【摘要】：本文基于國家自然科學(xué)基金資助項(xiàng)目—非線性旋轉(zhuǎn)機(jī)械轉(zhuǎn)子系統(tǒng)的突變故障預(yù)測(cè)研究（50975105）撰寫。隨著生產(chǎn)技術(shù)的高速發(fā)展，電力、機(jī)械等行業(yè)的設(shè)備日趨大型化和精密化，設(shè)備的健康狀況對(duì)企業(yè)的安全、經(jīng)濟(jì)生產(chǎn)具有重要意義。預(yù)測(cè)技術(shù)可以從設(shè)備歷史狀態(tài)參數(shù)的發(fā)展趨勢(shì)中挖掘其變化規(guī)律，對(duì)潛在的故障隱患進(jìn)行預(yù)報(bào)，為合理安排維修計(jì)劃提供技術(shù)支持，從而保證設(shè)備的安全運(yùn)行。 本文首先針對(duì)機(jī)械設(shè)備振動(dòng)狀態(tài)數(shù)據(jù)通常具有波動(dòng)性的問題，提出了一種利用馬爾科夫方法對(duì)灰色預(yù)測(cè)結(jié)果修正的預(yù)測(cè)模型。首先利用灰色等維新息GM(1,1)模型對(duì)樣本數(shù)據(jù)進(jìn)行灰預(yù)測(cè)，根據(jù)狀態(tài)實(shí)測(cè)數(shù)據(jù)與其灰預(yù)測(cè)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(duì)設(shè)備狀態(tài)進(jìn)行預(yù)測(cè)的時(shí)候，利用馬爾科夫狀態(tài)轉(zhuǎn)移概率矩陣和當(dāng)前狀態(tài)的誤差百分比狀態(tài)向量計(jì)算得到馬爾科夫修正值，對(duì)灰色預(yù)測(cè)結(jié)果進(jìn)行修正，實(shí)現(xiàn)對(duì)波動(dòng)狀態(tài)參數(shù)的預(yù)測(cè)；此外，本文從尋求新方法的角度出發(fā)，通過對(duì)蟻群算法的學(xué)習(xí)，建立了基于蟻群算法的信號(hào)重構(gòu)波動(dòng)運(yùn)行狀態(tài)參數(shù)預(yù)測(cè)模型，首先對(duì)波動(dòng)性數(shù)據(jù)使用信號(hào)重構(gòu)處理之后讓其在一定范圍內(nèi)波動(dòng)，然后采用蟻群算法中信息素的思想來對(duì)數(shù)據(jù)進(jìn)行預(yù)測(cè)；最后，本文介紹了作者在研究生期間的主要科研項(xiàng)目-國內(nèi)某汽車制造廠商的發(fā)動(dòng)機(jī)工廠設(shè)備點(diǎn)檢與維修信息管理系統(tǒng)，并介紹在該系統(tǒng)中如何實(shí)現(xiàn)對(duì)設(shè)備的故障預(yù)測(cè)。 通過對(duì)潛油泵的實(shí)例證明，首先本文所建立的灰色-馬爾科夫波動(dòng)運(yùn)行參數(shù)預(yù)測(cè)模型，不論在數(shù)據(jù)預(yù)測(cè)精度還是在對(duì)波動(dòng)性數(shù)據(jù)的趨勢(shì)預(yù)測(cè)上都有不錯(cuò)的效果；其次基于蟻群算法的信號(hào)重構(gòu)波動(dòng)運(yùn)行狀態(tài)參數(shù)預(yù)測(cè)模型作為一種新的預(yù)測(cè)模型具有很高的預(yù)測(cè)精度，，而且對(duì)數(shù)據(jù)趨勢(shì)的預(yù)測(cè)也具有較好的結(jié)果；最后通過在系統(tǒng)中建立預(yù)測(cè)模型，將預(yù)測(cè)技術(shù)用到實(shí)際的企業(yè)中，為工程師對(duì)設(shè)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(xué)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(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TH17

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