本文選題：故障檢測(cè)與診斷 + 多模態(tài)過(guò)程�。� 參考：《沈陽(yáng)理工大學(xué)》2017年碩士論文

【摘要】：隨著科技水平和工業(yè)化程度的不斷提升,工業(yè)過(guò)程中的狀態(tài)監(jiān)控和故障檢測(cè)也日趨復(fù)雜。這些復(fù)雜性主要表現(xiàn)在:多變量、非線性、強(qiáng)耦合等。此外,由于人們需求的日益多樣化,這對(duì)單一的工業(yè)過(guò)程模態(tài)提出了挑戰(zhàn)。于是,對(duì)于多模態(tài)工業(yè)過(guò)程領(lǐng)域的故障診斷技術(shù)的研究應(yīng)運(yùn)而生。這里的多模態(tài)是指由于操作條件、外界環(huán)境、過(guò)程本身固有因素或者特定需求的變化導(dǎo)致產(chǎn)生新的運(yùn)行模態(tài),使工業(yè)生產(chǎn)過(guò)程具有了多個(gè)穩(wěn)定工況。多模態(tài)工業(yè)過(guò)程越復(fù)雜,其發(fā)生故障時(shí)造成的經(jīng)濟(jì)損失和人員傷亡往往也就越大,后果也就越嚴(yán)重。因此對(duì)整個(gè)多模態(tài)過(guò)程進(jìn)行準(zhǔn)確、有效的故障診斷顯得尤為必要。本文在單模態(tài)TE過(guò)程的基礎(chǔ)上提出以多模態(tài)TE過(guò)程為研究對(duì)象,展開(kāi)對(duì)面向多模態(tài)過(guò)程故障診斷方法的研究。同時(shí)提出了一種面向多模態(tài)TE過(guò)程的集合型故障診斷方法,即GFCM-VMD-ICA-KPCA診斷方法,對(duì)多模態(tài)過(guò)程的故障進(jìn)行了有效的檢測(cè)和分離。同時(shí),為解決多模態(tài)TE過(guò)程的大數(shù)據(jù)量故障診斷,本文又提出了一種基于大數(shù)據(jù)Hadoop平臺(tái)以并行計(jì)算、分布式處理技術(shù)來(lái)進(jìn)行故障診斷和分析的方法。本文提出了面向多模態(tài)TE過(guò)程的GFCM-VMD-ICA-KPCA集合型故障診斷方法。首先采用全局模糊C均值聚類(lèi)算法(GFCM)對(duì)多模態(tài)數(shù)據(jù)進(jìn)行聚類(lèi)分析,以區(qū)分樣本數(shù)據(jù)所屬的工業(yè)模態(tài)。同時(shí)應(yīng)用變分模態(tài)分解法(VMD)對(duì)數(shù)據(jù)進(jìn)行預(yù)處理,濾除樣本數(shù)據(jù)中的噪聲。然后通過(guò)獨(dú)立主元分析(ICA)算法提取主元變量,以降低核主成分分析法(KPCA)對(duì)于變量的分析維度,提高診斷效率。最后,利用KPCA的2T、SPE控制圖和各變量貢獻(xiàn)率圖來(lái)輸出對(duì)多模態(tài)TE過(guò)程的狀態(tài)監(jiān)控和故障診斷結(jié)果。并且引入一個(gè)數(shù)值仿真實(shí)例來(lái)驗(yàn)證該方法的有效性和準(zhǔn)確性。本文針對(duì)多模態(tài)TE過(guò)程在海量數(shù)據(jù)情況下故障診斷算法效率大大降低的弊端,提出應(yīng)用大數(shù)據(jù)Hadoop平臺(tái)進(jìn)行數(shù)據(jù)分析、故障診斷的方法。首先在原始樣本數(shù)據(jù)進(jìn)行模態(tài)聚類(lèi)之后進(jìn)行數(shù)據(jù)預(yù)處理,根據(jù)數(shù)值波動(dòng)范圍轉(zhuǎn)化為相關(guān)標(biāo)識(shí)字符文件,并通過(guò)FTP工具上傳入大數(shù)據(jù)分布式文件系統(tǒng)(HDFS)。然后在MapReduce并行計(jì)算框架下編寫(xiě)字符檢測(cè)程序進(jìn)行數(shù)據(jù)分析和故障診斷。最后通過(guò)RStudio分析平臺(tái)對(duì)輸出相關(guān)的故障變量進(jìn)行可視化展示,以達(dá)到故障檢測(cè)與診斷的目的。為了驗(yàn)證所提的兩種方法的有效性,進(jìn)行了多模態(tài)TE過(guò)程的實(shí)驗(yàn)仿真。首先,將多模態(tài)TE過(guò)程的GFCM-VMD-ICA-KPCA集合型故障診斷方法與傳統(tǒng)方法KPCA方法相比較,驗(yàn)證該方法的有效性和準(zhǔn)確性。其次,將基于大數(shù)據(jù)Hadoop平臺(tái)的故障檢測(cè)方法付諸實(shí)驗(yàn),驗(yàn)證該方法的有效性。實(shí)驗(yàn)仿真結(jié)果表明本文所提出的GFCM-VMD-ICA-KPCA集合型故障診斷方法和基于大數(shù)據(jù)Hadoop平臺(tái)的故障數(shù)據(jù)診斷方法能夠有效檢測(cè)出故障,準(zhǔn)確性和快速性?xún)?yōu)于傳統(tǒng)方法。
[Abstract]:With the development of science and technology and industrialization, condition monitoring and fault detection in industrial process are becoming more and more complicated. These complexities are mainly manifested in: multivariable, nonlinear, strong coupling and so on. In addition, due to the increasing diversity of human needs, this poses a challenge to a single industrial process mode. Therefore, the research of fault diagnosis technology in the field of multimodal industrial process came into being. The multi-mode operation means that the operation conditions, the external environment, the inherent factors of the process itself or the change of the specific demand lead to the new mode of operation, which makes the industrial production process have more than one stable working condition. The more complex the multimodal industrial process is, the greater the economic loss and casualties will be, and the more serious the consequences will be. Therefore, it is necessary to make accurate and effective fault diagnosis for the whole multimodal process. On the basis of single mode te process, this paper presents a new method of fault diagnosis for multimodal te process based on multimodal te process. At the same time, a set fault diagnosis method for multimodal te process, called GFCM-VMD-ICA-KPCA diagnosis method, is proposed, which can effectively detect and separate the faults of multimodal process. At the same time, in order to solve the problem of mass data fault diagnosis in multimodal te process, this paper presents a method of fault diagnosis and analysis based on big data Hadoop platform, which is based on parallel computing and distributed processing technology. In this paper, a GFCM-VMD-ICA-KPCA set fault diagnosis method for multimodal te process is proposed. Firstly, the global fuzzy C-means clustering algorithm (GFCM) is used to analyze the multi-modal data in order to distinguish the industrial modes to which the sample data belong. At the same time, the variational mode decomposition (VMD) method is used to preprocess the data to filter the noise in the sample data. Then the independent principal component analysis (ICA) algorithm is used to extract the principal component variables in order to reduce the analysis dimension of KPCAs and improve the diagnostic efficiency. Finally, the state monitoring and fault diagnosis results of multimodal te process are outputted by using the control chart of 2T KPCA and the contribution rate diagram of each variable. A numerical simulation example is introduced to verify the validity and accuracy of the method. In this paper, a method of data analysis and fault diagnosis based on big data Hadoop platform is proposed to solve the problem that the efficiency of fault diagnosis algorithm of multimodal te process is greatly reduced under the condition of mass data. First, the data is preprocessed after modal clustering of the original sample data, and then converted into the relevant identification character files according to the range of numerical fluctuations, and then passed to the big data distributed file system (HDFS) via the FTP tool. Then the character detection program is written under the MapReduce parallel computing framework for data analysis and fault diagnosis. Finally, the output related fault variables are visualized through RStudio analysis platform to achieve the purpose of fault detection and diagnosis. In order to verify the effectiveness of the proposed two methods, the experimental simulation of multimodal te process is carried out. Firstly, the GFCM-VMD-ICA-KPCA set fault diagnosis method for multimodal te process is compared with the traditional KPCA method to verify the validity and accuracy of the method. Secondly, the fault detection method based on big data Hadoop platform is tested to verify the effectiveness of the method. The experimental results show that the proposed GFCM-VMD-ICA-KPCA set fault diagnosis method and the fault data diagnosis method based on big data Hadoop platform can effectively detect the fault, and the accuracy and rapidity are superior to the traditional methods.
【學(xué)位授予單位】：沈陽(yáng)理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP277

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本文編號(hào)：1886183