【摘要】：轉(zhuǎn)子作為旋轉(zhuǎn)機械的核心部件,對其進行故障檢測是非常重要的。轉(zhuǎn)子故障檢測是一門綜合多學科的技術。要對轉(zhuǎn)子進行故障檢測,必須進行信號的采集、處理和特征提取。轉(zhuǎn)子故障檢測主要依賴振動信號,轉(zhuǎn)子運行中產(chǎn)生的振動信號包含了豐富的信息。轉(zhuǎn)子振動信號由于受其它機械設備周期性振動及干擾信號的影響必須進行降噪。本文以轉(zhuǎn)子作為研究對象,選取轉(zhuǎn)子的典型故障作為例證,對其進行信號采集、分析和特征提取。 本文主要工作如下： 采用DSP TMS320F2812處理器作為平臺來設計轉(zhuǎn)子信號采集系統(tǒng)。介紹了TMS320F2812和AD7606-4的功能結(jié)構(gòu)及GPIO功能腳的設置,詳細闡述了它們之間引腳的連接方法和作用,以及采集系統(tǒng)的軟件功能。講述了并行模式下AD7606-4四通道同步采樣時序圖,采用了CPU定時器0中斷,控制A/D轉(zhuǎn)換器啟停。通過改變分頻系數(shù)的寄存器和預定計數(shù)常數(shù)的寄存器的值,實現(xiàn)改變采樣頻率。 采用Bayes樣本估計法獲取小波閾值,利用軟閾值函數(shù)對每層小波分解的細節(jié)系數(shù)進行處理,并逐層重構(gòu)處理后的系數(shù)獲得降噪后的信號,得到了一種實用的、具有自適應能力的轉(zhuǎn)子振動信號的小波降噪新方法。將Bayes閾值小波降噪處理結(jié)果與Donoho閾值法、Penalty閾值法、Birge-Massart閾值法的小波降噪處理結(jié)果進行比較,結(jié)合一些仿真和實例的驗證,表明使用此方法處理的信號較好地保留了原信號的細節(jié)部分,信噪比也有明顯提高。 轉(zhuǎn)子特征提取是轉(zhuǎn)子檢測的一個重要環(huán)節(jié)。研究了小波頻帶能量、小波包絡兩種特征量提取方法。用仿真的方法驗證了應用Mallat算法的正交小波分解頻率范圍的計算公式。對轉(zhuǎn)子信號小波分解,計算其各個頻帶的能量,對其進行歸一化處理作為特征量。在小波分解的故障頻帶內(nèi)采用Hilbert包絡譜提取故障特征頻率值及其頻譜獲取特征信號。根據(jù)特征向量判斷轉(zhuǎn)子故障。
[Abstract]:As the core component of rotating machinery, it is very important to detect its faults. Rotor fault detection is a multi-disciplinary technology. Signal acquisition, processing and feature extraction are necessary for rotor fault detection. Rotor fault detection mainly depends on vibration signal, which contains abundant information. The rotor vibration signal must be de-noised because of the periodic vibration and interference signal of other mechanical equipment. In this paper, the rotor is taken as the research object, the typical fault of the rotor is selected as an example, and the signal collection, analysis and feature extraction are carried out. The main work of this paper is as follows: using DSP TMS320F2812 processor as the platform to design the rotor signal acquisition system. The functional structure of TMS320F2812 and AD7606-4 and the setting of GPIO functional pin are introduced. The connecting method and function of pin between them and the software function of acquisition system are described in detail. The AD7606-4 four channel synchronous sampling sequence diagram in parallel mode is described. The CPU timer 0 interrupt is used to control the start and stop of the A / D converter. The sampling frequency is changed by changing the value of the register of the division coefficient and the register of the predetermined counting constant. The wavelet threshold is obtained by using Bayes sample estimation method, and the detail coefficients of each wavelet decomposition are processed by soft threshold function, and the de-noised signal is obtained by reconstructing the coefficients layer by layer, and a practical method is obtained. A new wavelet denoising method for rotor vibration signal with adaptive capability. The results of Bayes threshold wavelet denoising are compared with those of Donoho threshold method, Penalty threshold method and Birge-Massart threshold method. It shows that the signal processed by this method retains the details of the original signal, and the signal-to-noise ratio (SNR) is improved obviously. Rotor feature extraction is an important part of rotor detection. Wavelet band energy and wavelet envelope extraction methods are studied. The calculation formula of the frequency range of orthogonal wavelet decomposition using Mallat algorithm is verified by simulation. The rotor signal is decomposed by wavelet, the energy of each frequency band is calculated, and the signal is normalized as the eigenvalue. In the fault frequency band of wavelet decomposition, the Hilbert envelope spectrum is used to extract the characteristic frequency value of the fault and the spectrum to obtain the characteristic signal. The rotor fault is judged by eigenvector.
【學位授予單位】：華中農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3

【參考文獻】

相關期刊論文 前10條

1 姜少飛;閆英敏;趙霞;劉桂彬;;基于DSP和CAN總線的火炮隨動系統(tǒng)性能測試[J];四川兵工學報;2009年11期

2 任朝暉;馬輝;王德明;宋乃慧;;小波分析在轉(zhuǎn)子裂紋故障中的應用[J];東北大學學報(自然科學版);2007年04期

3 李才明;王良書;徐鳴潔;劉元生;鐘鍇;張善法;;基于小波能譜分析的巖溶區(qū)探地雷達目標識別[J];地球物理學報;2006年05期

4 師小紅;徐章遂;敦怡;;構(gòu)件裂紋缺陷的超聲識別[J];固體火箭技術;2007年06期

5 陳果;;基于小波分析的轉(zhuǎn)子故障信號自適應降噪技術研究[J];航空動力學報;2008年01期

6 胡海平,莫玉龍;基于貝葉斯估計的小波閾值圖像降噪方法[J];紅外與毫米波學報;2002年01期

7 張明;李開成;胡益勝;;基于Bayes估計的雙小波維納濾波電能質(zhì)量信號去噪算法[J];電力系統(tǒng)保護與控制;2011年04期

8 苗鋒;趙榮珍;;小波消噪和盲源分離在轉(zhuǎn)子故障信號分析中的應用方法[J];河南科技大學學報(自然科學版);2008年06期

9 趙道利,梁武科,羅興，

本文編號：2409931