【摘要】：現(xiàn)代機(jī)械設(shè)備的自動(dòng)化程度和智能化水平越來(lái)越先進(jìn)，它的發(fā)展對(duì)工業(yè)、經(jīng)濟(jì)有著深刻影響。隨著基于狀態(tài)維修（CBM）和故障預(yù)測(cè)與健康管理（PHM）等維修理論和技術(shù)的發(fā)展，近幾年，對(duì)狀態(tài)實(shí)時(shí)監(jiān)測(cè)技術(shù)、狀態(tài)信息采集和處理技術(shù)、故障診斷和預(yù)測(cè)技術(shù)的研究成為熱點(diǎn)。在機(jī)械設(shè)備狀態(tài)監(jiān)測(cè)和故障診斷領(lǐng)域內(nèi)，機(jī)械設(shè)備從正常運(yùn)行狀態(tài)到故障狀態(tài)通常要經(jīng)過(guò)一系列不同的退化狀態(tài)，如何正確識(shí)別設(shè)備當(dāng)前所處的狀態(tài)，進(jìn)一步預(yù)測(cè)設(shè)備的發(fā)展態(tài)勢(shì)，為維護(hù)決策提供依據(jù)是一個(gè)迫切需要解決的問(wèn)題�；谏鲜鰡�(wèn)題，本文做了以下研究： （1）非平穩(wěn)信號(hào)預(yù)處理 在對(duì)機(jī)械設(shè)備進(jìn)行狀態(tài)監(jiān)測(cè)情況下，由于振動(dòng)信號(hào)較其它信號(hào)容易采集，并且對(duì)故障較為敏感，能提供設(shè)備運(yùn)行狀況的豐富信息，所以把振動(dòng)信號(hào)作為設(shè)備退化狀態(tài)識(shí)別的特征信號(hào)。振動(dòng)信號(hào)是一種典型的非平穩(wěn)信號(hào)，由于外界干擾，對(duì)其進(jìn)行預(yù)處理是后期研究的關(guān)鍵。本文首先介紹了小波包能量閾值去噪法，并對(duì)小波包能量閾值去噪方法的小波基的選取進(jìn)行分析和仿真，通過(guò)實(shí)驗(yàn)給出小波包能量閾值去噪和譜相減去噪的適用環(huán)境。小波包能量閾值去噪適合用于輸入信噪比低的信號(hào)，譜相減去噪適合用于信噪比高的信號(hào)，兩種方法可以結(jié)合使用對(duì)振動(dòng)信號(hào)去噪處理。 （2）EMD能量熵特征提取 傳統(tǒng)的傅里葉變換無(wú)法同時(shí)兼顧信號(hào)在時(shí)頻兩域的全貌和局部化特征，小波分析不具有自適應(yīng)性，針對(duì)兩者的不足，論文提出了基于經(jīng)驗(yàn)?zāi)B(tài)分解（EMD）的特征提取方法；信息熵是對(duì)設(shè)備狀態(tài)不確定程度和復(fù)雜程度的描述，，當(dāng)信源含有的信息波動(dòng)不穩(wěn)定、成分比較復(fù)雜時(shí)，信息熵就越大。論文利用EMD方法把經(jīng)過(guò)去噪后的信號(hào)分解成一組固有模態(tài)函數(shù)（IMF）分量，并提取和計(jì)算各IMF能量及能量熵，作為描述退化狀態(tài)的特征參數(shù)。 （3）基于HMM退化狀態(tài)識(shí)別和故障預(yù)測(cè) 針對(duì)隱馬爾科夫模型（HMM）算法中參數(shù)設(shè)置問(wèn)題、訓(xùn)練算法容易陷入局部最優(yōu)的問(wèn)題，深入研究了HMM的改進(jìn)算法；針對(duì)單一方法進(jìn)行故障預(yù)測(cè)存在的缺陷，利用HMM和指數(shù)平滑預(yù)測(cè)相結(jié)合的方法進(jìn)行研究，可以綜合兩者的優(yōu)點(diǎn)；最后以液壓元件為研究對(duì)象，對(duì)上述方法進(jìn)行驗(yàn)證，并和BPNN、SVM的識(shí)別效果進(jìn)行比較，算例結(jié)果表明，該方法具有魯棒性好、分辨靈敏度高和故障預(yù)測(cè)總體準(zhǔn)確率較高的優(yōu)點(diǎn)。
[Abstract]:The level of automation and intelligence of modern mechanical equipment is more and more advanced, and its development has a profound impact on industry and economy. With the development of maintenance theory and technology such as condition based maintenance (CBM) and fault prediction and health management (PHM), the research on state real-time monitoring technology, state information collection and processing technology, fault diagnosis and prediction technology has become a hot topic in recent years. In the field of mechanical equipment condition monitoring and fault diagnosis, mechanical equipment usually goes through a series of different degenerative states from normal operation state to fault state, how to correctly identify the current state of the equipment, It is an urgent problem to predict the development situation of equipment and provide basis for maintenance decision. Based on the above problems, this paper has done the following research: (1) in the condition of monitoring the condition of mechanical equipment, the vibration signal is easier to collect than other signals, and it is more sensitive to fault. The vibration signal is regarded as the characteristic signal of equipment degenerative state recognition because it can provide abundant information about the equipment running condition. Vibration signal is a kind of typical nonstationary signal. In this paper, the wavelet packet energy threshold denoising method is first introduced, and the selection of wavelet basis for wavelet packet energy threshold denoising method is analyzed and simulated. The suitable environment for wavelet packet energy threshold denoising and spectral phase subtraction is given through experiments. Wavelet packet energy threshold denoising is suitable for input signal with low signal-to-noise ratio (SNR), and spectral phase subtraction is suitable for high SNR signal. The two methods can be combined with vibration signal denoising. (2) EMD energy entropy feature extraction can not simultaneously take into account the full feature and localization feature of the signal in time-frequency domain. Wavelet analysis is not self-adaptive. In view of the shortcomings of the two methods, a feature extraction method based on empirical mode decomposition (EMD) is proposed, and the information entropy is used to describe the degree of uncertainty and complexity of equipment state. When the information contained in the source fluctuates unsteadily and the components are complex, the information entropy increases. In this paper, the de-noised signal is decomposed into a set of inherent mode function (IMF) components by EMD method, and each IMF energy and energy entropy are extracted and calculated. As a characteristic parameter to describe degenerate state. (3) based on HMM degenerate state identification and fault prediction, the training algorithm is prone to fall into the local optimal problem for parameter setting in Hidden Markov Model (HMM) algorithm. This paper deeply studies the improved algorithm of HMM, aiming at the defects of single method in fault prediction, using the combination of HMM and exponential smoothing prediction, it can synthesize the advantages of both. Finally, the hydraulic components are taken as the research object. The method is verified and compared with that of BPNNN SVM. The results show that the proposed method has the advantages of good robustness, high resolution sensitivity and high overall accuracy of fault prediction.
【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN911.7

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