基于信息融合技術(shù)的高速?zèng)_床故障診斷研究
發(fā)布時(shí)間：2019-06-26 17:49
【摘要】：在故障診斷的實(shí)踐中人們發(fā)現(xiàn)：(1)基于不同位置傳感器的診斷結(jié)論有時(shí)會(huì)沖突；(2)基于不同的特征域的診斷結(jié)論有時(shí)會(huì)沖突；(3)基于不同的診斷推理方法的診斷結(jié)論有時(shí)會(huì)沖突。這些都是由于大型設(shè)備結(jié)構(gòu)復(fù)雜和運(yùn)行條件多樣等所導(dǎo)致故障診斷過(guò)程中不確定大量引入,致使診斷的可靠性和準(zhǔn)確性下降,難以滿足日益大型化復(fù)雜化設(shè)備的故障診斷需求。為此,論證信息融合技術(shù)在高速?zèng)_床故障診斷中的應(yīng)用,降低故障診斷的不確定性,提高設(shè)備的診斷精度顯得尤為必要。 本文主要是從理論上和實(shí)踐中探索了信息融合技術(shù)在高速?zèng)_床振動(dòng)故障診斷系統(tǒng)中的應(yīng)用,將多個(gè)傳感器信號(hào)、設(shè)備多個(gè)方面故障特征信息和多種故障診斷推理方法綜合合理融合利用,最大限度降低診斷的不確定性,實(shí)現(xiàn)對(duì)設(shè)備全面與準(zhǔn)確的診斷。其主要工作如下： (1)通過(guò)對(duì)信息融合技術(shù)和對(duì)故障診斷過(guò)程中的不確定性進(jìn)行分析,采用信息融合技術(shù)在故障診斷中的理論框架,確立并采用了信息融合診斷組建方法,保證故障診斷過(guò)程中存在的不確定性經(jīng)達(dá)融合后能夠最大限度相互削弱,從而從理論上降低融合診斷的不確定性,達(dá)到精確診斷的目的。 (2)主元分析能夠有效處理線性問(wèn)題,核函數(shù)理論具有將低維非線性問(wèn)題轉(zhuǎn)化為高維線性問(wèn)題的特性,將主元分析和核函數(shù)理論相結(jié)合,構(gòu)成了核主元分析方法,使其對(duì)非線性問(wèn)題具備非常強(qiáng)的處理能力。將其應(yīng)用于機(jī)械設(shè)備故障特征壓縮提取,經(jīng)實(shí)驗(yàn)證實(shí)效果很好,從而成功解決多源信息融合診斷中信息量大且冗余的難題。 (3)歸納總結(jié)出神經(jīng)網(wǎng)絡(luò)在故障診斷中的具體應(yīng)用方法,并通過(guò)實(shí)驗(yàn)分析發(fā)現(xiàn),核主元分析與神經(jīng)網(wǎng)絡(luò)相結(jié)合能有效簡(jiǎn)化網(wǎng)絡(luò)結(jié)構(gòu)、緩減診斷推理的復(fù)雜度,從而提高了故障診斷的準(zhǔn)確率。 (4)將證據(jù)理論與加權(quán)思想相結(jié)合,形成了加權(quán)證據(jù)理論。它通過(guò)對(duì)各證據(jù)進(jìn)行加權(quán)組合,客觀體現(xiàn)了不同來(lái)源的證據(jù)對(duì)識(shí)別框架中各真子集的識(shí)別具有不同的可靠性和權(quán)威性這一普遍事實(shí),彌補(bǔ)了證據(jù)理論在應(yīng)用中的缺陷,為證據(jù)理論在融合故障診斷中的應(yīng)用打下基礎(chǔ)。 (5)為了將多個(gè)特征域的局部診斷結(jié)果進(jìn)行有效的決策融合,本文依據(jù)加權(quán)證據(jù)理論,通過(guò)構(gòu)建加權(quán)證據(jù)理論在故障融合診斷中的具體實(shí)施框架,并遵循第二章確立的融合診斷組建方法,驗(yàn)證了基于加權(quán)證據(jù)理論的融合故障診斷方法。 最后,對(duì)沈陽(yáng)造幣有限公司1號(hào)高速?zèng)_床進(jìn)行實(shí)驗(yàn)分析,先分別從頻域、時(shí)域和軸心軌跡三個(gè)特征域進(jìn)行局部診斷,再將三個(gè)局部診斷的結(jié)果進(jìn)行決策融合。實(shí)驗(yàn)結(jié)果表明：多故障特征信息融合后的診斷結(jié)果可信度明顯增大,不確定性明顯減小,故障診斷的準(zhǔn)確率顯著提高,充分驗(yàn)證了本文所采用的融合診斷方法的效性,并且該方法富有開(kāi)放性、易實(shí)現(xiàn),具有很強(qiáng)的工程實(shí)際應(yīng)用價(jià)值。
[Abstract]:In the practice of fault diagnosis, it is found that: (1) the diagnosis conclusions based on different position sensors sometimes conflict; (2) the diagnosis conclusions based on different feature domains sometimes conflict; (3) the diagnosis conclusions based on different diagnostic reasoning methods sometimes conflict. These are caused by the complexity of large equipment structure and various operating conditions, resulting in a large number of uncertainties in the process of fault diagnosis, resulting in the decline of reliability and accuracy of diagnosis, and it is difficult to meet the fault diagnosis requirements of increasingly large and complex equipment. Therefore, it is particularly necessary to demonstrate the application of information fusion technology in fault diagnosis of high speed punch, to reduce the uncertainty of fault diagnosis and to improve the diagnosis accuracy of equipment. In this paper, the application of information fusion technology in high speed punch vibration fault diagnosis system is explored in theory and practice. Multiple sensor signals, equipment fault characteristic information and various fault diagnosis reasoning methods are integrated and utilized reasonably, so as to reduce the uncertainty of diagnosis to the greatest extent and realize the comprehensive and accurate diagnosis of equipment. The main work is as follows: (1) through the analysis of information fusion technology and the uncertainty in the process of fault diagnosis, the theoretical framework of information fusion technology in fault diagnosis is adopted, and the construction method of information fusion diagnosis is established and adopted to ensure that the uncertainties existing in the process of fault diagnosis can weaken each other to the maximum extent after fusion, so as to reduce the uncertainty of fusion diagnosis in theory. To achieve the purpose of accurate diagnosis. (2) Principal component analysis can effectively deal with linear problems. Kernel function theory has the characteristic of transforming low-dimensional nonlinear problems into high-dimensional linear problems. Combining principal component analysis with kernel function theory, a kernel principal component analysis method is formed, which makes it have a very strong ability to deal with nonlinear problems. It is applied to the fault feature compression extraction of mechanical equipment, and the experimental results show that the effect is very good, thus successfully solving the problem of large amount of information and redundancy in multi-source information fusion diagnosis. (3) the application method of neural network in fault diagnosis is summarized, and through experimental analysis, it is found that the combination of kernel principal component analysis and neural network can effectively simplify the network structure and reduce the complexity of diagnosis reasoning, thus improving the accuracy of fault diagnosis. (4) the weighted evidence theory is formed by combining the evidence theory with the weighted thought. Through the weighted combination of each evidence, it objectively reflects the general fact that the evidence from different sources has different reliability and authority for the recognition of each true subset in the recognition framework, makes up for the defects of the evidence theory in the application, and lays the foundation for the application of the evidence theory in the fusion fault diagnosis. (5) in order to fuse the local diagnosis results of multiple feature domains effectively, according to the weighted evidence theory, this paper verifies the fusion fault diagnosis method based on weighted evidence theory by constructing the concrete implementation framework of weighted evidence theory in fault fusion diagnosis, and following the fusion diagnosis construction method established in Chapter 2. Finally, the No. 1 high speed punch of Shenyang Mint making Co., Ltd. is analyzed experimentally. the local diagnosis is carried out from three characteristic domains: frequency domain, time domain and axis trajectory, and then the results of the three local diagnosis are combined. The experimental results show that the reliability of the diagnosis results after multi-fault feature information fusion is obviously increased, the uncertainty is obviously reduced, and the accuracy of fault diagnosis is significantly improved, which fully verifies the effectiveness of the fusion diagnosis method used in this paper, and the method is open, easy to implement, and has a strong practical engineering application value.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TG385.1;TH165.3
本文編號(hào)：2506362
[Abstract]:In the practice of fault diagnosis, it is found that: (1) the diagnosis conclusions based on different position sensors sometimes conflict; (2) the diagnosis conclusions based on different feature domains sometimes conflict; (3) the diagnosis conclusions based on different diagnostic reasoning methods sometimes conflict. These are caused by the complexity of large equipment structure and various operating conditions, resulting in a large number of uncertainties in the process of fault diagnosis, resulting in the decline of reliability and accuracy of diagnosis, and it is difficult to meet the fault diagnosis requirements of increasingly large and complex equipment. Therefore, it is particularly necessary to demonstrate the application of information fusion technology in fault diagnosis of high speed punch, to reduce the uncertainty of fault diagnosis and to improve the diagnosis accuracy of equipment. In this paper, the application of information fusion technology in high speed punch vibration fault diagnosis system is explored in theory and practice. Multiple sensor signals, equipment fault characteristic information and various fault diagnosis reasoning methods are integrated and utilized reasonably, so as to reduce the uncertainty of diagnosis to the greatest extent and realize the comprehensive and accurate diagnosis of equipment. The main work is as follows: (1) through the analysis of information fusion technology and the uncertainty in the process of fault diagnosis, the theoretical framework of information fusion technology in fault diagnosis is adopted, and the construction method of information fusion diagnosis is established and adopted to ensure that the uncertainties existing in the process of fault diagnosis can weaken each other to the maximum extent after fusion, so as to reduce the uncertainty of fusion diagnosis in theory. To achieve the purpose of accurate diagnosis. (2) Principal component analysis can effectively deal with linear problems. Kernel function theory has the characteristic of transforming low-dimensional nonlinear problems into high-dimensional linear problems. Combining principal component analysis with kernel function theory, a kernel principal component analysis method is formed, which makes it have a very strong ability to deal with nonlinear problems. It is applied to the fault feature compression extraction of mechanical equipment, and the experimental results show that the effect is very good, thus successfully solving the problem of large amount of information and redundancy in multi-source information fusion diagnosis. (3) the application method of neural network in fault diagnosis is summarized, and through experimental analysis, it is found that the combination of kernel principal component analysis and neural network can effectively simplify the network structure and reduce the complexity of diagnosis reasoning, thus improving the accuracy of fault diagnosis. (4) the weighted evidence theory is formed by combining the evidence theory with the weighted thought. Through the weighted combination of each evidence, it objectively reflects the general fact that the evidence from different sources has different reliability and authority for the recognition of each true subset in the recognition framework, makes up for the defects of the evidence theory in the application, and lays the foundation for the application of the evidence theory in the fusion fault diagnosis. (5) in order to fuse the local diagnosis results of multiple feature domains effectively, according to the weighted evidence theory, this paper verifies the fusion fault diagnosis method based on weighted evidence theory by constructing the concrete implementation framework of weighted evidence theory in fault fusion diagnosis, and following the fusion diagnosis construction method established in Chapter 2. Finally, the No. 1 high speed punch of Shenyang Mint making Co., Ltd. is analyzed experimentally. the local diagnosis is carried out from three characteristic domains: frequency domain, time domain and axis trajectory, and then the results of the three local diagnosis are combined. The experimental results show that the reliability of the diagnosis results after multi-fault feature information fusion is obviously increased, the uncertainty is obviously reduced, and the accuracy of fault diagnosis is significantly improved, which fully verifies the effectiveness of the fusion diagnosis method used in this paper, and the method is open, easy to implement, and has a strong practical engineering application value.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TG385.1;TH165.3
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