本文關鍵詞：不完備信息下基于流向圖的齒輪故障診斷方法研究　出處：《哈爾濱工業(yè)大學》2017年博士論文　論文類型：學位論文

  更多相關文章： 故障診斷 齒輪 不完備信息 流向圖 知識獲取 模式識別

【摘要】：齒輪作為旋轉(zhuǎn)機械的重要組成部分,已被廣泛應用于電站、直升機和重型卡車等設備中。由于齒輪的運行環(huán)境惡劣、工況復雜,使得齒輪本身狀態(tài)信息表露不完備。而且人類實踐活動總是受到客觀環(huán)境和條件的限制,所獲得的描述齒輪故障模式的診斷信息常有某種程度的不完備。本文首先將量化特征關系用于解決齒輪不完備診斷信息處理的問題。然后,以流向圖理論為基礎,提出了不完備信息下齒輪故障診斷知識表示方法、不完備信息下齒輪故障診斷知識獲取方法,以及齒輪齒故障模式識別方法。最后,以齒輪箱實驗組件為研究對象進行驗證。本文主要包括以下幾個方面的內(nèi)容:現(xiàn)有的廣義不可分辨關系大多僅能采用一種語意來理解、分析和處理不完備信息;無法理解、分析和處理由多種原因造成的齒輪不完備故障診斷信息。為此,本文在特征關系的基礎上,給出了一種實例間相似度的計算方法。對特征關系進行改進,給出了一種量化特征關系。針對齒輪不完備故障診斷信息,提出了基于量化特征關系的齒輪不完備故障診斷信息處理方法。采用自動變速箱故障診斷信息的處理實例驗證該方法的實用性、有效性和準確性�，F(xiàn)有的不完備信息下齒輪故障診斷知識表示方法無法直觀地表示各故障屬性值,以及故障征兆屬性值和故障決策屬性值之間的依賴關系。難于定量描述屬性間的依賴程度。為此,本文在流向圖的基礎上,給出了不完備流向圖的定義。根據(jù)不完備流向圖的定義,給出了一種不完備流向圖的構(gòu)建算法。針對齒輪不完備故障診斷信息的知識表示問題,提出了一種基于流向圖的齒輪不完備故障診斷信息的知識表示方法。結(jié)合知識表示實例驗證該方法可直觀地表示包含三種未知屬性值不完備故障診斷信息,定量描述屬性值之間的依賴程度,便于用戶理解和分析�，F(xiàn)有不完備信息下齒輪故障診斷知識獲取方法大多僅能從包含一種未知屬性值的不完備信息中獲取故障診斷知識。個別方法能從同時包含兩種未知屬性值的不完備信息中提取故障診斷知識,但知識獲取過程抽象難于理解。為此,本文在量化特征關系的基礎上,給出了一種基于量化特征關系的分配約簡算法。借鑒基于量化特征關系的分配約簡算法,給出了一種不完備流向圖的屬性約簡算法。針對齒輪不完備故障診斷信息的知識獲取問題,提出了一種基于流向圖的齒輪不完備故障診斷信息的知識獲取方法。通過實例驗證了此方法能夠從同時包含三種未知屬性值的不完備信息系統(tǒng)中直接獲取故障診斷知識;且知識獲取結(jié)果直觀、簡潔與清晰�，F(xiàn)有的齒輪故障模式識別方法無法直觀地表示出故障征兆屬性值,以及屬性值之間的依賴關系。有些方法的識別模型過于復雜,識別過程依然較晦澀難于理解。為此,本文給出了流向圖中節(jié)點條件概率的定義、先驗概率的定義,以及完整路徑后驗概率的定義,實現(xiàn)流向圖的概率化。在流向圖概率化的基礎上,給出了一種基于流向圖的模式識別算法,進而提出了一種基于流向圖的齒輪故障模式識別方法。實驗結(jié)果表明該方法可準確地識別齒輪故障模式,而且模式識別模型的結(jié)構(gòu)簡單,模式識別策略清晰。以Spectra Quest公司開發(fā)的齒輪實驗組件為研究對象,利用振動分析法和油樣分析法提取的齒輪不完備故障診斷信息,驗證了本文提出的不完備信息下基于流向圖的齒輪故障診斷方法。建立齒輪不完備故障診斷信息系統(tǒng),采用量化特征關系對該信息系統(tǒng)進行處理。構(gòu)建齒輪不完備故障診斷流向圖,并從中獲取故障診斷知識。通過基于流向圖的模式識別算法判斷待診樣本的故障類型。實驗結(jié)果表明此方法具有非常高的準確率,而且故障診斷過程直觀,故障診斷策略清晰。
[Abstract]:Gear is an important part of the rotating machinery has been widely used in power plants, helicopters and heavy trucks and other equipment. Because of the operation environment of gear bad conditions are complex, so that the gear state information disclosure is not complete. But human activities have always been the objective environment and conditions, the fault diagnosis of gear model description information obtained is incomplete to some degree. This paper will be used to solve the relationship between the quantitative characteristics of gear incomplete diagnosis information processing. Then, the flow graph theory, the incomplete information of gear fault diagnosis knowledge representation method, incomplete information of gear fault diagnosis knowledge acquisition method, and the gear fault the pattern recognition method. Finally, the gear box test module as the object of study is verified. This paper mainly includes the following aspects: the existing The generalized indiscernibility relation most can only adopt a semantic analysis to understand and deal with incomplete information; to understand, analyze and deal with by a variety of causes of gear incomplete fault diagnosis information. Therefore, based on the characteristics of the relationship, a method of calculating the similarity between the examples are given. The characteristics of the relationship improved, gives a quantitative relationship. According to the characteristics of gear fault diagnosis of incomplete information, put forward the relationship between the quantitative characteristics of incomplete gear fault diagnosis method based on information processing. Based on an example of fault diagnosis information processing automatic transmission to verify the practicability of the method. The accuracy and effectiveness of the existing gear under incomplete information fault diagnosis knowledge representation methods cannot directly represent the fault attribute value and attribute value relation between fault symptoms and fault decision attribute values to quantitative. Describe the degree of dependence between attributes. Therefore, based on the flow pattern, gives the definition of incomplete flow graph. According to the definition of incomplete flow graph, gives the algorithm to construct a complete flow chart. According to incomplete information of the gear fault diagnosis knowledge representation problem, this paper proposes a new method flow chart of the incomplete gear fault diagnosis information based on knowledge. Combined with the knowledge representation example shows that this method can intuitively represent contains three unknown attribute values of incomplete fault diagnosis information, quantitative description of dependence between attribute values, allowing users to understand and analyze the existing incomplete information of gear fault diagnosis knowledge acquisition methods are only from contains a fault diagnosis knowledge acquisition in incomplete information of unknown attribute values. The individual method can also contain two kinds of extraction from incomplete information of unknown attribute values Fault diagnosis knowledge, but the knowledge acquisition process is abstract and difficult to understand. Therefore, based on the quantitative characteristics of relations, presents a distribution reduction algorithm based on the relationship between the quantitative characteristics. From distribution reduction algorithm based on the relationship between the quantitative characteristics, gives an incomplete flow diagram. Attribute reduction algorithm to extract gear incomplete fault diagnosis information knowledge, proposes a method of obtaining the flow graph of gear incomplete fault diagnosis information based on knowledge. Through the example proves the method can directly obtain the fault diagnosis knowledge from also contains three unknown attribute values in incomplete information system; knowledge acquisition and intuitive results, concise and clear. Gear fault pattern recognition of the existing methods can not clearly shown that the fault attribute value and attribute value dependencies between. Some methods of identifying model In the complex, the recognition process is still obscure and difficult to understand. Therefore, this paper gives a definition of the Condition node flow graph probability, defined a priori probability, and the definition of the full path of the posterior probability of the flow graph. Based on the probability of the flow chart, a pattern recognition algorithm based on flow graphs given, then put forward a kind of gear fault pattern recognition method based on flow graphs. The experimental results show that this method can accurately identify the gear fault pattern and structural pattern recognition model, pattern recognition strategy clear. In gear test component development Spectra of Quest company as the research object, analysis extraction using vibration analysis method the gear oil sample and incomplete fault diagnosis information, gear fault diagnosis method based on flow graphs are verified under incomplete information is proposed in this paper. The establishment of incomplete gear fault diagnosis Fault information system, the relationship between the quantitative characteristics of the information system construction of gear processing. Incomplete fault diagnosis flow chart, and obtain the fault diagnosis knowledge from it. By judging the fault type diagnosis for sample pattern recognition flow graph based algorithm. Experimental results show that this method has very high accuracy rate, and the process of fault diagnosis intuitively, the fault diagnosis strategy is clear.

【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TH132.41
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本文編號：1393759