基于支持向量機(jī)的旋轉(zhuǎn)機(jī)械故障診斷與預(yù)測方法研究
發(fā)布時(shí)間：2018-09-12 14:09
【摘要】：隨著現(xiàn)代科學(xué)技術(shù)的迅猛發(fā)展,旋轉(zhuǎn)機(jī)械不斷朝著大型化、復(fù)雜化、高速化、連續(xù)化和自動(dòng)化的方向發(fā)展。這些發(fā)展在帶來較高生產(chǎn)效率的同時(shí),對設(shè)備的安全運(yùn)轉(zhuǎn)也提出了更高的要求,一旦發(fā)生故障,將造成巨大的經(jīng)濟(jì)損失,甚至?xí)䦟?dǎo)致災(zāi)難性的人員傷亡事故和嚴(yán)重的社會(huì)影響。對設(shè)備的故障進(jìn)行診斷,以及根據(jù)歷史數(shù)據(jù)對設(shè)備運(yùn)行狀態(tài)進(jìn)行預(yù)測,是保證設(shè)備安全可靠運(yùn)行的重要措施,只有如此才能及時(shí)有效的處理存在的問題,將故障消除在萌芽狀態(tài)。本文研究基于支持向量機(jī)理論對設(shè)備運(yùn)行狀態(tài)進(jìn)行故障診斷并根據(jù)歷史運(yùn)行數(shù)據(jù)對設(shè)備未來狀態(tài)進(jìn)行預(yù)測的方法,建立了設(shè)備運(yùn)行狀態(tài)預(yù)報(bào)和故障診斷模型,并對一些關(guān)鍵問題的解決進(jìn)行了深入研究。本文主要?jiǎng)?chuàng)新工作如下： 1、提出了基于遺傳算法的支持向量機(jī)樣本平衡方法 對于故障診斷來說,故障樣本一般少于正常樣本,所以普遍存在不平衡樣本問題。支持向量機(jī)在遇到樣本不平衡問題時(shí),往往造成“少樣本類”的誤診。針對該問題,本文提出了基于遺傳算法的樣本平衡方法,利用遺傳算法中的交叉、變異方式生成子代樣本,對“少樣本類”進(jìn)行繁殖擴(kuò)充,得到更多的該類樣本進(jìn)而達(dá)到兩類樣本的平衡。為使擴(kuò)充的樣本更具有針對性,更有利于形成正確的“分類超平面”,給出了父代樣本的選擇方法以及子代樣本的評價(jià)方法。 2、分析了EMD方法產(chǎn)生虛假分量的原因,并提出了一種識(shí)別虛假分量的方法 作為特征提取的方法,EMD能夠較好的處理非平穩(wěn)、非線性問題,但利用EMD方法對信號進(jìn)行處理時(shí)常常會(huì)引入虛假分量,影響分析的準(zhǔn)確性,是嚴(yán)重制約EMD方法發(fā)展的瓶頸問題。為了消除虛假分量的影響,更好地發(fā)揮EMD方法在特征提取中的作用,本文提出基于K-L散度的虛假分量識(shí)別方法,該方法利用K-L散度來評價(jià)EMD分解得到的各個(gè)分量與原信號的關(guān)系程度,分量與原信號之間的K-L散度越小,關(guān)系程度越大,分量的真實(shí)性就越高,反之虛假性就越高,虛假分量通過設(shè)定閾值進(jìn)行判別。同時(shí),文中研究并給出了閡值設(shè)定方法。 3、提出了基于EMD特征提取的支持向量機(jī)算法 在根據(jù)歷史數(shù)據(jù)對運(yùn)行狀態(tài)進(jìn)行預(yù)測時(shí),特征參數(shù)與預(yù)測點(diǎn)的關(guān)聯(lián)程度在很大程度上決定了預(yù)測值的準(zhǔn)確性。目前,特征參數(shù)的選取方法主要有兩種：一種是基于實(shí)測數(shù)據(jù)的特征,即采集與預(yù)測值相關(guān)聯(lián)的影響因素作為特征,如對風(fēng)電功率預(yù)測時(shí)選取風(fēng)速、氣壓等因素作為特征,但對于振動(dòng)等一些預(yù)測量來說,其影響因素往往十分復(fù)雜,不易明確,利用這種方式也就無法建立高精度預(yù)測模型；另一種是通過對時(shí)間序列的計(jì)算得到其特征參數(shù),這類方法中最具代表性且最常用的方法是基于相空間重構(gòu)的方法,方法利用混沌理論計(jì)算嵌入維數(shù)和時(shí)間延遲重構(gòu)相空間,得到時(shí)間序列的特征,然而嵌入維數(shù)以及時(shí)間延遲的確定只是從時(shí)間序列的動(dòng)力學(xué)特性角度來考慮的,用其構(gòu)造的特征對于預(yù)測模型來說并不一定合適,所以往往會(huì)由于二者選取的不恰當(dāng)而無法得到合適的特征,進(jìn)而造成預(yù)測的精度大大降低。故本文針對預(yù)測模型特征選取的問題,提出了基于EMD特征提取的支持向量機(jī)算法(EMD-SVM),利用EMD分解后各時(shí)間點(diǎn)的分量值作為特征,并與該時(shí)間點(diǎn)對應(yīng)的時(shí)間序列值(目標(biāo)值)共同構(gòu)成樣本,建立預(yù)測模型,并通過實(shí)驗(yàn)證明其較高的準(zhǔn)確性和穩(wěn)定性。 4、針對支持向量機(jī)存在大規(guī)模樣本問題提出了基于信息熵的樣本長度選擇方法 大規(guī)模訓(xùn)練樣本問題一直是困擾SVM計(jì)算速度提升的瓶頸,過多的訓(xùn)練樣本會(huì)大大的增加計(jì)算成本,而且不一定會(huì)帶來更準(zhǔn)確的預(yù)測結(jié)果,甚至?xí)䦟?dǎo)致更嚴(yán)重的偏差。所以,訓(xùn)練樣本的長度必須控制在合適的范圍內(nèi)。針對該問題,本文提出了基于信息熵的樣本長度選擇方法。該方法的基本思想是選取與預(yù)測點(diǎn)最相關(guān)的歷史數(shù)據(jù)作為訓(xùn)練樣本,保證數(shù)據(jù)信息的完備性和不冗余�？拷A(yù)測點(diǎn)的歷史數(shù)據(jù)通常與預(yù)測點(diǎn)之間關(guān)系越強(qiáng),這些數(shù)據(jù)作為訓(xùn)練樣本對于預(yù)測點(diǎn)來說意義較大；隨著距離的增加(時(shí)間不斷向前推移)關(guān)聯(lián)性越弱,對于預(yù)測點(diǎn)意義較小,將這些數(shù)據(jù)加入到訓(xùn)練樣本中會(huì)表現(xiàn)出數(shù)據(jù)波動(dòng)性增大,平穩(wěn)性降低,需要對這些點(diǎn)進(jìn)行刪減。本文方法基于這一思想,在歷史數(shù)據(jù)中從前向后依次選取不同的位置作為起始點(diǎn)截取時(shí)間序列,計(jì)算不同起始點(diǎn)截得時(shí)間序列的信息熵值。將對應(yīng)信息熵最小的起始點(diǎn)作為新時(shí)間坐標(biāo)軸的“0點(diǎn)坐標(biāo)”,坐標(biāo)軸負(fù)半軸的數(shù)據(jù)時(shí)間久遠(yuǎn)且與當(dāng)前狀態(tài)相關(guān)程度低,需剔除,正半軸數(shù)據(jù)與當(dāng)前相關(guān)程度高,故用來作為訓(xùn)練樣本,如此可以保證信息的完備性,同時(shí)避免了建立模型時(shí)的尋優(yōu)過程對相關(guān)程度較低的訓(xùn)練樣本的“照料”。文中通過理論分析和實(shí)驗(yàn)的方式從計(jì)算時(shí)間和預(yù)測精度的角度考察了方法的有效性。
[Abstract]:With the rapid development of modern science and technology, rotating machinery is developing toward the direction of large-scale, complex, high-speed, continuous and automation. These developments bring about higher production efficiency, at the same time, higher requirements for the safe operation of equipment, once a fault occurs, it will cause enormous economic losses, or even lead to it. Disastrous casualties and serious social impacts. Diagnosis of equipment failures and prediction of equipment operation status based on historical data are important measures to ensure the safe and reliable operation of equipment. Only in this way can problems be dealt with in a timely and effective manner, and the failure can be eliminated in the embryonic state. Support Vector Machine (SVM) theory is used to diagnose the running state of the equipment and predict the future state of the equipment according to the historical running data.
1, a sample balance method based on genetic algorithm for support vector machines is proposed.
For fault diagnosis, the number of fault samples is usually less than that of normal samples, so unbalanced samples are common. Support vector machines often cause misdiagnosis of "few sample classes" when they encounter unbalanced samples. In order to make the expanded sample more pertinent and more conducive to forming a correct "classification hyperplane", the selection method of parent sample and the evaluation method of offspring sample are given.
2, the causes of false components produced by the EMD method are analyzed, and a method to identify false components is proposed.
As a method of feature extraction, EMD can deal with non-stationary and non-linear problems well, but when EMD is used to process signals, false components are often introduced. The accuracy of impact analysis is a bottleneck problem which seriously restricts the development of EMD. In order to eliminate the influence of false components, EMD method can be better used in feature extraction. In this paper, a method of false component identification based on K-L divergence is proposed. This method uses K-L divergence to evaluate the relationship between each component obtained by EMD decomposition and the original signal. The smaller the K-L divergence between the component and the original signal, the greater the relationship, the higher the authenticity of the component, otherwise the higher the falseness, and the false component by setting a threshold. At the same time, the method of setting the threshold value is studied and given.
3, a support vector machine algorithm based on EMD feature extraction is proposed.
At present, there are two main methods to select feature parameters: one is based on the characteristics of measured data, that is, the factors associated with the predicted values are collected as features, such as wind power. Wind speed, air pressure and other factors are selected as the characteristics of power forecasting, but for some forecasting variables such as vibration, the influencing factors are often very complex and difficult to define, so it is impossible to establish a high-precision forecasting model by using this method; the other is to get the characteristic parameters by calculating the time series, which is the most representative method. And the most commonly used method is based on the phase space reconstruction method. The method uses chaos theory to calculate the embedding dimension and time delay to reconstruct the phase space and get the characteristics of time series. Because of the improper selection of the two features, the prediction accuracy will be greatly reduced. Therefore, this paper proposes a support vector machine algorithm based on EMD feature extraction (EMD-SVM), which uses the component values of each time point after EMD decomposition as the prediction model feature selection problem. It is characterized by the time series value (target value) corresponding to the time point to form a sample, and the prediction model is established, and its high accuracy and stability are proved by experiments.
4. Aiming at the problem of large-scale sample in support vector machine, a sample length selection method based on information entropy is proposed.
The problem of large-scale training samples has always been a bottleneck to speed up the calculation of SVM. Too many training samples will greatly increase the calculation cost, and will not necessarily lead to more accurate prediction results, or even lead to more serious deviations. Therefore, the length of training samples must be controlled within a suitable range. The basic idea of this method is to select the most relevant historical data as training samples to ensure the completeness and non-redundancy of data information. With the increase of distance (time goes forward) the correlation is weaker and the significance for predicting points is smaller. Adding these data into training samples will increase the data volatility and reduce the stationarity, which needs to be deleted. Based on this idea, this method selects the historical data from front to back in turn. The information entropy value of the time series is calculated by using the same position as the starting point and the minimum starting point of the corresponding information entropy is regarded as the "0-point coordinate" of the new time coordinate axis. It is used as training sample to ensure the completeness of the information and avoid the "care" of the training sample with low correlation in the process of establishing the model.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TH165.3
[Abstract]:With the rapid development of modern science and technology, rotating machinery is developing toward the direction of large-scale, complex, high-speed, continuous and automation. These developments bring about higher production efficiency, at the same time, higher requirements for the safe operation of equipment, once a fault occurs, it will cause enormous economic losses, or even lead to it. Disastrous casualties and serious social impacts. Diagnosis of equipment failures and prediction of equipment operation status based on historical data are important measures to ensure the safe and reliable operation of equipment. Only in this way can problems be dealt with in a timely and effective manner, and the failure can be eliminated in the embryonic state. Support Vector Machine (SVM) theory is used to diagnose the running state of the equipment and predict the future state of the equipment according to the historical running data.
1, a sample balance method based on genetic algorithm for support vector machines is proposed.
For fault diagnosis, the number of fault samples is usually less than that of normal samples, so unbalanced samples are common. Support vector machines often cause misdiagnosis of "few sample classes" when they encounter unbalanced samples. In order to make the expanded sample more pertinent and more conducive to forming a correct "classification hyperplane", the selection method of parent sample and the evaluation method of offspring sample are given.
2, the causes of false components produced by the EMD method are analyzed, and a method to identify false components is proposed.
As a method of feature extraction, EMD can deal with non-stationary and non-linear problems well, but when EMD is used to process signals, false components are often introduced. The accuracy of impact analysis is a bottleneck problem which seriously restricts the development of EMD. In order to eliminate the influence of false components, EMD method can be better used in feature extraction. In this paper, a method of false component identification based on K-L divergence is proposed. This method uses K-L divergence to evaluate the relationship between each component obtained by EMD decomposition and the original signal. The smaller the K-L divergence between the component and the original signal, the greater the relationship, the higher the authenticity of the component, otherwise the higher the falseness, and the false component by setting a threshold. At the same time, the method of setting the threshold value is studied and given.
3, a support vector machine algorithm based on EMD feature extraction is proposed.
At present, there are two main methods to select feature parameters: one is based on the characteristics of measured data, that is, the factors associated with the predicted values are collected as features, such as wind power. Wind speed, air pressure and other factors are selected as the characteristics of power forecasting, but for some forecasting variables such as vibration, the influencing factors are often very complex and difficult to define, so it is impossible to establish a high-precision forecasting model by using this method; the other is to get the characteristic parameters by calculating the time series, which is the most representative method. And the most commonly used method is based on the phase space reconstruction method. The method uses chaos theory to calculate the embedding dimension and time delay to reconstruct the phase space and get the characteristics of time series. Because of the improper selection of the two features, the prediction accuracy will be greatly reduced. Therefore, this paper proposes a support vector machine algorithm based on EMD feature extraction (EMD-SVM), which uses the component values of each time point after EMD decomposition as the prediction model feature selection problem. It is characterized by the time series value (target value) corresponding to the time point to form a sample, and the prediction model is established, and its high accuracy and stability are proved by experiments.
4. Aiming at the problem of large-scale sample in support vector machine, a sample length selection method based on information entropy is proposed.
The problem of large-scale training samples has always been a bottleneck to speed up the calculation of SVM. Too many training samples will greatly increase the calculation cost, and will not necessarily lead to more accurate prediction results, or even lead to more serious deviations. Therefore, the length of training samples must be controlled within a suitable range. The basic idea of this method is to select the most relevant historical data as training samples to ensure the completeness and non-redundancy of data information. With the increase of distance (time goes forward) the correlation is weaker and the significance for predicting points is smaller. Adding these data into training samples will increase the data volatility and reduce the stationarity, which needs to be deleted. Based on this idea, this method selects the historical data from front to back in turn. The information entropy value of the time series is calculated by using the same position as the starting point and the minimum starting point of the corresponding information entropy is regarded as the "0-point coordinate" of the new time coordinate axis. It is used as training sample to ensure the completeness of the information and avoid the "care" of the training sample with low correlation in the process of establishing the model.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TH165.3
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 王娟娟;任秋實(shí);;基于SMO的不同懲罰系數(shù)的SVM算法[J];信息技術(shù);2006年10期
2 李雪耀;鄒曉杰;張汝波;錢真;;譜熵和主成分分析用于EMD分解研究[J];哈爾濱工程大學(xué)學(xué)報(bào);2009年07期
3 程軍圣;于德介;楊宇;;基于EMD和SVM的滾動(dòng)軸承故障診斷方法[J];航空動(dòng)力學(xué)報(bào);2006年03期
4 翟永杰;劉林;王朋;;基于ADE-SVM和模糊理論的電力系統(tǒng)中期負(fù)荷預(yù)測[J];電力系統(tǒng)保護(hù)與控制;2012年08期
5 劉向東 ,駱斌 ,陳兆乾;支持向量機(jī)最優(yōu)模型選擇的研究[J];計(jì)算機(jī)研究與發(fā)展;2005年04期
6 李林峰;孫長銀;;基于FCM聚類與SVM的電力系統(tǒng)短期負(fù)荷預(yù)測[J];江蘇電機(jī)工程;2007年03期
7 范勁松,方廷健;特征選擇和提取要素的分析及其評價(jià)[J];計(jì)算機(jī)工程與應(yīng)用;2001年13期
8 劉慧婷,張e，
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