本文選題：貝葉斯網(wǎng)絡(luò)應(yīng)用　切入點(diǎn)：機(jī)械系統(tǒng)檢測　出處：《華中科技大學(xué)》2011年碩士論文

【摘要】：在現(xiàn)代機(jī)械系統(tǒng)的故障檢測問題中，由于系統(tǒng)內(nèi)部錯(cuò)綜復(fù)雜的關(guān)系、信息測量手段的局限性、對系統(tǒng)知識的不甚了解等原因，會使得我們考察的問題本身具有較大的不確定性。 貝葉斯網(wǎng)絡(luò)作為基于概率論和圖論的可視化網(wǎng)絡(luò)模型，具有較強(qiáng)的自主學(xué)習(xí)能力和簡潔直觀的表達(dá)能力等諸多優(yōu)越性，對于包含不確定性因素的復(fù)雜機(jī)械系統(tǒng)的相關(guān)問題研究具有很大的優(yōu)勢和廣泛的應(yīng)用前景。 過貝葉斯網(wǎng)在具體應(yīng)用中，也有很多問題需要考慮，比如樣本過少，節(jié)點(diǎn)繁雜時(shí)，如何有效進(jìn)行近似推理，貝葉斯網(wǎng)的節(jié)點(diǎn)賦值出現(xiàn)誤差時(shí)我們怎么辦，還有在應(yīng)用貝葉斯網(wǎng)對機(jī)械進(jìn)行故障檢測時(shí)，需要安放許多傳感器對系統(tǒng)進(jìn)行信息讀取，傳感器過多可以更詳盡地獲取系統(tǒng)信息，不過過多的傳感器會帶有不少的“冗余信息”，并且會導(dǎo)致構(gòu)建的網(wǎng)絡(luò)底部節(jié)點(diǎn)相當(dāng)多，加大網(wǎng)絡(luò)學(xué)習(xí)成本。如何優(yōu)化觀察節(jié)點(diǎn)，提高推斷效率是很有意義的工作。 本文主要對貝葉斯網(wǎng)應(yīng)用于機(jī)械故障檢測中的上述關(guān)鍵問題進(jìn)行一定研究和探討，首先我們對實(shí)際應(yīng)用中貝葉斯網(wǎng)的近似推理問題進(jìn)行了研究，比對了兩種隨機(jī)模擬算法，，并指出利弊，以便在應(yīng)用中更好的實(shí)施。 其次，觀察節(jié)點(diǎn)的測量誤差在機(jī)械系統(tǒng)故障檢測中較為常見，但是傳統(tǒng)的方法象小波包去噪之類幾乎全部是對于連續(xù)信息的去噪處理，本文引進(jìn)了Gmbs抽樣方法用于對于離散化后節(jié)點(diǎn)的信息去噪消除測量誤差，進(jìn)行了相關(guān)探討，并期望在實(shí)際應(yīng)用中有斷推廣。 最后我們考慮在系統(tǒng)故障檢測問題中構(gòu)建的貝葉斯網(wǎng)絡(luò)觀察節(jié)點(diǎn)的簡化問題，由于實(shí)際問題中經(jīng)驗(yàn)信息的缺乏及對系統(tǒng)機(jī)理的不甚了解，使得我們安放的傳感器接收了過多冗余的系統(tǒng)信息，從而導(dǎo)致觀察節(jié)點(diǎn)過多，進(jìn)而導(dǎo)致貝葉斯網(wǎng)絡(luò)推斷成本的加大，我們以汽輪機(jī)故障檢測為實(shí)例探討了貝葉斯網(wǎng)應(yīng)用中觀測節(jié)點(diǎn)的優(yōu)化問題，結(jié)合常用統(tǒng)計(jì)手段主成份分析和因子分析對含有重疊信息的貝葉斯網(wǎng)的底部節(jié)點(diǎn)進(jìn)行主要故障信息的提取，在呆留原有主要觀察信息的基礎(chǔ)上，簡化貝葉斯網(wǎng)葉節(jié)點(diǎn)，構(gòu)造新網(wǎng)絡(luò)進(jìn)行故障診斷，降低推斷成本，提高推斷效率。
[Abstract]:In the problem of fault detection in modern mechanical system, due to the intricate relations within the system, the limitation of information measurement means and the lack of understanding of the system knowledge, the problems we examine have greater uncertainty. As a visual network model based on probability theory and graph theory, Bayesian network has many advantages, such as strong autonomous learning ability and simple and intuitive expression ability. The research on the related problems of complex mechanical systems with uncertain factors has great advantages and wide application prospects. There are also many problems to be considered in the application of the Bayesian network. For example, when the samples are too small and the nodes are complicated, how to effectively carry out approximate reasoning, and what should we do when there are errors in the assignment of the nodes of the Bayesian networks? And when using Bayesian network to detect the fault of machinery, many sensors need to be put in to read the information of the system, and too many sensors can obtain the information of the system in more detail. However, too many sensors will have a lot of "redundant information", and will lead to a considerable number of nodes at the bottom of the network, which will increase the cost of network learning. How to optimize observation nodes and improve the efficiency of inference is a very meaningful work. In this paper, the key problems mentioned above in the application of Bayesian network in mechanical fault detection are studied and discussed. Firstly, the approximate reasoning problem of Bayesian network in practical application is studied, and two stochastic simulation algorithms are compared. And points out the advantages and disadvantages, in order to better implement in the application. Secondly, the measurement error of observation nodes is more common in mechanical system fault detection, but the traditional methods such as wavelet packet denoising are almost all for the continuous information denoising processing. In this paper, the Gmbs sampling method is introduced to eliminate the measurement error for the discrete node information denoising, and it is expected to be extended in practical application. Finally, we consider the simplification of Bayesian network observation nodes in the system fault detection problem, because of the lack of empirical information and the lack of understanding of the mechanism of the system. The sensor we put in receives too much redundant system information, which leads to too many observation nodes, which leads to the increase of the cost of Bayesian network inference. Taking turbine fault detection as an example, we discuss the optimization of observation nodes in Bayesian network application. Combined with principal component analysis and factor analysis, the main fault information of the bottom node of Bayesian network with overlapping information is extracted, and the leaf node of Bayesian network is simplified on the basis of retaining the original main observation information. A new network is constructed for fault diagnosis to reduce the cost of inference and improve the efficiency of inference.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3;TP18

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