基于變分貝葉斯混合獨立分量分析的機械故障診斷方法研究
發(fā)布時間：2018-09-09 12:13
【摘要】：本論文在國家自然科學基金(50775208,51075372)和湖南省機械設(shè)備健康維護重點實驗室開放基金(200904)資助下,通過將變分貝葉斯混合獨立分量分析理論引入到機械故障診斷中,提出了基于變分貝葉斯混合獨立分量分析的機械故障診斷方法,并進行了仿真和實驗研究,取得了一些創(chuàng)新性成果。其主要內(nèi)容包括以下幾個方面： 第一章,詳細介紹了本課題的選題背景、意義,全面綜述了變分貝葉斯理論和獨立分量分析理論的研究現(xiàn)狀及其應(yīng)用現(xiàn)狀,在此基礎(chǔ)上,提出了本論文的研究內(nèi)容與創(chuàng)新之處。 第二章,論述了變分貝葉斯獨立分量分析(vbICA)的基本原理及其兩種算法(vbICA1算法和vbICA2算法)。并通過實驗比較了這兩種算法的分離性能。實驗結(jié)果表明,在噪聲環(huán)境下的信號盲源分離,用這兩種算法都能得到很好的分離效果。然而,在分離過程中,vbICA2算法能夠得到比vbICA1算法更好的分離效果,而且,隨著噪聲的增強,vbICA2算法的分離性能的優(yōu)越性越明顯。在此基礎(chǔ)上,給出了變分貝葉斯混合獨立分量分析提出的意義,詳細論述了變分貝葉斯混合獨立分量分析理論和算法。本章的內(nèi)容是整篇論文的理論基礎(chǔ)。 第三章,指出用ICA分解和表示數(shù)據(jù)時,假設(shè)整個數(shù)據(jù)分布完全可以用一個坐標系來描述。然而,當觀測數(shù)據(jù)是由許多自相似的、非高斯的流形組成時,則硬是用一個單獨的、全局的表示是不合適的,這樣會產(chǎn)生一個次優(yōu)的表示。針對ICA在盲源分離中的不足,本文在變分貝葉斯理論的基礎(chǔ)上,提出了一種基于變分貝葉斯混合獨立分量分析的機械故障源盲分離方法。該方法是考慮到源信號來自于多個坐標系,然后在多個坐標系下建立獨立分量分析混合模型對觀測信號進行學習分離。仿真結(jié)果驗證了該方法的有效性。最后,將提出的方法應(yīng)用到軸承內(nèi)、外圈故障的源分離中,實驗結(jié)果也驗證了提出的方法的有效性。 第四章,針對現(xiàn)有的機械故障源數(shù)估計方法存在的不足,即它只能給出源數(shù)估計的上限,并不能準確估計源數(shù),而且未考慮噪聲干擾的影響,提出了一種基于變分貝葉斯混合獨立分量分析的機械故障源數(shù)估計方法,提出的方法以貝葉斯網(wǎng)絡(luò)為基礎(chǔ),將混合獨立分量分析與變分貝葉斯結(jié)合起來,并通過最大化目標函數(shù)得到負自由能(Negative Free Energy,NFE)來估計出最佳的隱藏信源數(shù)目。仿真和實驗結(jié)果表明,本文提出的方法是非常有效的。 第五章,論述了機械故障診斷欠定盲分離方法中存在的不足,即現(xiàn)有的欠定盲分離并沒有考慮在噪聲環(huán)境下,針對此不足,在變分貝葉斯和混合獨立分量分析理論的基礎(chǔ)上,提出了一種基于變分貝葉斯混合獨立分量分析的機械故障診斷欠定盲源分離方法。該方法是假設(shè)源信號來自不同的簇(即流形),對每個簇建立一個ICA模型,這樣就產(chǎn)生了ICA混合模型,然后結(jié)合變分貝葉斯對ICA混合模型進行學習,通過學習可以從觀測信號中估計出混合矩陣以及恢復出源信號。仿真研究和實驗研究表明,該方法對觀測信號數(shù)目少于源信號數(shù)目的欠定盲源分離的效果非常滿意。 第六章,對本論文的研究工作進行了全面總結(jié),并對進一步開展的工作做了展望。
[Abstract]:In this paper, with the support of the National Natural Science Foundation of China (50775208, 51075372) and the Open Fund of the Key Laboratory of Health Maintenance of Mechanical Equipment of Hunan Province (200904), by introducing the theory of Variational Bayesian Mixed Independent Component Analysis into mechanical fault diagnosis, a method of mechanical fault diagnosis based on Variational Bayesian Mixed Independent Component Analysis is proposed. The main contents include the following aspects:
In the first chapter, the background and significance of this topic are introduced in detail. The research status and application status of variational Bayesian theory and independent component analysis theory are summarized. On this basis, the research contents and innovations of this paper are put forward.
In the second chapter, the basic principle of variational Bayesian independent component analysis (vbICA) and its two algorithms (vbICA1 algorithm and vbICA2 algorithm) are discussed. The separation performance of the two algorithms is compared through experiments. The experimental results show that the two algorithms can achieve good separation performance in noise environment. In the process of separation, vbICA2 algorithm can get better separation effect than vbICA1 algorithm, and with the increase of noise, the superiority of vbICA2 algorithm is more obvious. On this basis, the significance of variational Bayesian Mixed Independent Component Analysis is given, and the theory of variational Bayesian Mixed Independent Component Analysis and the theory of variational Bayesian Mixed Independent Component Analysis are discussed in detail. Algorithm. The content of this chapter is the theoretical basis of the whole paper.
In Chapter 3, it is pointed out that when ICA is used to decompose and represent data, it is assumed that the whole data distribution can be completely described in a coordinate system. However, when the observed data are composed of many self-similar, non-Gaussian manifolds, it is inappropriate to simply use a single, global representation, which results in a suboptimal representation. In this paper, based on the variational Bayesian theory, a method of blind source separation for mechanical faults based on the variational Bayesian hybrid independent component analysis is proposed. The simulation results verify the effectiveness of the proposed method. Finally, the proposed method is applied to the source separation of bearing inner and outer ring faults. The experimental results also verify the effectiveness of the proposed method.
In Chapter 4, aiming at the shortcomings of the existing methods for estimating the number of mechanical fault sources, that is, they can only give the upper limit of the number of sources, can not accurately estimate the number of sources, and do not consider the influence of noise interference, a method of estimating the number of mechanical fault sources based on variational Bayesian mixed independent component analysis is proposed. Based on the network, the hybrid independent component analysis (HICA) and variational Bayesian are combined to estimate the optimal number of hidden sources by maximizing the Negative Free Energy (NFE) of the objective function.
In the fifth chapter, the shortcomings of under-determined blind separation method for mechanical fault diagnosis are discussed, that is, the existing under-determined blind separation method does not take into account the noise environment. To overcome this shortcomings, a mechanical fault diagnosis method based on variational Bayesian and mixed independent component analysis is proposed. This method is based on the assumption that the source signals come from different clusters (i.e. manifolds), and an ICA model is established for each cluster. Thus, a ICA hybrid model is generated. Then the ICA hybrid model is studied by using the variational Bayesian method. The mixed matrix can be estimated from the observed signals and the source signals can be recovered by learning. Research and experimental results show that this method is very satisfactory for under-determined blind source separation with less than the number of observed signals.
In the sixth chapter, the research work of this paper is summarized comprehensively, and the further work is prospected.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3
本文編號：2232331
[Abstract]:In this paper, with the support of the National Natural Science Foundation of China (50775208, 51075372) and the Open Fund of the Key Laboratory of Health Maintenance of Mechanical Equipment of Hunan Province (200904), by introducing the theory of Variational Bayesian Mixed Independent Component Analysis into mechanical fault diagnosis, a method of mechanical fault diagnosis based on Variational Bayesian Mixed Independent Component Analysis is proposed. The main contents include the following aspects:
In the first chapter, the background and significance of this topic are introduced in detail. The research status and application status of variational Bayesian theory and independent component analysis theory are summarized. On this basis, the research contents and innovations of this paper are put forward.
In the second chapter, the basic principle of variational Bayesian independent component analysis (vbICA) and its two algorithms (vbICA1 algorithm and vbICA2 algorithm) are discussed. The separation performance of the two algorithms is compared through experiments. The experimental results show that the two algorithms can achieve good separation performance in noise environment. In the process of separation, vbICA2 algorithm can get better separation effect than vbICA1 algorithm, and with the increase of noise, the superiority of vbICA2 algorithm is more obvious. On this basis, the significance of variational Bayesian Mixed Independent Component Analysis is given, and the theory of variational Bayesian Mixed Independent Component Analysis and the theory of variational Bayesian Mixed Independent Component Analysis are discussed in detail. Algorithm. The content of this chapter is the theoretical basis of the whole paper.
In Chapter 3, it is pointed out that when ICA is used to decompose and represent data, it is assumed that the whole data distribution can be completely described in a coordinate system. However, when the observed data are composed of many self-similar, non-Gaussian manifolds, it is inappropriate to simply use a single, global representation, which results in a suboptimal representation. In this paper, based on the variational Bayesian theory, a method of blind source separation for mechanical faults based on the variational Bayesian hybrid independent component analysis is proposed. The simulation results verify the effectiveness of the proposed method. Finally, the proposed method is applied to the source separation of bearing inner and outer ring faults. The experimental results also verify the effectiveness of the proposed method.
In Chapter 4, aiming at the shortcomings of the existing methods for estimating the number of mechanical fault sources, that is, they can only give the upper limit of the number of sources, can not accurately estimate the number of sources, and do not consider the influence of noise interference, a method of estimating the number of mechanical fault sources based on variational Bayesian mixed independent component analysis is proposed. Based on the network, the hybrid independent component analysis (HICA) and variational Bayesian are combined to estimate the optimal number of hidden sources by maximizing the Negative Free Energy (NFE) of the objective function.
In the fifth chapter, the shortcomings of under-determined blind separation method for mechanical fault diagnosis are discussed, that is, the existing under-determined blind separation method does not take into account the noise environment. To overcome this shortcomings, a mechanical fault diagnosis method based on variational Bayesian and mixed independent component analysis is proposed. This method is based on the assumption that the source signals come from different clusters (i.e. manifolds), and an ICA model is established for each cluster. Thus, a ICA hybrid model is generated. Then the ICA hybrid model is studied by using the variational Bayesian method. The mixed matrix can be estimated from the observed signals and the source signals can be recovered by learning. Research and experimental results show that this method is very satisfactory for under-determined blind source separation with less than the number of observed signals.
In the sixth chapter, the research work of this paper is summarized comprehensively, and the further work is prospected.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3
【參考文獻】
相關(guān)期刊論文 前10條
1 蘇野平,何量,楊榮震,朱小剛;一種改進的基于高階累積量的語音盲分離算法[J];電子學報;2002年07期
2 倪晉平,馬遠良,鄢社鋒;基于高階累積量的復數(shù)混合矩陣盲估計算法[J];電子與信息學報;2002年11期
3 譚北海;謝勝利;;基于源信號數(shù)目估計的欠定盲分離[J];電子與信息學報;2008年04期
4 劉琨;杜利民;王勁林;;基于時頻域單源主導區(qū)的盲源欠定分離方法[J];中國科學(E輯:信息科學);2008年08期
5 張朝柱;張健沛;孫曉東;;基于curvelet變換和獨立分量分析的含噪盲源分離[J];計算機應(yīng)用;2008年05期
6 陳仲生,楊擁民,沈國際;獨立分量分析在直升機齒輪箱故障早期診斷中的應(yīng)用[J];機械科學與技術(shù);2004年04期
7 李志農(nóng);呂亞平;韓捷;;基于時頻分析的機械設(shè)備非平穩(wěn)信號盲分離[J];機械強度;2008年03期
8 范濤;李志農(nóng);肖堯先;;基于源數(shù)估計的機械源信號盲分離方法研究[J];機械強度;2011年01期
9 張洪淵,賈鵬,史習智;確定盲分離中未知信號源個數(shù)的奇異值分解法[J];上海交通大學學報;2001年08期
10 徐尚志;蘇勇;葉中付;;欠定條件下的盲分離算法[J];數(shù)據(jù)采集與處理;2006年02期
相關(guān)碩士學位論文 前4條
1 陳勇;獨立分量分析在振動信號處理中的應(yīng)用[D];吉林大學;2007年
2 張楠;基于貝葉斯網(wǎng)絡(luò)的汽輪機振動故障診斷研究[D];華北電力大學（河北）;2007年
3 康斌;獨立分量分析在機械振動信號中的應(yīng)用研究[D];武漢理工大學;2008年
4 王琦;基于獨立分量分析的故障源識別技術(shù)[D];華北電力大學（北京）;2008年
，本文編號：2232331
本文鏈接：http://www.sikaile.net/kejilunwen/jixiegongcheng/2232331.html
最近更新
教材專著
