本文選題：轉(zhuǎn)子系統(tǒng) + 量化特征提取��； 參考：《蘭州理工大學(xué)》2012年碩士論文

【摘要】：旋轉(zhuǎn)機械是工業(yè)部門中應(yīng)用最為廣泛的一類機械設(shè)備，其核心部件為轉(zhuǎn)子-軸承系統(tǒng)。利用振動信號對轉(zhuǎn)子-軸承系統(tǒng)的運行進行實時監(jiān)測、分析與診斷，是保證旋轉(zhuǎn)機械穩(wěn)定、高效運作的重要措施。但隨著旋轉(zhuǎn)機械轉(zhuǎn)速的提高和新材料、新結(jié)構(gòu)的出現(xiàn)，傳統(tǒng)的線性理論已不能滿足現(xiàn)代轉(zhuǎn)子系統(tǒng)故障分析的需要，非線性理論和方法在轉(zhuǎn)子故障診斷的研究中占有愈加重要的地位。然而，現(xiàn)有成果仍存在難以系統(tǒng)歸納可用于故障識別的非線性特征、難以對信號給出精確的量化評價結(jié)果的缺陷，一定程度上降低了故障識別的準(zhǔn)確性�；诖耍疚尼槍D(zhuǎn)子典型故障的動力學(xué)模型，運用混沌理論與信息熵方法，對其振動信號的非線性狀態(tài)特征的量化提取問題開展了研究。主要工作內(nèi)容及獲得的研究結(jié)論如下： (1)通過對不同慢變參數(shù)下系統(tǒng)數(shù)學(xué)模型的建立和演算，完成了基于混沌理論的典型故障轉(zhuǎn)子動力學(xué)行為分析，并歸納了四種故障在不同轉(zhuǎn)速域內(nèi)的動力學(xué)響應(yīng)差異特征，且該差異特征在系統(tǒng)故障慢變過程下仍保持一致性，因此能夠作為轉(zhuǎn)子故障模式辨識所需的特征信息，為本研究所提出方法的合理性做出了證明。 (2)根據(jù)轉(zhuǎn)子故障非線性振動信號處理的要求，設(shè)計了局部投影濾波的改進算法，將鄰域半徑和噪聲子空間維數(shù)等參數(shù)的自適應(yīng)選擇環(huán)節(jié)加入原算法中，以充分保留強噪聲背景下系統(tǒng)的混沌運動信號。將該算法用于轉(zhuǎn)子試驗臺采集的故障信號降噪，獲得了較為理想的結(jié)果。 (3)針對實際復(fù)雜不可建模系統(tǒng)的情況，通過計算不同故障下轉(zhuǎn)子隨轉(zhuǎn)速變化的關(guān)聯(lián)維數(shù)和最大Lyapunov指數(shù)，反映其不同的動力學(xué)行為狀態(tài)，并利用故障轉(zhuǎn)子模型仿真序列及實驗采集數(shù)據(jù)的分析，對該方法的有效性進行了驗證。其結(jié)果說明，系統(tǒng)的吸引子不變量可準(zhǔn)確反映出其非線性運動的狀態(tài)和程度，是實際復(fù)雜系統(tǒng)非線性特性識別的有效特征參數(shù)。 (4)對轉(zhuǎn)子故障非線性特征差異較大的一階臨界轉(zhuǎn)速域和高頻轉(zhuǎn)速域進行吸引子不變量信息熵的量化提取，，得出其熵帶和熵值分量譜，作為區(qū)分四種典型故障的依據(jù)。同時，研究了利用熵值分量譜圖反映故障嚴重程度的方法，為采用非線性理論進行轉(zhuǎn)子系統(tǒng)的故障模式辨識提供了一種新型圖像依據(jù)。 研究表明，機械系統(tǒng)非線性動力特征的量化提取作為機械故障診斷的新趨勢，具有很大的研究空間和研究價值，同時也為智能故障診斷，即數(shù)據(jù)驅(qū)動技術(shù)在故障診斷中的應(yīng)用奠定了基礎(chǔ)。
[Abstract]:Rotating machinery is the most widely used type of mechanical equipment in the industrial sector, its core component is rotor-bearing system. Using vibration signals to monitor, analyze and diagnose the rotor-bearing system in real time is an important measure to ensure the stable and efficient operation of the rotating machinery. However, with the increase of rotating speed and the emergence of new materials and new structures, the traditional linear theory can not meet the needs of modern rotor system fault analysis. Nonlinear theory and methods play an increasingly important role in rotor fault diagnosis. However, the existing results still have the defect that it is difficult to systematically sum up the nonlinear features that can be used in fault identification, and it is difficult to give accurate quantitative evaluation results for the signal, which reduces the accuracy of fault identification to a certain extent. Based on this, the dynamic model of typical rotor faults is studied in this paper. The problem of quantification extraction of nonlinear state characteristics of rotor vibration signals is studied by using chaos theory and information entropy method. The main contents and conclusions of the study are as follows: 1) through the establishment and calculation of the mathematical model of the system under different slowly varying parameters, the dynamic behavior analysis of typical fault rotor based on chaos theory is completed, and the dynamic response characteristics of four kinds of faults in different rotational speed domain are summarized. The differential feature is consistent in the process of system fault slow change, so it can be used as the characteristic information needed for rotor fault mode identification, which proves the rationality of the method proposed in this paper. 2) according to the requirement of nonlinear vibration signal processing of rotor fault, an improved algorithm of local projection filter is designed. The adaptive selection of parameters such as neighborhood radius and noise subspace dimension is added to the original algorithm. In order to fully retain the chaotic motion signal of the system under the strong noise background. The algorithm is applied to the noise reduction of the fault signal collected by the rotor test-bed, and the better results are obtained. In view of the actual complex and unmodeled system, by calculating the correlation dimension and the maximum Lyapunov exponent of rotor speed variation under different faults, the dynamic behavior of the rotor is reflected. The validity of the method is verified by analyzing the simulation sequence of the fault rotor model and the data collected from the experiment. The results show that the attractor invariant of the system can accurately reflect the state and degree of its nonlinear motion and is an effective characteristic parameter for the identification of nonlinear characteristics of practical complex systems. In this paper, the information entropy of the first order critical speed domain and the high frequency speed domain of rotor fault is extracted by quantifying the information entropy of the attractor invariant, and the entropy band and entropy component spectrum are obtained, which can be used as the basis for distinguishing the four typical faults. At the same time, the method of using entropy component spectrum to reflect the degree of fault severity is studied, which provides a new image basis for fault mode identification of rotor system based on nonlinear theory. As a new trend of mechanical fault diagnosis, the quantitative extraction of nonlinear dynamic features of mechanical system has great research space and value, and it is also a kind of intelligent fault diagnosis. That is, the application of data-driven technology in fault diagnosis laid the foundation.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3

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