本文選題：水電機(jī)組 + 故障診斷　； 參考：《浙江工業(yè)大學(xué)》2014年碩士論文

【摘要】：水電機(jī)組作為小水電生產(chǎn)過程中的核心設(shè)備,它的運行狀況不僅關(guān)系到水電廠的安全還直接關(guān)系到水電廠能否向電網(wǎng)安全、經(jīng)濟(jì)地提供可靠電力。由于水電機(jī)組具有構(gòu)造復(fù)雜,機(jī)組運行呈季節(jié)性,異常振動誘發(fā)因素多等特點,日益影響著電網(wǎng)的安全穩(wěn)定運行。因此,對水電機(jī)組進(jìn)行運行狀態(tài)監(jiān)測和故障診斷,確保水電機(jī)組安全、可靠、穩(wěn)定運行,使其發(fā)揮最大的發(fā)電效益,具有十分重要的意義。傳統(tǒng)的故障診斷方法主要基于專業(yè)技術(shù)人員的經(jīng)驗和知識來推理診斷。這種過分依賴于個人經(jīng)驗和知識的方法目前仍在水電機(jī)組故障診斷中占主導(dǎo)地位,其弊端是顯而易見的。因此,必須提高設(shè)備故障診斷的自動化和智能化程度,實現(xiàn)對設(shè)備的高效、可靠的智能診斷。本文分析了隨機(jī)近鄰嵌入分析系列方法的特點,并將其應(yīng)用在水電機(jī)組故障診斷中。具體工作包含以下4個方面：(1)針對隨機(jī)近鄰嵌入分析系列方法的非線性本質(zhì)和無監(jiān)督學(xué)習(xí)特征等問題,提出了一種線性有監(jiān)督的特征提取方法,稱為判別隨機(jī)近鄰嵌入分析方法。該方法的優(yōu)勢主要包括：通過輸入樣本的類別信息構(gòu)建數(shù)據(jù)分布的聯(lián)合概率表達(dá)式,用于反映同類和異類數(shù)據(jù)間的相似度,使得方法具有監(jiān)督性；引入線性投影矩陣生成子空間數(shù)據(jù),使得方法呈現(xiàn)線性本質(zhì)。對比實驗表明,所提方法不僅具有較好的可視化能力,而且能夠有效地對不同類別的數(shù)據(jù)進(jìn)行降維分簇,提升后續(xù)模式分類器的鑒別效果。(2)針對判別隨機(jī)近鄰嵌入分析方法計算量復(fù)雜且不適合多樣本數(shù)據(jù)等問題,提出了一種快速判別隨機(jī)近鄰嵌入分析方法。該方法通過引入K最近鄰分類算法的思想,減少樣本量來計算樣本相似度,其在保證識別率的前提下減少了算法的運行時間。(3)提出核判別隨機(jī)近鄰嵌入分析方法。該方法在判別隨機(jī)近鄰嵌入分析方法的基礎(chǔ)上,通過引入核函數(shù)將原空間中的樣本映射到高維核空間中,構(gòu)建了用于反映同類和異類數(shù)據(jù)間相似度的聯(lián)合概率表達(dá)式。其突出了異類樣本間的特征差異,使樣本變得線性可分,從而提高了分類性能。(4)將所提的核判別隨機(jī)近鄰嵌入分析方法應(yīng)用在軸心軌跡特征提取上,以達(dá)到對水電機(jī)組進(jìn)行故障診斷的應(yīng)用。仿真實驗證明了該方法在水電機(jī)組故障診斷上的有效性和可行性。
[Abstract]:As the core equipment in the process of small hydropower production, the operation condition of hydropower unit is not only related to the safety of hydropower plant, but also directly related to whether the hydropower plant can provide reliable power to the power grid economically. Because of the complex structure, seasonal operation and many induced factors of abnormal vibration, hydropower units increasingly affect the safe and stable operation of the power grid. Therefore, it is of great significance to monitor the operation status and fault diagnosis of hydropower units, to ensure the safe, reliable and stable operation of hydropower units and to maximize the power generation efficiency. The traditional fault diagnosis methods are mainly based on the experience and knowledge of professional technicians. This method, which relies too much on personal experience and knowledge, still dominates the fault diagnosis of hydroelectric generating sets, and its disadvantages are obvious. Therefore, it is necessary to improve the automation and intelligence of equipment fault diagnosis, and to realize efficient and reliable intelligent diagnosis of equipment. This paper analyzes the characteristics of random nearest neighbor embedding analysis method and applies it to fault diagnosis of hydropower unit. The specific work includes the following four aspects: (1) A linear supervised feature extraction method is proposed to solve the nonlinear nature and unsupervised learning characteristics of the stochastic nearest neighbor embedding analysis method. It is called discriminant random nearest neighbor embedding analysis method. The advantages of this method are as follows: the joint probability expression of data distribution is constructed by input the category information of the sample, which is used to reflect the similarity between the similar and heterogeneous data, so that the method is supervised; The linear projection matrix is introduced to generate subspace data so that the method is linear in nature. The experimental results show that the proposed method not only has good visualization ability, but also can effectively reduce the dimensionality of different kinds of data. To improve the discriminant effect of subsequent pattern classifier, a fast discriminant random nearest neighbor embedding analysis method is proposed to solve the problems of complex computation and unsuitable for multi-sample data. By introducing the idea of K-nearest neighbor classification algorithm to reduce the sample size to calculate the sample similarity, this method reduces the running time of the algorithm under the premise of ensuring the recognition rate. (3) A kernel discriminant random nearest neighbor embedding analysis method is proposed. On the basis of discriminating random nearest neighbor embedding analysis method, by introducing kernel function to map the samples in the original space to high dimensional kernel space, the joint probability expression is constructed to reflect the similarity between the similar and heterogeneous data. The proposed kernel discriminant random nearest neighbor embedding analysis method is applied to the feature extraction of the axis locus, which highlights the characteristic differences among the heterogeneous samples, makes the samples linearly separable, and improves the classification performance. In order to achieve the application of fault diagnosis for hydropower units. The simulation results show that the method is effective and feasible in fault diagnosis of hydropower units.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TV738
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本文編號：1842796