基于勢函數(shù)與壓縮感知的欠定盲源分離及應(yīng)用
發(fā)布時間：2018-12-11 13:25
【摘要】：盲源信號分離因其應(yīng)用廣泛，自提出就備受關(guān)注。正定及超定盲源分離研究已較為成熟，而測量信號少于源信號的欠定盲源分離問題在理論和算法方面卻存在著一定的技術(shù)瓶頸有待進(jìn)一步探索。 信號的稀疏性是欠定盲源分離算法的前提，但是在實(shí)際應(yīng)用中，只有極少數(shù)的信號在時域具備稀疏性，所以對于多數(shù)信號，在進(jìn)行欠定盲源分離之前必須進(jìn)行稀疏分解，使之具備稀疏性。傳統(tǒng)基于過完備原子庫的匹配追蹤稀疏分解算法存在運(yùn)算速度較慢、運(yùn)算結(jié)果精度不高等不足，鑒于此，本文將基于梯度信息改進(jìn)的粒子群優(yōu)化算法用于稀疏分解過程中尋找最佳原子，大幅增加了算法的收斂速度與分解速度，并在一定程度上也提高了算法精度。在同等精度要求的前提下，本文算法所用時間僅為經(jīng)典稀疏分解算法的五分之一。將此改進(jìn)算法用于信號去噪，對信噪比為5dB的信號進(jìn)行去噪，本文算法的去噪效果比小波去噪高7.5519dB，效果優(yōu)于小波去噪，充分體現(xiàn)了算法優(yōu)勢。 欠定盲源分離算法分為估計混疊矩陣與重構(gòu)源信號兩個步驟。傳統(tǒng)的基于K均值聚類算法及最小路徑法的欠定盲源分離兩步法存在K值難以確定，對初始值敏感，噪聲和奇異點(diǎn)難以排除以及相對缺乏理論依據(jù)等諸多不足，針對以上問題，本文提出了基于勢函數(shù)及壓縮感知理論的新型兩步算法。該算法首先利用多峰值粒子群尋優(yōu)算法改進(jìn)的勢函數(shù)法來估計混合矩陣，然后利用估計矩陣來構(gòu)建傳感矩陣，并將基于正交匹配追蹤的壓縮感知算法引入欠定盲源分離過程中，最終實(shí)現(xiàn)源信號的重構(gòu)。本文分別對混合正弦信號與混合聲音信號進(jìn)行欠定盲源分離實(shí)驗，仿真實(shí)驗結(jié)果表明，混合矩陣最高估計精度達(dá)到99.13%，重構(gòu)信號干擾比均高于10dB，很好的滿足了重構(gòu)精度的要求，驗證了本文算法的有效性。所提算法對一維混合信號的欠定盲源分離具有良好的普適性和較高的準(zhǔn)確率。 與實(shí)際工程應(yīng)用相結(jié)合，將本文算法應(yīng)用于風(fēng)機(jī)齒輪箱振動信號的分析處理與故障診斷。首先，在保留故障信息的前提下，將采集到的風(fēng)機(jī)齒輪箱振動信號利用本文改進(jìn)匹配追蹤算法進(jìn)行稀疏分解及去噪處理。其次，利用本文算法對去噪后的風(fēng)機(jī)齒輪箱振動信號進(jìn)行欠定盲源分離，對比正常運(yùn)行狀態(tài)下的信號分離結(jié)果，可以初步斷定故障點(diǎn)位置。最后，對分離后風(fēng)機(jī)齒輪箱振動信號進(jìn)行頻譜分析，并且結(jié)合故障診斷相關(guān)知識，可以最終斷定故障原因。通過風(fēng)機(jī)拆機(jī)檢修，發(fā)現(xiàn)風(fēng)機(jī)故障部位與故障原因均與推斷結(jié)果相符，在實(shí)踐中驗證了算法的可行性。 全文最后，，總結(jié)了算法的優(yōu)點(diǎn)與不足，同時結(jié)合無線傳感網(wǎng)絡(luò)，勾勒出風(fēng)機(jī)狀態(tài)無線實(shí)時監(jiān)測的美好藍(lán)圖，展望了本文算法的應(yīng)用前景。
[Abstract]:Blind source signal separation has attracted much attention because of its wide application. The study of positive definite and overdetermined blind source separation is mature, but the problem of under-determined blind source separation with less measured signal than the source signal has some technical bottlenecks in theory and algorithm to be further explored. The sparsity of the signal is the premise of the underdetermined blind source separation algorithm, but in practical applications, only a few signals have sparsity in the time domain, so for most signals, the sparse decomposition must be carried out before the underdetermined blind source separation. Make it sparse. The traditional matching tracing sparse decomposition algorithm based on over-complete atomic library has some shortcomings, such as slow operation speed and low precision. In this paper, an improved particle swarm optimization algorithm based on gradient information is used to find the best atoms in the sparse decomposition process, which greatly increases the convergence speed and decomposition speed of the algorithm, and improves the accuracy of the algorithm to a certain extent. On the premise of the same precision requirement, the time of this algorithm is only 1/5 of that of the classical sparse decomposition algorithm. The improved algorithm is applied to signal denoising and the signal to noise ratio (5dB) is de-noised. The denoising effect of this algorithm is 7.5519dBhigher than that of wavelet denoising, which fully reflects the superiority of the algorithm. The underdetermined blind source separation algorithm is divided into two steps: estimating the aliasing matrix and reconstructing the source signal. The traditional two-step undetermined blind source separation method based on K-means clustering algorithm and minimum path method is difficult to determine K value, sensitive to initial value, difficult to eliminate noise and singularity, and relatively lack of theoretical basis. In this paper, a new two-step algorithm based on potential function and compression sensing theory is proposed. Firstly, the hybrid matrix is estimated by the improved potential function method of multi-peak particle swarm optimization algorithm, and then the sensing matrix is constructed by using the estimation matrix, and the compression sensing algorithm based on orthogonal matching tracking is introduced into the process of under-determined blind source separation. Finally, the source signal is reconstructed. In this paper, the underdetermined blind source separation experiments of mixed sinusoidal signal and mixed sound signal are carried out respectively. The simulation results show that the maximum estimation accuracy of mixed matrix is 99.13 and the interference ratio of reconstructed signal is higher than 10 dB. It meets the requirements of reconstruction accuracy and verifies the effectiveness of this algorithm. The proposed algorithm has good universality and high accuracy for undetermined blind source separation of one-dimensional mixed signals. Combined with practical engineering application, this paper applies the algorithm to the vibration signal analysis and fault diagnosis of fan gearbox. Firstly, on the premise of retaining fault information, the vibration signals of fan gearbox are processed by sparse decomposition and denoising using the improved matching tracking algorithm in this paper. Secondly, by using the algorithm in this paper, the vibration signals of the de-noised fan gearbox are separated by under-determined blind source, and the location of the fault point can be preliminarily determined by comparing the results of the signal separation under normal operation. Finally, the vibration signal of the separated fan gearbox is analyzed by frequency spectrum analysis and combined with the knowledge of fault diagnosis, the cause of the fault can be determined finally. By examining and repairing the fan disassembly machine, it is found that the fault location and cause of the fan are consistent with the inferred results, and the feasibility of the algorithm is verified in practice. At the end of the paper, the advantages and disadvantages of the algorithm are summarized. At the same time, combining with the wireless sensor network, the good blueprint of wireless real-time monitoring of fan status is drawn, and the application prospect of this algorithm is prospected.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7
本文編號：2372629
[Abstract]:Blind source signal separation has attracted much attention because of its wide application. The study of positive definite and overdetermined blind source separation is mature, but the problem of under-determined blind source separation with less measured signal than the source signal has some technical bottlenecks in theory and algorithm to be further explored. The sparsity of the signal is the premise of the underdetermined blind source separation algorithm, but in practical applications, only a few signals have sparsity in the time domain, so for most signals, the sparse decomposition must be carried out before the underdetermined blind source separation. Make it sparse. The traditional matching tracing sparse decomposition algorithm based on over-complete atomic library has some shortcomings, such as slow operation speed and low precision. In this paper, an improved particle swarm optimization algorithm based on gradient information is used to find the best atoms in the sparse decomposition process, which greatly increases the convergence speed and decomposition speed of the algorithm, and improves the accuracy of the algorithm to a certain extent. On the premise of the same precision requirement, the time of this algorithm is only 1/5 of that of the classical sparse decomposition algorithm. The improved algorithm is applied to signal denoising and the signal to noise ratio (5dB) is de-noised. The denoising effect of this algorithm is 7.5519dBhigher than that of wavelet denoising, which fully reflects the superiority of the algorithm. The underdetermined blind source separation algorithm is divided into two steps: estimating the aliasing matrix and reconstructing the source signal. The traditional two-step undetermined blind source separation method based on K-means clustering algorithm and minimum path method is difficult to determine K value, sensitive to initial value, difficult to eliminate noise and singularity, and relatively lack of theoretical basis. In this paper, a new two-step algorithm based on potential function and compression sensing theory is proposed. Firstly, the hybrid matrix is estimated by the improved potential function method of multi-peak particle swarm optimization algorithm, and then the sensing matrix is constructed by using the estimation matrix, and the compression sensing algorithm based on orthogonal matching tracking is introduced into the process of under-determined blind source separation. Finally, the source signal is reconstructed. In this paper, the underdetermined blind source separation experiments of mixed sinusoidal signal and mixed sound signal are carried out respectively. The simulation results show that the maximum estimation accuracy of mixed matrix is 99.13 and the interference ratio of reconstructed signal is higher than 10 dB. It meets the requirements of reconstruction accuracy and verifies the effectiveness of this algorithm. The proposed algorithm has good universality and high accuracy for undetermined blind source separation of one-dimensional mixed signals. Combined with practical engineering application, this paper applies the algorithm to the vibration signal analysis and fault diagnosis of fan gearbox. Firstly, on the premise of retaining fault information, the vibration signals of fan gearbox are processed by sparse decomposition and denoising using the improved matching tracking algorithm in this paper. Secondly, by using the algorithm in this paper, the vibration signals of the de-noised fan gearbox are separated by under-determined blind source, and the location of the fault point can be preliminarily determined by comparing the results of the signal separation under normal operation. Finally, the vibration signal of the separated fan gearbox is analyzed by frequency spectrum analysis and combined with the knowledge of fault diagnosis, the cause of the fault can be determined finally. By examining and repairing the fan disassembly machine, it is found that the fault location and cause of the fan are consistent with the inferred results, and the feasibility of the algorithm is verified in practice. At the end of the paper, the advantages and disadvantages of the algorithm are summarized. At the same time, combining with the wireless sensor network, the good blueprint of wireless real-time monitoring of fan status is drawn, and the application prospect of this algorithm is prospected.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7
【參考文獻(xiàn)】
相關(guān)期刊論文 前3條
1 尹忠科;邵君;Pierre Vandergheynst;;利用FFT實(shí)現(xiàn)基于MP的信號稀疏分解[J];電子與信息學(xué)報;2006年04期
2 劉亞新;趙瑞珍;胡紹海;姜春暉;;用于壓縮感知信號重建的正則化自適應(yīng)匹配追蹤算法[J];電子與信息學(xué)報;2010年11期
3 ;A Method for Gear Fault Diagnosis Based on the Empirical Mode Decomposition[J];International Journal of Plant Engineering and Management;2004年04期
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