法提取故障特征,在來自美國西儲(chǔ)大學(xué)的軸承故障數(shù)據(jù)和全壽命軸承故障數(shù)據(jù)上驗(yàn)證了該方法的優(yōu)勢(shì)。
[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(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TH133.33

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