【摘要】：貨運列車滾動軸承故障診斷技術(shù)是一門緊密結(jié)合實際的工程科學(xué),生產(chǎn)實際的需要是它發(fā)展的根本原因,因此研究簡便的診斷方法具有廣闊的實用價值。本文從貨運列車滾動軸承噪聲產(chǎn)生和傳播的理論分析入手,綜合考慮貨運列車滾動軸承聲音的衰減和背景噪聲的影響,針對貨運列車滾動軸承聲信號非平穩(wěn)性這一特點,采用現(xiàn)代信號分析技術(shù)對聲信號進(jìn)行處理和故障識別,以提高其診斷識別的有效性和可靠性。本文主要研究以下幾個方面： 針對貨運列車滾動軸承聲信號的非平穩(wěn)性,本文利用小波變換的多分辨率性質(zhì)來分析聲信號,發(fā)現(xiàn)沖擊成分在小波分解的細(xì)節(jié)信號中得到放大,對比該頻率和各種故障下形成地故障頻率找到故障的原因,從而實現(xiàn)對信號波形有效地識別。本文提出一種基于非線性小波變換的去噪方法—分層閾值去噪算法。仿真結(jié)果表明,該方法能顯著提高濾波精度,在有效去除噪聲的同時,能很好地保留信號的主要細(xì)節(jié)。然后通過對經(jīng)小波變換后的信號進(jìn)行自功率譜密度分析,仿真結(jié)果表明,基于小波變換的自功率譜密度分析能有效地提取貨運列車滾動軸承故障聲信號的特征頻率,適合聲信號這樣的非平穩(wěn)信號的分析與研究。在特征提取方面,本文又提出了一種新方法—基于頻段局部能量的區(qū)間小波包特征提取,它可以根據(jù)需要細(xì)分各個頻帶。經(jīng)實踐驗證這些特征因子可以很好地代表滾動軸承的工作狀況。 研究了神經(jīng)網(wǎng)絡(luò)在貨運列車滾動軸承智能診斷方面的應(yīng)用�；诼曇粜盘柗治龅墓收咸卣魈崛》椒ê芏�,但每種方法都只在某一方面反映了故障特點,單獨應(yīng)用診斷效果不是很好。本文通過對比以不同方法提取的故障特征組合作為神經(jīng)網(wǎng)絡(luò)的輸入,最終確定利用小波分析和神經(jīng)網(wǎng)絡(luò)相結(jié)合的方法對貨運列車滾動軸承進(jìn)行故障診斷。利用神經(jīng)網(wǎng)絡(luò)進(jìn)行滾動軸承故障診斷,可以降低對操作人員的專業(yè)知識要求,將故障診斷從傳統(tǒng)方法轉(zhuǎn)向人工智能方向。同時,診斷系統(tǒng)中智能技術(shù)的應(yīng)用能大大降低維修人員工作壓力。
[Abstract]:The fault diagnosis technology of rolling bearing of freight train is an engineering science which is closely combined with practice. The basic reason of its development is the need of production. Therefore, the research of simple diagnosis method has broad practical value. This paper starts with the theoretical analysis of the noise generation and propagation of rolling bearing of freight train, synthetically considering the influence of sound attenuation and background noise of rolling bearing on freight train, aiming at the non-stationary sound signal of rolling bearing of freight train. In order to improve the effectiveness and reliability of the diagnosis and identification of acoustic signals, modern signal analysis technology is used to process and identify the acoustic signals. This paper mainly studies the following aspects: aiming at the non-stationarity of the sound signal of the rolling bearing of freight train, this paper uses the multi-resolution property of the wavelet transform to analyze the acoustic signal. It is found that the shock component is amplified in the detail signal of wavelet decomposition, and the reason of the fault is found by comparing the frequency with the fault frequency formed under various kinds of faults, so that the waveform of the signal can be effectively recognized. In this paper, a hierarchical threshold denoising method based on nonlinear wavelet transform is proposed. The simulation results show that the proposed method can significantly improve the filtering accuracy and preserve the main details of the signal at the same time of removing noise effectively. Then the self-power spectrum density analysis of the signal after wavelet transform is carried out. The simulation results show that the self-power spectrum density analysis based on wavelet transform can effectively extract the characteristic frequency of the fault acoustic signal of rolling bearing of freight train. It is suitable for the analysis and research of nonstationary signals such as acoustic signals. In the aspect of feature extraction, a new method of feature extraction based on local energy of frequency band is proposed in this paper, which can subdivide every frequency band according to the need. It is proved by practice that these characteristic factors can well represent the working condition of rolling bearings. The application of neural network in intelligent diagnosis of rolling bearing of freight train is studied. There are a lot of fault feature extraction methods based on sound signal analysis, but each method only reflects the fault characteristics in one aspect, and the diagnosis effect is not very good. In this paper, the fault features extracted by different methods are compared as the input of neural network, and the method of combining wavelet analysis and neural network is used to diagnose the fault of rolling bearing of freight train. The fault diagnosis of rolling bearing based on neural network can reduce the requirement of professional knowledge for the operator and turn the fault diagnosis from traditional method to artificial intelligence. At the same time, the application of intelligent technology in diagnosis system can greatly reduce the working pressure of maintainers.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TN912.3;TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張武軍,徐金梧,楊德斌,周艷玲,王海峰;聲信號分析方法在軸承故障診斷中的應(yīng)用[J];北京科技大學(xué)學(xué)報;2002年01期

2 唐德堯,王定曉,楊政明,宋辛輝,王巍松;共振解調(diào)技術(shù)與機(jī)車車輛傳動裝置故障診斷[J];電力機(jī)車技術(shù);2002年05期

3 王鐵,張國忠,侯榮濤;基于倒譜和小波變換的驅(qū)動橋故障特征提取[J];計算機(jī)測量與控制;2003年08期

4 劉慶升,徐霄鵬,黃文浩;一種語音端點檢測方法的探究[J];計算機(jī)工程;2003年03期

5 莫琦,陳立定,馮太合;小波包在旋轉(zhuǎn)機(jī)械故障振動信號處理中的應(yīng)用[J];計算技術(shù)與自動化;2005年02期

6 許隴云,卞青青,朱善安,鄭劍鋒,張慧才,金鳳釗;滾動軸承異音的數(shù)學(xué)模型和評估參數(shù)[J];機(jī)械工程學(xué)報;2002年01期

7 石華峰,尹國華,賀旭軍,葉秋文;機(jī)車軸承故障診斷[J];機(jī)車電傳動;2004年02期

8 桂普江,林建中;滾動軸承故障診斷的神經(jīng)網(wǎng)絡(luò)方法[J];機(jī)械;2004年10期

9 陳四根;基于熵函數(shù)的語音端點檢測方法[J];聲學(xué)與電子工程;2001年01期

10 張生玉,馮庚斌,段全勝,丁福焰;JL-601機(jī)車走行部檢測系統(tǒng)[J];鐵道機(jī)車車輛;2000年05期

相關(guān)碩士學(xué)位論文 前1條

1 崔寶珍;基于小波分析的滾動軸承故障診斷方法研究與應(yīng)用[D];中北大學(xué);2005年

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本文編號：2202665