基于HMM的退化狀態(tài)識別和故障預測研究
發(fā)布時間：2018-08-02 11:13
【摘要】：現(xiàn)代機械設備的自動化程度和智能化水平越來越先進，它的發(fā)展對工業(yè)、經濟有著深刻影響。隨著基于狀態(tài)維修（CBM）和故障預測與健康管理（PHM）等維修理論和技術的發(fā)展，近幾年，對狀態(tài)實時監(jiān)測技術、狀態(tài)信息采集和處理技術、故障診斷和預測技術的研究成為熱點。在機械設備狀態(tài)監(jiān)測和故障診斷領域內，機械設備從正常運行狀態(tài)到故障狀態(tài)通常要經過一系列不同的退化狀態(tài)，如何正確識別設備當前所處的狀態(tài)，進一步預測設備的發(fā)展態(tài)勢，為維護決策提供依據(jù)是一個迫切需要解決的問題�；谏鲜鰡栴}，本文做了以下研究： （1）非平穩(wěn)信號預處理 在對機械設備進行狀態(tài)監(jiān)測情況下，由于振動信號較其它信號容易采集，并且對故障較為敏感，能提供設備運行狀況的豐富信息，所以把振動信號作為設備退化狀態(tài)識別的特征信號。振動信號是一種典型的非平穩(wěn)信號，由于外界干擾，對其進行預處理是后期研究的關鍵。本文首先介紹了小波包能量閾值去噪法，并對小波包能量閾值去噪方法的小波基的選取進行分析和仿真，通過實驗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，譜相減去噪適合用于信噪比高的信號，兩種方法可以結合使用對振動信號去噪處理。 （2）EMD能量熵特征提取 傳統(tǒng)的傅里葉變換無法同時兼顧信號在時頻兩域的全貌和局部化特征，小波分析不具有自適應性，針對兩者的不足，論文提出了基于經驗模態(tài)分解（EMD）的特征提取方法；信息熵是對設備狀態(tài)不確定程度和復雜程度的描述，，當信源含有的信息波動不穩(wěn)定、成分比較復雜時，信息熵就越大。論文利用EMD方法把經過去噪后的信號分解成一組固有模態(tài)函數(shù)（IMF）分量，并提取和計算各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)識別和故障預測 針對隱馬爾科夫模型（HMM）算法中參數(shù)設置問題、訓練算法容易陷入局部最優(yōu)的問題，深入研究了HMM的改進算法；針對單一方法進行故障預測存在的缺陷，利用HMM和指數(shù)平滑預測相結合的方法進行研究，可以綜合兩者的優(yōu)點；最后以液壓元件為研究對象，對上述方法進行驗證，并和BPNN、SVM的識別效果進行比較，算例結果表明，該方法具有魯棒性好、分辨靈敏度高和故障預測總體準確率較高的優(yōu)點。
[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.
【學位授予單位】：太原科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.7
本文編號：2159246
[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.
【學位授予單位】：太原科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.7
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