本文選題：監(jiān)測數(shù)據(jù)去噪 + 小波基選擇��； 參考：《西南交通大學》2017年碩士論文

【摘要】：小波變換因其具有多分辨分析和時頻局部化的優(yōu)點,特別適合用于對含有多種頻率成分且為非平穩(wěn)序列的變形監(jiān)測數(shù)據(jù)進行去噪處理并提取其中的變形信息。在使用小波變換對變形監(jiān)測數(shù)據(jù)進行去噪處理時,不同的變形監(jiān)測數(shù)據(jù)具有不同的特點(如采樣率、受噪聲污染程度等),應(yīng)當選取不同的去噪?yún)?shù),包括小波基的選取,最佳分解層數(shù)的確定以及閾值的選擇。這個問題一直是研究的重點.目前許多專家學者就最佳分解層數(shù)和閾值的選擇問題已經(jīng)進行了大量的研究探索,而最優(yōu)小波基的選取還沒有一個系統(tǒng)規(guī)范的標準。另一方面,工程體的變形信息往往表現(xiàn)為監(jiān)測數(shù)據(jù)頻率成分的變化�；谶@一事實,變形監(jiān)測數(shù)據(jù)中的變形信息提取就是要獲取數(shù)據(jù)信號頻率出現(xiàn)奇異性的地方。而在使用單子帶重構(gòu)算法對數(shù)據(jù)信號進行分析的過程中,由于Mallat算法固有的頻率混淆,常常會導(dǎo)致提取出錯誤的特征信息,得到的重構(gòu)數(shù)據(jù)也會因為與濾波器卷積而長度發(fā)生改變,進而導(dǎo)致邊界效應(yīng)問題。針對以上兩個問題,本文在查閱國內(nèi)外大量文獻的基礎(chǔ)上,嘗試從能量和熵的角度出發(fā),提出了一種能夠在一定程度上指導(dǎo)不同變形監(jiān)測數(shù)據(jù)選擇其適合的最優(yōu)小波基的融合指標。同時通過研究Mallat算法中頻率混淆的產(chǎn)生原因,在單子帶重構(gòu)改進算法的基礎(chǔ)上提出抗混疊單子帶重構(gòu)改進算法,以期能夠解決頻率混淆和信號長度變化的問題。采用本文提出的方法對仿真數(shù)據(jù)進行小波閾值去噪處理以及特征信息提取。通過分析,證明了小波基選擇的融合指標的可靠性和實用性,同時驗證了抗混疊單子帶重構(gòu)改進算法能夠克服頻率混淆的問題,保證了信號長度不受與濾波器卷積的影響,消除了邊界效應(yīng)問題。最后將提出的兩種方法應(yīng)用于變形監(jiān)測的實測數(shù)據(jù)處理中,得到了較為滿意的結(jié)果。
[Abstract]:Because wavelet transform has the advantages of multi-resolution analysis and time-frequency localization, it is especially suitable for de-noising and extracting deformation information from deformation monitoring data with multiple frequency components and non-stationary sequences. When using wavelet transform to Denoise deformation monitoring data, different deformation monitoring data have different characteristics (such as sampling rate, degree of noise pollution, etc.) different denoising parameters should be selected, including the selection of wavelet basis. The determination of the optimal decomposition layer number and the selection of the threshold value. This question has always been the focus of study. At present, many experts and scholars have done a lot of research on the selection of optimal decomposition layer number and threshold, but there is not a systematic standard for the selection of optimal wavelet basis. On the other hand, the deformation information of engineering body is often shown as monitoring the change of frequency component of the data. Based on this fact, the extraction of deformation information from deformation monitoring data is to obtain the singularity of data signal frequency. In the process of data signal analysis using single subband reconstruction algorithm, because of the inherent frequency confusion of Mallat algorithm, it often leads to the extraction of wrong feature information. The length of the reconstructed data will change because of convolution with the filter, which will lead to the boundary effect problem. In view of the above two problems, this paper tries to start from the angle of energy and entropy on the basis of consulting a lot of literature at home and abroad. An optimal wavelet basis fusion index which can guide different deformation monitoring data to a certain extent is proposed. At the same time, by studying the causes of frequency confusion in Mallat algorithm, an improved anti-aliasing single subband reconstruction algorithm is proposed based on the improved single-subband reconstruction algorithm, in order to solve the problems of frequency confusion and signal length change. The method proposed in this paper is used for wavelet threshold denoising and feature information extraction. Through analysis, the reliability and practicability of the fusion index selected by wavelet basis are proved. At the same time, it is proved that the improved anti-aliasing single subband reconstruction algorithm can overcome the problem of frequency confusion and ensure that the signal length is not affected by convolution with filter. The boundary effect is eliminated. Finally, the proposed two methods are applied to the data processing of deformation monitoring, and satisfactory results are obtained.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TU196.1

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