基于NRST的轉(zhuǎn)子故障數(shù)據(jù)集屬性約簡方法研究
發(fā)布時間：2018-11-28 20:18
【摘要】：如何從海量數(shù)據(jù)中,挖掘出有用信息,尋找出數(shù)據(jù)之間蘊含的反映機械設(shè)備運行狀況規(guī)律,解決復雜診斷建模難的問題,實現(xiàn)對故障模式智能化識別,成為當前急切需要解決的問題。然而采集到的反映復雜機械系統(tǒng)運行狀況的工業(yè)數(shù)據(jù)往往夾雜著大量噪音,具有較強的非線性和耦合性,嚴重影響了有效信息的獲取,且目前單一的故障診斷模型無法有效的對復雜的機械系統(tǒng)做出全面的診斷。針對以上問題,本文開展了鄰域粗糙集理論(Neighborhood Rough Set Theory,NRST)與其它數(shù)據(jù)驅(qū)動方法結(jié)合的轉(zhuǎn)子故障模式識別方法的研究工作,重點對NRST的屬性約簡方法及NRST結(jié)合統(tǒng)計分析及機器學習的方法進行了探討。本文的主要工作概況及取得的研究成果如下:(1)介紹NRST的定義及前向貪心屬性約簡方法,充分利用了NRST能直接處理連續(xù)數(shù)值型屬性的優(yōu)勢,提出以轉(zhuǎn)子工頻倍頻為條件屬性以故障類型為決策屬性構(gòu)建NRST決策表進行特征提取的方法并且結(jié)合典型故障類別的頻率譜特性分析了可行性。實驗結(jié)果也同時證明該方法獲取的特征屬性更符合物理意義,避免了離散化過程中關(guān)鍵屬性的丟失。(2)在NRST屬性約簡的基礎(chǔ)上,提出了NRST結(jié)合費舍判別(FDA)對故障類別進行分類的方法,求出了判別函數(shù)和累積判別能力,探討了二次降維和去冗余后對故障模式識別的影響,完成了數(shù)據(jù)從高維到低維的映射,實現(xiàn)了低維下的故障分類效果。實驗結(jié)果證明該方法在特征屬性少的情況下能夠達到同樣的識別正確率,從而可以節(jié)省存儲空間提高運算效率。(3)為了尋求高效、準確的故障診斷方法,同時也為了探討NRST屬性約簡對機器學習的影響,提出了NRST結(jié)合徑向基神經(jīng)網(wǎng)絡(luò)(RBFNetwork)對故障類別辨識的方法,選用高斯標準函數(shù)作為徑向基函數(shù),采用自組織選取中心法確定基函數(shù)中心、寬度及連接權(quán)重。實驗結(jié)果證明該方法明顯縮短了建模時間,提高了識別準確率,值得推廣。(4)為了解決知識的存儲與發(fā)現(xiàn)難的問題,推動智能化診斷技術(shù)的發(fā)展,設(shè)計了基于WEKA數(shù)據(jù)挖掘平臺連接My SQL數(shù)據(jù)庫的故障識別系統(tǒng)。完成了故障知識的存儲、數(shù)據(jù)流的展現(xiàn)及WEKA中調(diào)用My SQL語句對數(shù)據(jù)庫的訪問。
[Abstract]:How to mine the useful information from the massive data, find out the rule of reflecting the running condition of the mechanical equipment contained in the data, solve the difficult problem of complex diagnosis and modeling, and realize the intelligent recognition of the fault pattern. Become the current urgent need to solve the problem. However, the industrial data collected to reflect the running state of complex mechanical systems are often mixed with a large amount of noise, which has strong nonlinearity and coupling, which seriously affects the acquisition of effective information. At present, a single fault diagnosis model can not effectively make a comprehensive diagnosis of complex mechanical systems. In order to solve the above problems, the research work of rotor fault pattern recognition based on neighborhood rough set theory (Neighborhood Rough Set Theory,NRST) and other data-driven methods is carried out in this paper. The attribute reduction method of NRST and the method of NRST combined with statistical analysis and machine learning are discussed. The main work and results of this paper are as follows: (1) the definition of NRST and the method of forward greedy attribute reduction are introduced. The advantage that NRST can directly deal with continuous numerical attributes is fully utilized. This paper presents a method of constructing NRST decision table based on rotor power frequency doubling as conditional attribute and fault type as decision attribute, and analyzes the feasibility of constructing NRST decision table based on the frequency spectrum characteristics of typical fault categories. The experimental results also show that the feature attributes obtained by this method are more physical and avoid the loss of key attributes in the discretization process. (2) on the basis of NRST attribute reduction, In this paper, a method of classifying fault categories with NRST and Fisher discriminant (FDA) is proposed. The discriminant function and cumulative discriminant ability are obtained. The effects of quadratic reduction and redundancy removal on fault pattern recognition are discussed. The mapping of data from high dimension to low dimension is completed, and the effect of fault classification under low dimension is realized. The experimental results show that the method can achieve the same recognition accuracy in the case of less feature attributes, thus saving storage space to improve the computational efficiency. (3) in order to seek an efficient and accurate fault diagnosis method, At the same time, in order to discuss the effect of NRST attribute reduction on machine learning, a method of fault classification identification based on NRST combined with radial basis function neural network (RBFNetwork) is proposed. Gao Si standard function is selected as radial basis function. The center, width and connection weight of the basis function are determined by the self-organizing selection center method. The experimental results show that the method can obviously shorten the modeling time and improve the recognition accuracy. (4) in order to solve the difficult problem of knowledge storage and discovery, the development of intelligent diagnosis technology is promoted. A fault identification system based on WEKA data mining platform is designed to connect My SQL database. The storage of fault knowledge, the display of data stream and the access of database by calling My SQL in WEKA are completed.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH133;TP18
[Abstract]:How to mine the useful information from the massive data, find out the rule of reflecting the running condition of the mechanical equipment contained in the data, solve the difficult problem of complex diagnosis and modeling, and realize the intelligent recognition of the fault pattern. Become the current urgent need to solve the problem. However, the industrial data collected to reflect the running state of complex mechanical systems are often mixed with a large amount of noise, which has strong nonlinearity and coupling, which seriously affects the acquisition of effective information. At present, a single fault diagnosis model can not effectively make a comprehensive diagnosis of complex mechanical systems. In order to solve the above problems, the research work of rotor fault pattern recognition based on neighborhood rough set theory (Neighborhood Rough Set Theory,NRST) and other data-driven methods is carried out in this paper. The attribute reduction method of NRST and the method of NRST combined with statistical analysis and machine learning are discussed. The main work and results of this paper are as follows: (1) the definition of NRST and the method of forward greedy attribute reduction are introduced. The advantage that NRST can directly deal with continuous numerical attributes is fully utilized. This paper presents a method of constructing NRST decision table based on rotor power frequency doubling as conditional attribute and fault type as decision attribute, and analyzes the feasibility of constructing NRST decision table based on the frequency spectrum characteristics of typical fault categories. The experimental results also show that the feature attributes obtained by this method are more physical and avoid the loss of key attributes in the discretization process. (2) on the basis of NRST attribute reduction, In this paper, a method of classifying fault categories with NRST and Fisher discriminant (FDA) is proposed. The discriminant function and cumulative discriminant ability are obtained. The effects of quadratic reduction and redundancy removal on fault pattern recognition are discussed. The mapping of data from high dimension to low dimension is completed, and the effect of fault classification under low dimension is realized. The experimental results show that the method can achieve the same recognition accuracy in the case of less feature attributes, thus saving storage space to improve the computational efficiency. (3) in order to seek an efficient and accurate fault diagnosis method, At the same time, in order to discuss the effect of NRST attribute reduction on machine learning, a method of fault classification identification based on NRST combined with radial basis function neural network (RBFNetwork) is proposed. Gao Si standard function is selected as radial basis function. The center, width and connection weight of the basis function are determined by the self-organizing selection center method. The experimental results show that the method can obviously shorten the modeling time and improve the recognition accuracy. (4) in order to solve the difficult problem of knowledge storage and discovery, the development of intelligent diagnosis technology is promoted. A fault identification system based on WEKA data mining platform is designed to connect My SQL database. The storage of fault knowledge, the display of data stream and the access of database by calling My SQL in WEKA are completed.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH133;TP18
【參考文獻】
相關(guān)期刊論文 前10條
1 馬憲民;張興;張永強;;基于支持向量機與粗糙集的隔爆電動機故障診斷[J];工礦自動化;2017年02期
2 李輝;王毅;楊曉萍;賈嶸;羅興，
本文編號：2364147
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