卷積混合旋轉(zhuǎn)機(jī)械故障信號的盲分離
發(fā)布時間：2018-11-29 08:21
【摘要】：隨著科學(xué)技術(shù)與現(xiàn)代化工業(yè)的不斷發(fā)展，各種工業(yè)設(shè)備日趨集成化、高速化和智能化，，設(shè)備振動監(jiān)測與故障診斷越來越重要，對振動信號的采集、分析與處理是設(shè)備故障診斷的基礎(chǔ)，傳統(tǒng)的振動信號分析方法雖然比較成熟，但是他們都有其自身的局限性。盲源分離（Blind Source Separation，BSS）是現(xiàn)代振動信號處理領(lǐng)域中的一個新的研究熱點(diǎn)，由于其可以在源信號和傳輸通道等先驗(yàn)知識均未知的情況下，僅依靠觀測信號就能恢復(fù)出源信號，目前已經(jīng)被用于語音信號處理、陣列信號處理、數(shù)據(jù)挖掘、圖像識別、生物醫(yī)學(xué)信號處理等很多領(lǐng)域。本文就盲源分離的混合模型、理論算法及其在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用等方面開展研究并取得了一些有意義的結(jié)論。 從瞬時線性混合的盲源分離模型出發(fā)，介紹了幾種基于信息論的盲源分離獨(dú)立性判據(jù)，選取三種瞬時線性混合盲源分離算法（FastICA算法、EASI算法、SOBI算法）進(jìn)行了仿真試驗(yàn)，仿真試驗(yàn)證明， FastICA算法的分離效果優(yōu)于EASI算法和SOBI算法。 考慮到實(shí)際應(yīng)用中傳感器接收的信號往往是振動源信號與傳遞通道沖擊響應(yīng)的卷積，本文重點(diǎn)研究了卷積混合信號的分離問題，對時域RLS盲解卷算法和頻域復(fù)數(shù)FastICA盲解卷算法進(jìn)行的仿真試驗(yàn)表明，時域盲解卷積算法較頻域盲解卷積算法復(fù)雜，求解速度相差數(shù)十倍。 在模擬故障試驗(yàn)臺上采集了滾動軸承外圈故障和齒輪斷齒故障振動信號，對實(shí)測信號進(jìn)行了時域RLS盲解卷與頻域復(fù)數(shù)FastICA盲解卷，更進(jìn)一步，對解卷結(jié)果進(jìn)行小波分解，得到了比較理想的分析結(jié)果。盲解卷與小波分解相結(jié)合的信號處理方法較單純的盲解卷方法及瞬時混合盲分離方法能獲取更清晰和更豐富的故障特征信息。
[Abstract]:With the continuous development of science and technology and modern industry, all kinds of industrial equipments are becoming more and more integrated, high-speed and intelligent, the vibration monitoring and fault diagnosis of equipment is more and more important, and the acquisition of vibration signals is becoming more and more important. Analysis and processing are the basis of equipment fault diagnosis. Although the traditional vibration signal analysis methods are mature, they all have their own limitations. Blind source separation (Blind Source Separation,BSS) is a new research hotspot in the field of modern vibration signal processing, because it can recover the source signal only by observation signal when the prior knowledge such as source signal and transmission channel are unknown. At present, it has been used in many fields, such as speech signal processing, array signal processing, data mining, image recognition, biomedical signal processing and so on. In this paper, the mixed model of blind source separation, theoretical algorithm and its application in fault diagnosis of rotating machinery are studied and some meaningful conclusions are obtained. Based on the instantaneous linear mixing blind source separation model, several independence criteria of blind source separation based on information theory are introduced. Three instantaneous linear mixed blind source separation algorithms (FastICA algorithm, EASI algorithm, SOBI algorithm) are selected for simulation. Simulation results show that the separation effect of FastICA algorithm is better than that of EASI algorithm and SOBI algorithm. Considering that the signal received by the sensor is usually the convolution between the vibration source signal and the shock response of the transmission channel in practical applications, the separation of convolution mixed signals is studied in this paper. The simulation results of the time domain RLS blind deconvolution algorithm and the frequency domain complex FastICA blind deconvolution algorithm show that the time domain blind deconvolution algorithm is more complex than the frequency domain blind deconvolution algorithm, and the speed difference is tens of times. The vibration signals of rolling bearing outer ring fault and gear broken tooth fault are collected on the simulated fault test bed. The measured signals are analyzed by RLS blind deconvolution in time domain and FastICA blind deconvolution in frequency domain. Furthermore, the deconvolution results are decomposed by wavelet transform. An ideal analysis result is obtained. The signal processing method combining blind deconvolution with wavelet decomposition can obtain clearer and richer fault feature information than the simple blind deconvolution method and instantaneous mixed blind separation method.
【學(xué)位授予單位】：華東交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3
本文編號：2364479
[Abstract]:With the continuous development of science and technology and modern industry, all kinds of industrial equipments are becoming more and more integrated, high-speed and intelligent, the vibration monitoring and fault diagnosis of equipment is more and more important, and the acquisition of vibration signals is becoming more and more important. Analysis and processing are the basis of equipment fault diagnosis. Although the traditional vibration signal analysis methods are mature, they all have their own limitations. Blind source separation (Blind Source Separation,BSS) is a new research hotspot in the field of modern vibration signal processing, because it can recover the source signal only by observation signal when the prior knowledge such as source signal and transmission channel are unknown. At present, it has been used in many fields, such as speech signal processing, array signal processing, data mining, image recognition, biomedical signal processing and so on. In this paper, the mixed model of blind source separation, theoretical algorithm and its application in fault diagnosis of rotating machinery are studied and some meaningful conclusions are obtained. Based on the instantaneous linear mixing blind source separation model, several independence criteria of blind source separation based on information theory are introduced. Three instantaneous linear mixed blind source separation algorithms (FastICA algorithm, EASI algorithm, SOBI algorithm) are selected for simulation. Simulation results show that the separation effect of FastICA algorithm is better than that of EASI algorithm and SOBI algorithm. Considering that the signal received by the sensor is usually the convolution between the vibration source signal and the shock response of the transmission channel in practical applications, the separation of convolution mixed signals is studied in this paper. The simulation results of the time domain RLS blind deconvolution algorithm and the frequency domain complex FastICA blind deconvolution algorithm show that the time domain blind deconvolution algorithm is more complex than the frequency domain blind deconvolution algorithm, and the speed difference is tens of times. The vibration signals of rolling bearing outer ring fault and gear broken tooth fault are collected on the simulated fault test bed. The measured signals are analyzed by RLS blind deconvolution in time domain and FastICA blind deconvolution in frequency domain. Furthermore, the deconvolution results are decomposed by wavelet transform. An ideal analysis result is obtained. The signal processing method combining blind deconvolution with wavelet decomposition can obtain clearer and richer fault feature information than the simple blind deconvolution method and instantaneous mixed blind separation method.
【學(xué)位授予單位】：華東交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3
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