【摘要】：旋轉(zhuǎn)機械運行狀態(tài)的好壞,會直接影響系統(tǒng)的工作性能。本文對旋轉(zhuǎn)機械的故障檢測技術(shù)和方法進行研究,針對振動信號存在的非線性特性,研究了非線性評價指標;探討了信號分解對降低非線性程度的影響,及降低非線性程度的方法;綜合運用信號分解、時間序列建模、隱馬爾科夫等理論,構(gòu)建故障檢測模型,對旋轉(zhuǎn)機械故障做出精確判斷,為確保旋轉(zhuǎn)機械正常工作具有重要意義。主要研究內(nèi)容如下:研究了振動信號的非線性特性,確定了嵌入維數(shù)、延遲時間兩個重要參數(shù)。采用混沌與分形理論,對非線性評價指標進行研究,給出最大Lyapunov指數(shù)、柯爾莫哥洛夫熵、關(guān)聯(lián)維數(shù)、盒維數(shù)計算方法。采用信號分解手段降低非線性程度,比較了信號經(jīng)小波分解和集成經(jīng)驗?zāi)B(tài)分解后的非線性度強弱,提出二者結(jié)合的振動信號去噪方法。構(gòu)建振動信號故障檢測模型,采用時間序列建模的方法,精確提取能夠表征故障的特征。振動信號經(jīng)過集成經(jīng)驗?zāi)B(tài)分解后,計算混沌與分形的數(shù)值特征,根據(jù)非線性強弱評價指標,判斷分解信號的非線性程度。針對分解后的線性分量,建立線性模型,提取線性模型參數(shù);針對分解后的非線性分量,構(gòu)建Volterra模型,提取Volterra模型參數(shù)。在深入研究非線性特征提取的基礎(chǔ)上,探討了HMM技術(shù)的實現(xiàn)方法,提出采用H MM模型進行故障識別。對旋轉(zhuǎn)機械的軸承信號進行實驗分析,將信號分解后提取的線性、非線性特征量輸入到HMM模型中,對正常、內(nèi)環(huán)故障、外環(huán)故障、滾動體故障這四種信號進行模式識別,實驗結(jié)果表明該模型能夠準確識別旋轉(zhuǎn)機械故障,且識別率高。
[Abstract]:The running state of rotating machinery will directly affect the working performance of the system. In this paper, the fault detection techniques and methods of rotating machinery are studied, and the nonlinear evaluation index of vibration signal is studied, and the influence of signal decomposition on the reduction of nonlinear degree is discussed. And the methods of reducing nonlinear degree, synthetically using the theory of signal decomposition, time series modeling, hidden Markov and so on, to construct the fault detection model, and to make accurate judgment on the fault of rotating machinery. In order to ensure the normal operation of rotating machinery has important significance. The main contents are as follows: the nonlinear characteristics of vibration signal are studied, and two important parameters, embedding dimension and delay time, are determined. The nonlinear evaluation index is studied by using chaos and fractal theory. The calculation methods of maximum Lyapunov exponent, Kolmogorov entropy, correlation dimension and box dimension are given. Using signal decomposition to reduce the degree of nonlinearity, the degree of nonlinearity after wavelet decomposition and integrated empirical mode decomposition is compared, and a method of vibration signal denoising is proposed. The fault detection model of vibration signal is constructed, and the time series modeling method is used to accurately extract the characteristics that can represent the fault. After integrated empirical mode decomposition, the numerical characteristics of chaos and fractal are calculated, and the degree of nonlinearity of the decomposed signal is judged according to the evaluation index of nonlinear intensity. For the decomposed linear component, the linear model is established to extract the parameters of the linear model, and for the decomposed nonlinear component, the Volterra model is constructed to extract the parameters of the Volterra model. Based on the research of nonlinear feature extraction, the realization method of HMM technology is discussed, and the method of fault identification based on hmm model is proposed. Through the experimental analysis of bearing signals of rotating machinery, the extracted linear and nonlinear eigenvalues are input into the HMM model after signal decomposition, and the four signals, namely normal, inner, outer and rolling faults, are recognized by pattern recognition. The experimental results show that the model can accurately identify the rotating machinery faults, and the recognition rate is high.
【學(xué)位授予單位】：天津理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH17

【參考文獻】

相關(guān)期刊論文 前6條

1 張永宏;陶潤U，

本文編號：2152329