本文關鍵詞： 順序離散事件機電系統(tǒng) 故障診斷 Petri網(wǎng) Bayes方法 故障預測　出處：《上海交通大學》2011年碩士論文　論文類型：學位論文

【摘要】：隨著現(xiàn)代科學技術的飛速發(fā)展,自動化裝備的功能日益強大,結構日趨復雜,故障的發(fā)生也逐漸頻繁,同時在許多大型復雜機電系統(tǒng)中存在著大量具有瞬時性,異步性,并帶有順序動作特點的離散事件機電系統(tǒng)。這類順序離散事件機電系統(tǒng)有其自身的特點,但缺乏相關的研究手段,為了適應系統(tǒng)可靠性和穩(wěn)定性的要求,有必要對這類順序離散事件機電系統(tǒng)的故障診斷方法進行研究。 研究歸納了順序離散事件機電系統(tǒng)的主要類型,分析了順序離散事件機電系統(tǒng)的故障特點,研究了其相應的建模方法�；陧樞螂x散事件機電系統(tǒng)的行為特點,分析了Petri網(wǎng)作為系統(tǒng)建模工具的依據(jù),并在此基礎上對三種主要類型建立基本Petri網(wǎng)模型,同時將時間統(tǒng)計信息引入Petri網(wǎng),對順序離散事件機電系統(tǒng)建立賦時Petri網(wǎng)模型。提出了基于Bayes試驗方法的故障診斷算法,對變遷的時間統(tǒng)計量進行顯著性檢驗,結合系統(tǒng)的先驗和后驗概率,在模塊化故障診斷思想的指導下,分層定位至故障源。分析了在該算法下兩類錯誤的概率,探討了在最小誤診率的情況下,閾值概率的最優(yōu)化問題,并推導了其計算方法。研究了微鉆檢測機的工作特點,分析了其作為順序離散事件機電系統(tǒng)故障診斷實驗平臺的可行性。在故障診斷的基礎上,研究了故障預測的方法,對順序離散事件機電系統(tǒng)建立了模糊Petri網(wǎng)模型,推導了庫所中托肯的模糊隸屬度函數(shù)計算方法,提出了基于模糊推理的故障預測方法。 在微鉆檢測設備上的實驗結果表明,所提故障診斷和預測方法是可行的,具有較高的故障診斷準確率,本文所研究的相關內(nèi)容可以為其他具有離散特征的工業(yè)故障診斷應用提供借鑒。
[Abstract]:With the rapid development of modern science and technology, the function of automation equipment is becoming more and more powerful, the structure is becoming more and more complex, and the faults occur frequently. At the same time, there are a large number of transient in many large complex electromechanical systems. Asynchronous discrete event electromechanical system with sequential action. This kind of sequential discrete event electromechanical system has its own characteristics, but lack of relevant research methods. In order to meet the requirements of system reliability and stability, it is necessary to study the fault diagnosis method of this kind of sequential discrete event electromechanical system. The main types of sequential discrete event electromechanical system are summarized and the fault characteristics of sequential discrete event electromechanical system are analyzed. Based on the behavior characteristics of sequential discrete event electromechanical system, the basis of Petri net as system modeling tool is analyzed. On this basis, the basic Petri net model is established for three main types, and the time statistics information is introduced into the Petri net at the same time. A timed Petri net model for sequential discrete event electromechanical systems is established. A fault diagnosis algorithm based on Bayes test method is proposed to test the significance of transition time statistics. Combined with the prior and posterior probability of the system, under the guidance of the modular fault diagnosis idea, the fault source is located in layers. The probability of two kinds of errors under the algorithm is analyzed, and the case of minimum misdiagnosis rate is discussed. The optimization problem of threshold probability and its calculation method are deduced, and the working characteristics of microdrill detector are studied. The feasibility of using it as an experimental platform for fault diagnosis of sequential discrete event electromechanical system is analyzed. On the basis of fault diagnosis, the method of fault prediction is studied. The fuzzy Petri net model for sequential discrete event electromechanical system is established. The calculation method of fuzzy membership function in the library is derived and the fault prediction method based on fuzzy reasoning is proposed. The experimental results on the microdrill detection equipment show that the proposed fault diagnosis and prediction method is feasible and has a higher fault diagnosis accuracy. The relevant contents of this paper can be used for reference for other industrial fault diagnosis applications with discrete characteristics.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

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