基于LLTSA算法的轉(zhuǎn)子故障特征數(shù)據(jù)集降維方法研究
發(fā)布時(shí)間：2018-09-13 06:02
【摘要】：隨著旋轉(zhuǎn)機(jī)械向超大型化方向快速發(fā)展,利用運(yùn)行中采集到的機(jī)械振動(dòng)信號(hào)實(shí)施設(shè)備的狀態(tài)監(jiān)測與故障診斷,是確保其穩(wěn)定、安全、高效運(yùn)行的主要措施。故障診斷存在著信息量大、知識(shí)匱乏的問題,因此如何從故障特征數(shù)據(jù)中篩選出對(duì)故障診斷有用的特征數(shù)據(jù)子集,已成為故障診斷研究中亟需盡快解決的問題。傳統(tǒng)的線性降維方法無法對(duì)非線性故障特征數(shù)據(jù)做出正確的降維處理,因此非線性故障特征數(shù)據(jù)的降維方法研究已成為當(dāng)前研究的重點(diǎn)。此外,當(dāng)機(jī)械系統(tǒng)需要實(shí)施在線狀態(tài)監(jiān)測時(shí),正確判別新增數(shù)據(jù)的類別是保證監(jiān)測結(jié)果正確性的必要前提條件。針對(duì)以上兩問題,本研究對(duì)局部切空間排列算法(LTSA)進(jìn)行改進(jìn),并將改進(jìn)后的算法用于解決故障統(tǒng)計(jì)特征數(shù)據(jù)集的降維問題。還對(duì)通過借助增量LTSA學(xué)習(xí)算法對(duì)新增特征數(shù)據(jù)進(jìn)行正確辨識(shí)的方法進(jìn)行了探討。開展的具體研究內(nèi)容與得到的研究結(jié)論情況如下： 1)針對(duì)原始故障數(shù)據(jù)無法直接用于故障分析的問題,研究了常用于故障分析的時(shí)域特征,這些特征包括均值、標(biāo)準(zhǔn)差、峰峰值和裕度指標(biāo)等。針對(duì)經(jīng)典的線性降維方法不能滿足非線性故障數(shù)據(jù)降維要求的問題,分析比較了幾種常用的流形學(xué)習(xí)算法的特點(diǎn)。分析顯示出,該類方法在處理非線性故障數(shù)據(jù)時(shí),擁有能夠較好地保留數(shù)據(jù)本質(zhì)信息特征的優(yōu)點(diǎn)。 2)針對(duì)傳統(tǒng)的線性降維方法難以對(duì)非線性故障統(tǒng)計(jì)特征實(shí)施有效降維的問題,將局部切空間排列算法(LTSA)用于非線性故障特征數(shù)據(jù)的降維中。該方法能有效的對(duì)非線性數(shù)據(jù)進(jìn)行降維處理。但由于在降維過程中,鄰域k值的選取沒有統(tǒng)一的標(biāo)準(zhǔn),因此僅憑經(jīng)驗(yàn)和試驗(yàn)法無法滿足快速正確處理數(shù)據(jù)的要求。針對(duì)此不足,在本問題研究中引入了線性分塊算法。應(yīng)用情況表明：構(gòu)造出的新算法能對(duì)非線性數(shù)據(jù)進(jìn)行合理的局部線性分塊,從而可解決LTSA算法鄰域K值選取問題,使得降維結(jié)果得到了良好的改善。 3)針對(duì)在線監(jiān)測與診斷需要及時(shí)正確的分析與處理新增數(shù)據(jù)的問題,采用增量局部切空間排列算法(LTSA)對(duì)新增數(shù)據(jù)進(jìn)行處理。設(shè)計(jì)出的新方法在利用歷史數(shù)據(jù)信息的同時(shí)還能夠?qū)π略鰯?shù)據(jù)進(jìn)行判別分析。本項(xiàng)研究工作對(duì)設(shè)備當(dāng)前狀況的分析與未來的趨勢發(fā)展判斷具有重要參考價(jià)值。 4)將LabVIEW與MATLAB進(jìn)行結(jié)合,充分利用兩者的優(yōu)勢,成功的將LLTSA降維算法嵌入到了基于這兩種軟件的混合編程中。突出了良好的分析效果和可視化效果。 研究表明,機(jī)械系統(tǒng)非線性數(shù)據(jù)的特征生成、選擇與降維和新增數(shù)據(jù)的有效辨識(shí)是機(jī)械故障診斷研究的新發(fā)展方向。該工作能夠?yàn)閿?shù)據(jù)驅(qū)動(dòng)的智能診斷實(shí)現(xiàn)提供新思路。
[Abstract]:With the rapid development of rotating machinery in the direction of super-large-scale, it is the main measure to ensure its stability, safety and high efficiency to use the mechanical vibration signals collected in operation to implement the state monitoring and fault diagnosis of the equipment. There are many problems in fault diagnosis, such as large amount of information and lack of knowledge. Therefore, how to select a subset of feature data useful for fault diagnosis from fault feature data has become a problem that needs to be solved as soon as possible in fault diagnosis research. The traditional linear dimensionality reduction method can not deal with the nonlinear fault feature data correctly, so the research of nonlinear fault feature data dimensionality reduction method has become the focus of current research. In addition, when the mechanical system needs to carry out on-line state monitoring, it is a necessary prerequisite to correctly judge the new data categories to ensure the correctness of the monitoring results. In view of the above two problems, this paper improves the local tangent space arrangement algorithm (LTSA), and applies the improved algorithm to reduce the dimension of the fault statistical feature data set. The method of correct identification of new feature data by means of incremental LTSA learning algorithm is also discussed. The specific research contents and conclusions obtained are as follows: 1) aiming at the problem that the original fault data can not be directly used in fault analysis, the time-domain features commonly used in fault analysis are studied, which include mean value. Standard deviation, peak value and margin index, etc. Aiming at the problem that the classical linear dimensionality reduction method can not meet the dimensionality reduction requirements of nonlinear fault data, the characteristics of several commonly used manifold learning algorithms are analyzed and compared. The analysis shows that this kind of method is used to deal with nonlinear fault data. It has the advantage that the essential information features of data can be preserved well. 2) aiming at the problem that traditional linear dimensionality reduction method is difficult to effectively reduce the dimension of nonlinear fault statistical features, The local tangent space arrangement algorithm (LTSA) is used to reduce the dimension of nonlinear fault feature data. This method can effectively reduce the dimension of nonlinear data. However, in the process of dimensionality reduction, there is no uniform criterion for the selection of neighborhood k value, so the experience and experimental method alone can not meet the requirements of fast and correct data processing. To solve this problem, a linear block algorithm is introduced in this paper. The application results show that the new algorithm can reasonably divide the nonlinear data into local linear blocks, thus solving the problem of selecting the neighborhood K value of the LTSA algorithm. The dimensionality reduction results are improved well. 3) aiming at the problem that on-line monitoring and diagnosis need to analyze and process the new data correctly, the incremental local tangent space arrangement algorithm (LTSA) is used to process the new data. The new method not only uses the historical data but also discriminates the new data. This work has important reference value for the analysis of the current situation of the equipment and the judgement of the future trend development. 4) combine LabVIEW with MATLAB and make full use of the advantages of both. Successfully embed the LLTSA dimensionality reduction algorithm into the hybrid programming based on these two kinds of software. Good analysis effect and visualization effect are highlighted. It is shown that the feature generation, selection and reduction of the nonlinear data and the effective identification of the new data are the new development directions in the research of mechanical fault diagnosis. This work can provide a new idea for the realization of data-driven intelligent diagnosis.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TH165.3
本文編號(hào)：2240291
[Abstract]:With the rapid development of rotating machinery in the direction of super-large-scale, it is the main measure to ensure its stability, safety and high efficiency to use the mechanical vibration signals collected in operation to implement the state monitoring and fault diagnosis of the equipment. There are many problems in fault diagnosis, such as large amount of information and lack of knowledge. Therefore, how to select a subset of feature data useful for fault diagnosis from fault feature data has become a problem that needs to be solved as soon as possible in fault diagnosis research. The traditional linear dimensionality reduction method can not deal with the nonlinear fault feature data correctly, so the research of nonlinear fault feature data dimensionality reduction method has become the focus of current research. In addition, when the mechanical system needs to carry out on-line state monitoring, it is a necessary prerequisite to correctly judge the new data categories to ensure the correctness of the monitoring results. In view of the above two problems, this paper improves the local tangent space arrangement algorithm (LTSA), and applies the improved algorithm to reduce the dimension of the fault statistical feature data set. The method of correct identification of new feature data by means of incremental LTSA learning algorithm is also discussed. The specific research contents and conclusions obtained are as follows: 1) aiming at the problem that the original fault data can not be directly used in fault analysis, the time-domain features commonly used in fault analysis are studied, which include mean value. Standard deviation, peak value and margin index, etc. Aiming at the problem that the classical linear dimensionality reduction method can not meet the dimensionality reduction requirements of nonlinear fault data, the characteristics of several commonly used manifold learning algorithms are analyzed and compared. The analysis shows that this kind of method is used to deal with nonlinear fault data. It has the advantage that the essential information features of data can be preserved well. 2) aiming at the problem that traditional linear dimensionality reduction method is difficult to effectively reduce the dimension of nonlinear fault statistical features, The local tangent space arrangement algorithm (LTSA) is used to reduce the dimension of nonlinear fault feature data. This method can effectively reduce the dimension of nonlinear data. However, in the process of dimensionality reduction, there is no uniform criterion for the selection of neighborhood k value, so the experience and experimental method alone can not meet the requirements of fast and correct data processing. To solve this problem, a linear block algorithm is introduced in this paper. The application results show that the new algorithm can reasonably divide the nonlinear data into local linear blocks, thus solving the problem of selecting the neighborhood K value of the LTSA algorithm. The dimensionality reduction results are improved well. 3) aiming at the problem that on-line monitoring and diagnosis need to analyze and process the new data correctly, the incremental local tangent space arrangement algorithm (LTSA) is used to process the new data. The new method not only uses the historical data but also discriminates the new data. This work has important reference value for the analysis of the current situation of the equipment and the judgement of the future trend development. 4) combine LabVIEW with MATLAB and make full use of the advantages of both. Successfully embed the LLTSA dimensionality reduction algorithm into the hybrid programming based on these two kinds of software. Good analysis effect and visualization effect are highlighted. It is shown that the feature generation, selection and reduction of the nonlinear data and the effective identification of the new data are the new development directions in the research of mechanical fault diagnosis. This work can provide a new idea for the realization of data-driven intelligent diagnosis.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TH165.3
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