【摘要】：本文基于國家自然科學基金資助項目—非線性旋轉(zhuǎn)機械轉(zhuǎn)子系統(tǒng)的突變故障預測研究（50975105）撰寫。隨著生產(chǎn)技術(shù)的高速發(fā)展，電力、機械等行業(yè)的設備日趨大型化和精密化，設備的健康狀況對企業(yè)的安全、經(jīng)濟生產(chǎn)具有重要意義。預測技術(shù)可以從設備歷史狀態(tài)參數(shù)的發(fā)展趨勢中挖掘其變化規(guī)律，對潛在的故障隱患進行預報，為合理安排維修計劃提供技術(shù)支持，從而保證設備的安全運行。 本文首先針對機械設備振動狀態(tài)數(shù)據(jù)通常具有波動性的問題，提出了一種利用馬爾科夫方法對灰色預測結(jié)果修正的預測模型。首先利用灰色等維新息GM(1,1)模型對樣本數(shù)據(jù)進行灰預測，根據(jù)狀態(tài)實測數(shù)據(jù)與其灰預測結(jié)果之間的誤差百分比劃分馬爾科夫狀態(tài)區(qū)間，建立馬爾科夫狀態(tài)轉(zhuǎn)移概率矩陣。在對設備狀態(tài)進行預測的時候，利用馬爾科夫狀態(tài)轉(zhuǎn)移概率矩陣和當前狀態(tài)的誤差百分比狀態(tài)向量計算得到馬爾科夫修正值，對灰色預測結(jié)果進行修正，實現(xiàn)對波動狀態(tài)參數(shù)的預測；此外，本文從尋求新方法的角度出發(fā)，通過對蟻群算法的學習，建立了基于蟻群算法的信號重構(gòu)波動運行狀態(tài)參數(shù)預測模型，首先對波動性數(shù)據(jù)使用信號重構(gòu)處理之后讓其在一定范圍內(nèi)波動，然后采用蟻群算法中信息素的思想來對數(shù)據(jù)進行預測；最后，本文介紹了作者在研究生期間的主要科研項目-國內(nèi)某汽車制造廠商的發(fā)動機工廠設備點檢與維修信息管理系統(tǒng)，并介紹在該系統(tǒng)中如何實現(xiàn)對設備的故障預測。 通過對潛油泵的實例證明，首先本文所建立的灰色-馬爾科夫波動運行參數(shù)預測模型，不論在數(shù)據(jù)預測精度還是在對波動性數(shù)據(jù)的趨勢預測上都有不錯的效果；其次基于蟻群算法的信號重構(gòu)波動運行狀態(tài)參數(shù)預測模型作為一種新的預測模型具有很高的預測精度，，而且對數(shù)據(jù)趨勢的預測也具有較好的結(jié)果；最后通過在系統(tǒng)中建立預測模型，將預測技術(shù)用到實際的企業(yè)中，為工程師對設備狀態(tài)的判斷提供更多的信息，使得判斷結(jié)果更加具有科學性，更加具有可信性。
[Abstract]:This paper is based on the sudden fault prediction of nonlinear rotating machinery rotor system, which is supported by the National Natural Science Foundation of China (50975105). With the rapid development of production technology, the equipment in power, machinery and other industries is becoming larger and more refined. The health of the equipment is of great significance to the safety and economic production of enterprises. The prediction technology can excavate the change law from the development trend of the historical state parameters of the equipment, predict the potential hidden trouble, and provide technical support for the reasonable arrangement of the maintenance plan, so as to ensure the safe operation of the equipment. In this paper, a prediction model based on Markov method is proposed to modify the grey prediction results in order to solve the problem that the vibration state data of mechanical equipment are usually fluctuating. Firstly, the grey equal dimension innovation GM (1, 1) model is used to predict the sample data. According to the error percentage between the measured state data and the grey prediction results, the Markov state interval is divided, and the Markov state transition probability matrix is established. When predicting the state of the equipment, the Markov correction value is obtained by using the Markov state transition probability matrix and the error percentage state vector of the current state, and the grey prediction results are modified to realize the prediction of the fluctuation state parameters. In addition, from the point of view of finding new methods, through the study of ant colony algorithm, a prediction model of wave operating state parameters based on ant colony algorithm is established. Firstly, the volatility data is reconstructed and then fluctuated in a certain range, and then the idea of pheromone in ant colony algorithm is used to predict the data. Finally, this paper introduces the equipment spot inspection and maintenance information management system of an automobile manufacturer in China, which is the main scientific research project of the author during the graduate period, and introduces how to realize the fault prediction of the equipment in the system. Through the example of submersible oil pump, it is proved that the grey-Markov fluctuation operation parameter prediction model established in this paper has a good effect both in the accuracy of data prediction and in the trend prediction of volatility data. Secondly, the signal reconstruction fluctuation state parameter prediction model based on ant colony algorithm has high prediction accuracy as a new prediction model, and also has good results for the prediction of data trends. Finally, by establishing the prediction model in the system, the prediction technology is applied to the actual enterprise, which provides more information for the engineer to judge the state of the equipment, and makes the judgment result more scientific and credible.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TH17

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