本文選題：特征選擇 + 智能診斷��； 參考：《蘭州理工大學》2013年碩士論文

【摘要】：旋轉機械是電力、化工等行業(yè)生產中的關鍵設備,對旋轉機械設備進行狀態(tài)監(jiān)測和故障診斷以保證設備的安全、可靠運行具有重要的經濟價值。隨著信息技術、現(xiàn)代控制理論和人工智能的發(fā)展,自動化和智能化逐漸成為故障診斷系統(tǒng)的發(fā)展方向。然而故障診斷技術發(fā)展到現(xiàn)在,仍然面臨著支撐理論存在局限性、規(guī)則獲取困難、診斷模型難以實際應用等各種問題。 本研究針對在轉子故障診斷中,特征的選擇缺乏可靠的依據、多通道信息融合造成的特征冗余問題以及故障識別精度不高的問題,研究了基于K-W檢驗特征選擇與CRFS識別的故障診斷模型在轉子故障診斷中的應用,開辟了轉子故障診斷的新途徑。本文的主要工作和研究結論如下： 1)針對常用的故障特征種類繁多,對其選擇缺乏可靠依據和針對性不強的問題,通過對不同故障類型樣本同一特征的數(shù)據區(qū)間進行分析,篩選出了數(shù)據區(qū)間重疊較小的具備較強表征轉子不同運行狀態(tài)能力的特征。以此為依據,建立了轉子原始故障特征數(shù)據集。 2)針對多通道監(jiān)測、數(shù)據采集能夠更加全面的反映轉子運行狀態(tài),但對故障特征集的建立造成了維數(shù)過高、不同類別子集間可分性差的問題,研究了K-W檢驗特征選擇方法,簡化了算法并與主成分分析(PCA)進行對比分析,實驗驗的證結果表明,K-W檢驗特征選擇的結果空間聚類緊致,降維效果和算法復雜度均優(yōu)于主成分分析法。 3)將條件隨機場模型(CRFS)引入到轉子故障診斷中,并將經過特征優(yōu)化后的數(shù)據輸入CRFS進行參數(shù)學習和樣本訓練,實驗結果表明條件隨機場穩(wěn)定性好且識別準確率高,大量特征疊加對識別精度的影響并不大。通過與HMM模型對比分析表明,在條件復雜、故障種類較多以及特征相似時HMM模型診斷率會明顯下降且訓練較慢,而CRFS所有特征可以進行全局歸一化,能夠求得全局的最優(yōu)解,在多故障診斷中表現(xiàn)出了優(yōu)良的性能。 4)利用虛擬儀器技術開發(fā)了一套集實時狀態(tài)監(jiān)測、預警、振動信號分析、振動數(shù)據和特征數(shù)據存儲、報表自動生成及遠程網絡訪問等功能于一體的機械振動自動測試分析信息系統(tǒng)。在實驗研究以及工程應用中均取得了較好的效果。 研究表明,通過特征降維、特征選擇能夠獲得具備較好的類別可分性的故障特征向量,故如何在智能故障診斷算法研究中獲得新突破,及如何將智能診斷算法合理地嵌入于自動化測控系統(tǒng),將是故障診斷領域開展研究工作的重要方向。
[Abstract]:Rotating machinery is the key equipment in the production of electric power and chemical industry. It is of great economic value to monitor and diagnose the status of rotating machinery and equipment to ensure the safety and reliable operation of the equipment. With the development of information technology, modern control theory and artificial intelligence, automation and intelligence are becoming the developing direction of fault diagnosis system. However, with the development of fault diagnosis technology, it still faces many problems, such as the limitation of supporting theory, the difficulty of obtaining rules, the difficulty of practical application of diagnostic model, and so on. This study aims at the lack of reliable basis for feature selection in rotor fault diagnosis, the problem of feature redundancy caused by multi-channel information fusion and the problem of low fault identification accuracy. The application of fault diagnosis model based on K-W test feature selection and CRFS identification in rotor fault diagnosis is studied, which opens a new way for rotor fault diagnosis. The main work and conclusions of this paper are as follows: 1) aiming at the problems of the variety of common fault features, the lack of reliable basis for their selection and the lack of pertinence, the data interval of the same feature of different fault types is analyzed. The characteristics of the rotor with little overlap in the data interval are selected, which can represent the different running states of the rotor. Based on this, the original fault feature data set of rotor is established. 2) in view of multi-channel monitoring, data acquisition can reflect rotor running state more comprehensively, but the establishment of fault feature set leads to the problems of high dimension and poor separability among different subsets, so the K-W test feature selection method is studied. The algorithm is simplified and compared with the principal component analysis (PCA). The experimental results show that the result space of the feature selection of K-W test is compact, the dimension reduction effect and the complexity of the algorithm are better than that of the principal component analysis. 3) the conditional random field model (CRFs) is introduced into rotor fault diagnosis, and the data after feature optimization is input into CRFS for parameter learning and sample training. The experimental results show that the conditional random field has good stability and high recognition accuracy. The superposition of a large number of features has little effect on the recognition accuracy. Compared with HMM model, it is shown that the diagnostic rate of HMM model decreases obviously and the training is slower when the conditions are complex, there are many kinds of faults and the characteristics are similar, while all the features of CRFS can be normalized globally and the global optimal solution can be obtained. It shows excellent performance in multi-fault diagnosis. 4) A set of real-time state monitoring, early warning, vibration signal analysis, vibration data and feature data storage are developed by using virtual instrument technology. Automatic test and analysis information system for mechanical vibration with automatic report generation and remote network access. Good results have been obtained in both experimental research and engineering application. The research shows that feature selection can obtain fault feature vector with better classification by feature reduction, so how to make a breakthrough in intelligent fault diagnosis algorithm. And how to embed the intelligent diagnosis algorithm into the automatic measurement and control system reasonably will be an important research direction in the field of fault diagnosis.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TH165.3

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