發(fā)布時間：2019-03-14 08:37

【摘要】：旋轉(zhuǎn)機(jī)械是生產(chǎn)領(lǐng)域中十分重要的機(jī)械設(shè)備,由于旋轉(zhuǎn)機(jī)械激勵源多、性質(zhì)復(fù)雜,其振動信號往往是非平穩(wěn)的多分量信號,其不同的非平穩(wěn)特性往往對應(yīng)不同的機(jī)械故障。為了更好的進(jìn)行旋轉(zhuǎn)機(jī)械的故障診斷,有必要對信號的時頻分析方法進(jìn)行研究與應(yīng)用。本文根據(jù)以上的需求開展研究工作,基于維格納-威爾分布、小波尺度譜、Hilbert時頻譜等時頻分析方法,結(jié)合盲分離、同步平均技術(shù)和多尺度熵方法,對旋轉(zhuǎn)機(jī)械的故障診斷方法進(jìn)行了較為深入的研究和應(yīng)用。 為了實(shí)現(xiàn)對旋轉(zhuǎn)機(jī)械振動信號中獨(dú)立源信號的提取,研究了一種基于時頻分析的盲分離方法,從而更加準(zhǔn)確的進(jìn)行設(shè)備故障診斷。首先在理論上推導(dǎo)了該方法的實(shí)現(xiàn)過程；采用仿真信號分析了基于不同時頻分布的盲分離的效果,并與獨(dú)立分量分析的結(jié)果進(jìn)行了對比；最后在轉(zhuǎn)子實(shí)驗(yàn)臺上模擬不平衡、不對中和基座松動三種故障,采用該方法較好的實(shí)現(xiàn)了三種故障的識別與診斷。 針對旋轉(zhuǎn)機(jī)械振動信號中往往夾雜著噪聲干擾和具有循環(huán)平穩(wěn)性的特點(diǎn),結(jié)合時域同步平均可降低噪聲干擾的優(yōu)點(diǎn)和小波變換可對信號進(jìn)行多尺度分析的優(yōu)勢,提出一種基于小波重排尺度譜的同步平均的信號分析方法。首先對信號進(jìn)行連續(xù)小波變換并進(jìn)行重排處理,然后對各個尺度上的信號進(jìn)行時域同步平均,獲得平均后的小波重排尺度譜。通過仿真分析和滾動軸承故障模擬實(shí)驗(yàn)檢驗(yàn)該方法的有效性。 旋轉(zhuǎn)機(jī)械的Hilbert時頻譜含有大量的機(jī)械設(shè)備工作狀態(tài)的特征信息,然而其特征往往難于辨識,而多尺度熵可以有效的描述序列的復(fù)雜度,提出了一種基于Hilbert時頻譜特征提取的設(shè)備狀態(tài)識別方法。首先對信號進(jìn)行希爾伯特-黃變換獲得Hilbert時頻譜,然后對時頻譜進(jìn)行區(qū)域劃分和降至一維并求其多尺度熵,通過對比設(shè)備不同運(yùn)行狀態(tài)下的Hilbert時頻譜的多尺度熵曲線,選擇有效分離不同設(shè)備狀態(tài)的尺度處的樣本熵和時頻譜的能量作為其特征向量用于設(shè)備狀態(tài)識別。采用本方法對不同軸承故障狀態(tài)的信號進(jìn)行了特征提取,實(shí)現(xiàn)了軸承狀態(tài)的有效識別。 基于虛擬儀器開發(fā)了旋轉(zhuǎn)機(jī)械振動測試與分析系統(tǒng)。該系統(tǒng)可以實(shí)現(xiàn)8通道的振動信號采集,并通過無線數(shù)據(jù)傳輸模式將測試數(shù)據(jù)傳輸給上位機(jī),進(jìn)行顯示、分析和存儲。具有常見的時域、頻域等分析功能和Hilbert時頻譜等時頻分析方法。通過實(shí)際應(yīng)用驗(yàn)證了系統(tǒng)的實(shí)用性和有效性。
[Abstract]:Rotating machinery is a very important mechanical equipment in the field of production. Because of its many excitation sources and complex properties, its vibration signal is usually a non-stationary multi-component signal, and its different non-stationary characteristics often correspond to different mechanical faults. In order to make better fault diagnosis of rotating machinery, it is necessary to study and apply the time-frequency analysis method of signal. Based on Wigner-Weill distribution, wavelet scale spectrum and Hilbert time-frequency analysis method, this paper combines blind separation, synchronous averaging technique and multi-scale entropy method. The fault diagnosis method of rotating machinery is studied and applied deeply. In order to extract the independent source signal from the vibration signal of rotating machinery, a blind separation method based on time-frequency analysis is studied, so that the equipment fault diagnosis can be carried out more accurately. Firstly, the realization process of this method is deduced theoretically, and the effect of blind separation based on different time-frequency distributions is analyzed by simulation signals, and the results are compared with the results of independent component analysis (ICA). At last, the unbalance is simulated on the rotor experimental platform, and the three faults of loose base are not neutralized. The method is used to realize the identification and diagnosis of the three kinds of faults. In view of the characteristics of noise interference and cyclic stationarity in vibration signals of rotating machinery, combined with the advantages of synchronous average in time domain and multi-scale analysis of signals by wavelet transform, the noise interference can be reduced in time domain, and the advantages of wavelet transform can be used in multi-scale analysis of signals. A synchronous average signal analysis method based on wavelet rearrangement scale spectrum is proposed. Firstly, continuous wavelet transform and rearrangement of the signal are carried out, and then the signal of each scale is averaged synchronously in time domain, and the average wavelet rearrangement scale spectrum is obtained. The effectiveness of the method is verified by simulation analysis and rolling bearing fault simulation experiment. The Hilbert spectrum of rotating machinery contains a large number of characteristic information of the working state of mechanical equipment, however, its characteristics are often difficult to identify, and multi-scale entropy can effectively describe the complexity of the sequence. In this paper, a device state recognition method based on Hilbert time spectrum feature extraction is proposed. Firstly, the Hilbert time spectrum is obtained by Hilbert-Huang transform, then the time spectrum is partitioned and reduced to one dimension and its multi-scale entropy is calculated. By comparing the multi-scale entropy curves of the Hilbert time spectrum under different operating states of the equipment, The entropy of samples and the energy of time spectrum at the scale of different equipment states are selected as their Eigenvectors for equipment state identification. Using this method, the signals of different bearing fault states are extracted, and the effective identification of bearing states is realized. The vibration test and analysis system of rotating machinery is developed based on virtual instrument. The system can realize 8-channel vibration signal acquisition and transmit the test data to the upper computer through wireless data transmission mode for display, analysis and storage. It has common time-frequency analysis functions such as time-domain and frequency-domain, and time-frequency analysis methods such as Hilbert time-frequency spectrum. The practicability and effectiveness of the system are verified by practical application.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李宏坤;張志新;馬孝江;王珍;;基于Hilbert譜熵的柴油機(jī)故障診斷方法研究[J];大連理工大學(xué)學(xué)報;2008年02期

2 蘇中元;賈民平;;基于希爾伯特-黃變換周期平穩(wěn)類微弱故障信號檢測[J];東南大學(xué)學(xué)報(自然科學(xué)版);2006年03期

3 劉峻華,黃樹紅,陸繼東;汽輪機(jī)故障診斷技術(shù)的發(fā)展與展望[J];動力工程;2001年02期

4 李霄,裴樹毅,屈梁生;全息譜技術(shù)用于化工動設(shè)備故障診斷[J];化工進(jìn)展;1993年04期

5 王平,廖明夫;滾動軸承故障診斷的自適應(yīng)共振解調(diào)技術(shù)[J];航空動力學(xué)報;2005年04期

6 成瓊,于德介;基于復(fù)小波變換相位功率譜的齒輪故障診斷[J];湖南大學(xué)學(xué)報(自然科學(xué)版);2001年S1期

7 楊叔子,師漢民,熊有倫,王治藩;機(jī)械設(shè)備診斷學(xué)的探討[J];華中工學(xué)院學(xué)報;1987年02期

8 張梅軍,何世平,葛強(qiáng)盛,劉念,熊明忠;偽魏格納分布和連續(xù)小波變換在變速箱故障診斷中的應(yīng)用[J];解放軍理工大學(xué)學(xué)報(自然科學(xué)版);2001年01期

9 楊宇,于德介,程軍圣;基于Hilbert-Huang變換的特征能量法及其在滾動軸承故障診斷中的應(yīng)用[J];計算機(jī)工程與應(yīng)用;2004年10期

10 何曉霞,沈玉娣,張西寧;連續(xù)小波變換在滾動軸承故障診斷中的應(yīng)用[J];機(jī)械科學(xué)與技術(shù);2001年04期

相關(guān)博士學(xué)位論文 前2條

1 程軍圣;基于Hilbert-Huang變換的旋轉(zhuǎn)機(jī)械故障診斷方法研究[D];湖南大學(xué);2005年

2 周福昌;基于循環(huán)平穩(wěn)信號處理的滾動軸承故障診斷方法研究[D];上海交通大學(xué);2006年

相關(guān)碩士學(xué)位論文 前2條

1 劉冬;基于振動信號處理的旋轉(zhuǎn)機(jī)械故障診斷[D];上海交通大學(xué);2010年

2 盧一相;時頻分析在軸承故障診斷中的應(yīng)用研究[D];安徽大學(xué);2007年

，

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