【摘要】：旋轉(zhuǎn)機(jī)械設(shè)備運(yùn)行時(shí)產(chǎn)生的振動(dòng)信號(hào)和聲信號(hào)都蘊(yùn)含著大量用于狀態(tài)監(jiān)測(cè)和故障診斷的重要信息。當(dāng)前運(yùn)行設(shè)備的狀態(tài)變化會(huì)時(shí)刻影響這些信號(hào)的相關(guān)特征參數(shù),而常用的狀態(tài)監(jiān)測(cè)與故障診斷的主要方法就是通過處理傳感器拾取的故障信號(hào),進(jìn)而根據(jù)相關(guān)特征參數(shù)的分布情況間接掌握設(shè)備的當(dāng)前運(yùn)行狀態(tài)。因此,狀態(tài)監(jiān)測(cè)與故障診斷成功與否的前提和關(guān)鍵在于如何從強(qiáng)干擾的機(jī)械狀態(tài)信號(hào)中提煉出絕對(duì)有用、能夠客觀的評(píng)價(jià)和判斷診斷對(duì)象的狀態(tài)特征。然而,實(shí)際工業(yè)現(xiàn)場(chǎng)存在大量背景干擾噪聲、多種未知的機(jī)械結(jié)構(gòu)源信號(hào)相互耦合、致使傳感器數(shù)目小于源信號(hào)數(shù)目情況屢屢存在、加之傳感器拾取的觀測(cè)信號(hào)的傳輸過程未知等因素,致使傳感器獲取的觀測(cè)信號(hào)每每都是所有可以影響的因素和故障信號(hào)經(jīng)過多次混雜后的結(jié)果,待估計(jì)的故障源目標(biāo)信號(hào)與其他干擾信號(hào)摻雜共處,通常幾乎不能直接從觀測(cè)故障信號(hào)中得到有價(jià)值的信息。因此,為了能夠準(zhǔn)確、高效地提取故障源目標(biāo)信號(hào),首先必須盡可能地將背景噪聲和其他干擾信號(hào)進(jìn)行抑制或排除。盲源分離已經(jīng)成為信號(hào)處理學(xué)科領(lǐng)域的研究熱點(diǎn),機(jī)械信號(hào)處理也不例外。尤其是在幾乎沒有先驗(yàn)知識(shí)情況下,盲信號(hào)處理技術(shù)可以實(shí)現(xiàn)從混合信號(hào)中恢復(fù)或估計(jì)出源信號(hào),體現(xiàn)它的優(yōu)越性,是解決機(jī)械故障復(fù)合信號(hào)盲分離的一個(gè)有力的手段。但是,傳統(tǒng)的盲信號(hào)處理方法對(duì)實(shí)際工況的機(jī)械故障信號(hào)的識(shí)別和提取還存在很多不足。因此,本學(xué)位論文在國(guó)家自然科學(xué)基金項(xiàng)目和云南省科技計(jì)劃資助項(xiàng)目的資助下,以盲信號(hào)處理為研究基礎(chǔ),針對(duì)實(shí)際復(fù)雜環(huán)境下的復(fù)合機(jī)械振動(dòng)信號(hào)和機(jī)械噪聲信號(hào)提取和分離問題,使用理論研究和實(shí)驗(yàn)驗(yàn)證相結(jié)合的研究方式,初步建立了機(jī)械故障信號(hào)的欠定盲源估計(jì)和盲提取模型及方法,為機(jī)械復(fù)合故障信號(hào)的欠定源估計(jì)和分離提供一種研究思路,全文主要研究?jī)?nèi)容如下：(1)從工程實(shí)際出發(fā),介紹本文的選題背景和研究意義。就盲信號(hào)處理技術(shù)和機(jī)械故障診斷盲處理應(yīng)用的國(guó)內(nèi)外研究現(xiàn)狀進(jìn)行了較為全面的綜述,總結(jié)了目前盲信號(hào)處理技術(shù)在故障診斷領(lǐng)域應(yīng)用現(xiàn)存問題。(2)針對(duì)傳感器拾取工業(yè)現(xiàn)場(chǎng)機(jī)械設(shè)備故障信號(hào)的特點(diǎn),提出了基于形態(tài)濾波和核獨(dú)立分量分析結(jié)合算法和基于形態(tài)濾波和遺傳模擬退火的模糊C均值聚類改進(jìn)的稀疏分量分析算法。仿真和實(shí)驗(yàn)研究結(jié)果顯示,結(jié)合形態(tài)濾波技術(shù)的兩個(gè)算法在完備情況下可以解決復(fù)合故障盲分離,從而提高了算法的實(shí)用性和分離結(jié)果的可靠性。但在在欠定情況下,前者失效,而后者需要預(yù)先給定聚類數(shù)目。(3)針對(duì)工業(yè)現(xiàn)場(chǎng)源數(shù)目未知、欠定的問題和SCA算法需要事先給定源數(shù)目,建立了機(jī)械沖擊信號(hào)的源數(shù)目估計(jì)方法研究框架。在此框架基礎(chǔ)上,提出了基于總體經(jīng)驗(yàn)?zāi)B(tài)和自適應(yīng)閾值設(shè)置的奇異值分解算法。通過計(jì)算機(jī)仿真和復(fù)合故障軸承振動(dòng)信號(hào)源數(shù)目估計(jì)來驗(yàn)證該理論框架的可行性,研究表明該算法有較好適應(yīng)性。(4)針對(duì)工業(yè)現(xiàn)場(chǎng)強(qiáng)背景噪聲和欠定問題,建立壓縮感知和欠定盲解卷積等價(jià)的理論框架,在此框架基礎(chǔ)上,提出改進(jìn)形態(tài)濾波和頻域壓縮感知重構(gòu)的欠定盲提取算法。利用前面提出的源估計(jì)算法估計(jì)源信號(hào)的數(shù)目；使用形態(tài)濾波濾除背景噪聲；遺傳模擬退火優(yōu)化的FCM算法用于對(duì)混合矩陣的估計(jì),進(jìn)而根據(jù)混合矩陣構(gòu)建傳感矩陣；最后使用壓縮感知重構(gòu)算法的正交匹配算法在頻域恢復(fù)源信號(hào)。計(jì)算機(jī)仿真和實(shí)驗(yàn)研究驗(yàn)證了上述算法的正確性,表明算法可以很好地分離復(fù)合故障信號(hào)。(5)針對(duì)現(xiàn)有盲解卷積算法對(duì)單一故障聲信號(hào)有效,但可以很好提取復(fù)合故障沖擊信號(hào),而前面提出的壓縮感知重構(gòu)算法對(duì)復(fù)合故障聲信號(hào)失效。提出盲解卷積和頻域壓縮重構(gòu)結(jié)合算法,并對(duì)復(fù)合故障軸承聲信號(hào)進(jìn)行分離,得到很好的分離結(jié)果。
[Abstract]:Vibration signals and acoustic signals produced by rotating machinery equipments in operation contain a large number of important information for state monitoring and fault diagnosis. The state changes of current operating equipment will affect the relevant characteristic parameters of these signals at all times. The main method of state monitoring and fault diagnosis is picked up by processing sensors. Therefore, the prerequisite and key to the success of condition monitoring and fault diagnosis lies in how to extract absolutely useful mechanical state signals with strong interference, and can objectively evaluate and judge the state characteristics of the diagnostic object. There are a lot of background interference noises in the actual industrial field, and many unknown mechanical structure sources are coupled with each other, so that the number of sensors is less than the number of source signals. In addition, the transmission process of the observed signals picked up by the sensors is unknown, and other factors, so that the observed signals obtained by the sensors are always all factors that can be affected. As a result of mixing elements and fault signals for many times, the estimated fault source target signal doped with other interference signals can hardly get valuable information directly from the observed fault signals. Therefore, in order to extract fault source target signal accurately and efficiently, the background noise and its background noise must be combined as much as possible. Blind Source Separation (BSS) has become a research hotspot in the field of signal processing, and mechanical signal processing is no exception. Especially in the case of little prior knowledge, BSS can recover or estimate the source signal from the mixed signal, which shows its superiority and solves the mechanical problem. However, the traditional blind signal processing methods still have many deficiencies in identifying and extracting mechanical fault signals under actual working conditions. Therefore, this dissertation is based on blind signal processing with the support of the National Natural Science Foundation of China and the Yunnan Science and Technology Project. Aiming at the problem of extracting and separating vibration signals and mechanical noise signals in complex environment, the model and method of underdetermined blind source estimation and blind source extraction for mechanical fault signals are preliminarily established by combining theoretical research with experimental verification, which can provide underdetermined source estimation and separation for mechanical composite fault signals. The main contents of this paper are as follows: (1) Based on the engineering practice, the background and significance of this paper are introduced. The research status of blind signal processing technology and blind processing of mechanical fault diagnosis at home and abroad is summarized, and the application of blind signal processing technology in the field of fault diagnosis is summarized. Existing problems. (2) According to the characteristics of sensor picking up faulty signals of industrial machinery and equipment, a sparse component analysis algorithm based on morphological filtering and kernel independent component analysis and improved fuzzy C-means clustering algorithm based on morphological filtering and genetic simulated annealing are proposed. The two algorithms can solve the blind separation of complex faults under complete conditions, which improves the practicability of the algorithm and the reliability of the separation results. However, under undetermined conditions, the former is invalid, while the latter requires a predetermined number of clusters. Aim To establish a research framework for estimating the number of sources of mechanical shock signals. Based on this framework, a singular value decomposition algorithm based on total empirical mode and adaptive threshold setting is proposed. The feasibility of the theoretical framework is verified by computer simulation and the estimation of the number of sources of bearing vibration signals with compound faults. (4) A theoretical framework of compressed sensing and underdetermined blind deconvolution is proposed to solve the problem of strong background noise and underdetermined background noise. Based on this framework, an underdetermined blind extraction algorithm is proposed to improve morphological filtering and frequency domain compressed sensing reconstruction. Morphological filtering filters the background noise; genetic simulated annealing optimized FCM algorithm is used to estimate the mixed matrix, and then the sensor matrix is constructed according to the mixed matrix; finally, the orthogonal matching algorithm of compressed sensing reconstruction algorithm is used to restore the source signal in the frequency domain. (5) Blind deconvolution algorithm is effective for single fault acoustic signal, but it can extract complex fault impulse signal very well. The compressed sensing reconstruction algorithm proposed earlier is invalid for complex fault acoustic signal. A combination algorithm of blind deconvolution and frequency domain compression reconstruction is proposed, and the combined algorithm is applied to complex fault acoustic signal. The sound signals of bearings are separated and good separation results are obtained.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TH165.3

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本文編號(hào)：2249359