本文選題：減速器 + 故障診斷　； 參考：《沈陽理工大學(xué)》2011年碩士論文

【摘要】：減速器是一種非常常見的傳動設(shè)備,它被廣泛地應(yīng)用在各種各樣的大型機械設(shè)備中,因此以減速器為研究對象進行故障診斷,不但具有代表性,而且具有十分重要的意義。 減速器是一種依靠齒輪進行能量傳遞的傳動裝置。在運行過程中,會隨著齒輪以及軸承的旋轉(zhuǎn)而產(chǎn)生一系列的振動,而其運行狀態(tài)的優(yōu)劣則可以直接通過對振動信號的分析而得出。 本文在對減速器的各種不同運行狀態(tài)進行學(xué)習(xí)的基礎(chǔ)上,深入研究了基于蟻群算法的減速器故障診斷方法,主要工作包括以下幾方面: (1)簡單介紹了蟻群算法的基本原理,在此基礎(chǔ)上以旅行商問題(TSP)為例闡述了蟻群算法的參數(shù)選取、性能評價等問題; (2)對減速器的常見故障形式及其產(chǎn)生原因進行了分析,并以齒輪為研究對象,實現(xiàn)了對齒輪故障現(xiàn)象的初步診斷; (3)在完成了對采集到的數(shù)據(jù)的進行了特征選取和歸一化處理,在此基礎(chǔ)上,將蟻群算法(ACO)和BP神經(jīng)網(wǎng)絡(luò)應(yīng)用到了減速器的故障診斷過程中,分別建立了基于蟻群算法的和基于BP神經(jīng)網(wǎng)絡(luò)的故障診斷模型,詳細闡述了基于蟻群算法的故障診斷模型的參數(shù)選取,并對兩個模型進行了訓(xùn)練學(xué)習(xí)、測試、性能分析和比較。 (4)設(shè)計完成了減速器故障診斷系統(tǒng),其主要功能包括:初期診斷模塊、精密診斷模塊、人機交互模塊等。該系統(tǒng)具有運算速度快,準確率高、易于維護等優(yōu)點。
[Abstract]:Reducer is a very common transmission equipment, it is widely used in a variety of large mechanical equipment, so it is not only representative, but also very important to take the reducer as the research object for fault diagnosis. A reducer is a transmission device that relies on gears for energy transfer. In the process of operation, a series of vibration will be produced with the rotation of the gear and bearing, and the advantages and disadvantages of the running state can be directly obtained by the analysis of the vibration signal. In this paper, based on the study of various running states of reducer, the fault diagnosis method of reducer based on ant colony algorithm is studied. The main work includes the following aspects: (1) the basic principle of ant colony algorithm is introduced, and the parameter selection of ant colony algorithm is illustrated by TSPS (traveling Salesman problem) as an example. The common fault forms and their causes of reducer are analyzed, and the gear is taken as the research object. The primary diagnosis of gear fault phenomenon is realized, and the feature selection and normalization processing of the collected data are completed, on the basis of which, The ant colony algorithm (ACO) and BP neural network are applied to the fault diagnosis of reducer, and the fault diagnosis models based on ant colony algorithm and BP neural network are established, respectively. The parameter selection of fault diagnosis model based on ant colony algorithm is described in detail, and the two models are trained, tested, analyzed and compared. Its main functions include: initial diagnosis module, precision diagnosis module, man-machine interaction module and so on. The system has the advantages of fast operation, high accuracy and easy maintenance.
【學(xué)位授予單位】：沈陽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3

【引證文獻】

相關(guān)碩士學(xué)位論文 前1條

1 施雷紅;基于信息融合的煤炭輸送機減速器故障診斷方法研究[D];江西理工大學(xué);2013年

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本文編號：2049134