本文選題：模糊聚類 + 故障診斷��； 參考：《南京航空航天大學(xué)》2012年碩士論文

【摘要】：基于模糊聚類的故障診斷技術(shù)是一類十分重要的故障診斷技術(shù)，在對復(fù)雜龐大的系統(tǒng)進(jìn)行故障診斷時(shí)有著獨(dú)特的優(yōu)勢，對系統(tǒng)先驗(yàn)知識的需求較少，不需要精確的數(shù)學(xué)解析模型，可以從大量的系統(tǒng)監(jiān)控?cái)?shù)據(jù)中獲取系統(tǒng)運(yùn)行模式信息。在工業(yè)生產(chǎn)流程和機(jī)器設(shè)備日趨復(fù)雜的今天，研究基于模糊聚類的故障診斷技術(shù)有著十分重要的理論意義和工程應(yīng)用價(jià)值。 基于模糊聚類的故障診斷技術(shù)一直是相關(guān)領(lǐng)域內(nèi)的熱點(diǎn)研究對象，從誕生之日起已經(jīng)涌現(xiàn)出了很多不同的聚類算法和診斷方法。但是在未知故障的診斷問題上的研究一直比較薄弱，并沒有形成成熟的方法和共識。本文便從未知故障的診斷問題出發(fā)，研究如何隔離未知故障與已知故障及如何隔離同一類型故障的不同強(qiáng)度，并在模糊聚類算法和在線診斷方案兩個(gè)方面進(jìn)行了深入的理論分析和大量的實(shí)驗(yàn)驗(yàn)證，分別提出了改進(jìn)型可能性GK聚類算法（IPGK）和基于故障向量的在線診斷方案，通過這兩種新方法的結(jié)合，較好的解決了未知故障的診斷問題。本文的主要內(nèi)容如下： 1、回顧故障診斷技術(shù)的發(fā)展歷程，分類介紹了幾種常見的故障診斷方法。著重總結(jié)了與基于模糊聚類的故障診斷技術(shù)相關(guān)的研究成果、研究文獻(xiàn)、基礎(chǔ)理論和技術(shù)方法，闡述了模糊聚類、模式識別和故障診斷之間的緊密聯(lián)系，對基于模糊聚類的故障診斷技術(shù)涉及到的幾個(gè)關(guān)鍵詞進(jìn)行了解釋。 2、簡單介紹了模糊數(shù)學(xué)的發(fā)展歷程，介紹了模糊聚類技術(shù)的發(fā)展歷程和理論背景，從理論分析和實(shí)驗(yàn)驗(yàn)證兩方面研究了應(yīng)用最為廣泛的模糊c-均值聚類（FCM）算法，結(jié)果表明未知故障的診斷需要聚類算法能夠檢測超橢球體或超線性分布的數(shù)據(jù)，，并具有適合檢測孤立點(diǎn)的特性。根據(jù)這一具體需求，針對FCM算法、可能性c-均值聚類（PCM）算法、改進(jìn)型可能性c-均值聚類（IPCM）算法三種有代表性的成熟算法各自的特點(diǎn)和不足，進(jìn)行算法的研究和改進(jìn)工作，提出了基于馬氏距離的改進(jìn)型可能性GK聚類算法(IPGK)。通過仿真實(shí)驗(yàn)說明，此算法能較好地處理超橢球體或超線性分布的數(shù)據(jù)，并具有適合檢測孤立點(diǎn)和診斷未知故障的特性。 3、通過理論分析和仿真實(shí)驗(yàn)指出，常見的在線診斷方法無法隔離同一類型故障的不同強(qiáng)度，會導(dǎo)致錯(cuò)誤診斷。在IPGK算法的基礎(chǔ)上進(jìn)行簡單修改即可得到單一類別數(shù)據(jù)的聚類計(jì)算方法，實(shí)現(xiàn)了在線數(shù)據(jù)聚類中心的計(jì)算。引入方向殘差的概念得到了故障強(qiáng)度的計(jì)算方法和故障數(shù)據(jù)聚類中心的分布規(guī)律，并在模糊聚類領(lǐng)域內(nèi)將方向殘差引申為故障向量，提出了基于故障向量的在線診斷方法。通過實(shí)驗(yàn)說明這一在線診斷方法可以對不同強(qiáng)度下的同一故障進(jìn)行隔離。 4、將IPGK算法和基于故障向量的在線診斷方案結(jié)合在一起形成基于模糊聚類的故障診斷流程，并在TE工業(yè)過程和近地空間飛行器仿真模型上進(jìn)行了診斷實(shí)驗(yàn)。仿真結(jié)果表明，相對于現(xiàn)有的模糊聚類故障診斷技術(shù)，本文提出的方法可以較好對未知故障和已知故障進(jìn)行隔離，也可以較好的對同一類型故障的不同強(qiáng)度進(jìn)行隔離，并具有一定的抗干擾能力。
[Abstract]:The fault diagnosis technology based on fuzzy clustering is a kind of very important fault diagnosis technology. It has a unique advantage in the fault diagnosis of complex and huge systems. It needs less prior knowledge of the system and does not need accurate mathematical analytic model. It can obtain system operation mode information from a large number of system monitoring data. With the increasing complexity of industrial production process and machine equipment, it is of great theoretical significance and engineering application value to study the fault diagnosis technology based on fuzzy clustering.
The fault diagnosis technology based on fuzzy clustering has always been a hot research object in the related fields, and many different clustering algorithms and diagnostic methods have emerged from the date of birth. However, the research on the diagnosis of unknown faults has been relatively weak, and does not form a mature method and consensus. This paper is from the unknown fault. On the basis of the diagnosis, we study how to isolate the different strengths of the unknown fault and the known fault and how to isolate the same type of fault. In the two aspects, the fuzzy clustering algorithm and the online diagnosis scheme have been analyzed in depth, and a large number of experimental verification are carried out. The improved probability GK clustering algorithm (IPGK) and fault vector based on the fault vector are proposed. Based on the combination of the two new methods, the online diagnosis scheme solves the problem of unknown fault diagnosis.
1, review the development process of fault diagnosis technology, classify several common fault diagnosis methods, summarize the research results related to the fault diagnosis technology based on fuzzy clustering, study literature, basic theory and technical method, and elaborate the close connection between fuzzy clustering, pattern recognition and fault diagnosis. Several key words involved in clustering fault diagnosis technology are explained.
2, the development course of fuzzy mathematics is briefly introduced, the development course and the theoretical background of fuzzy clustering are introduced. The most widely used fuzzy c- mean clustering (FCM) clustering algorithm is studied from two aspects of theoretical analysis and experimental verification. The result shows that the diagnosis of unknown fault needs the clustering algorithm to detect the superellipsoid or superlinear distribution. According to this specific requirement, according to this specific requirement, the characteristics and shortcomings of three representative mature algorithms of the FCM algorithm, the possibility c- mean clustering (PCM) algorithm and the improved probability c- mean clustering (IPCM) algorithm are studied and improved, and the Mahalanobis distance based modification is proposed. The progressive possibility GK clustering algorithm (IPGK). The simulation experiment shows that the algorithm can handle the data of superellipsoid or superlinear distribution better, and it has the characteristic of detecting the outlier and diagnosing the unknown fault.
3, through theoretical analysis and simulation experiments, it is pointed out that the common on-line diagnosis method can not isolate the different intensity of the same type of fault and can lead to the error diagnosis. The method of clustering calculation of single category data can be obtained by simple modification on the basis of the IPGK algorithm, and the calculation of the online data clustering center is realized. The probability of the residual error is introduced. The calculation method of the fault strength and the distribution of the cluster center of the fault data are read out, and the residual error of the direction is extended into the fault vector in the fuzzy clustering field. An on-line diagnosis method based on the fault vector is proposed. The experiment shows that the on-line diagnosis method can isolate the same fault under the same intensity.
4, the IPGK algorithm and the fault vector based online diagnosis scheme are combined to form a fault diagnosis process based on fuzzy clustering, and the diagnosis experiments are carried out on the TE industrial process and the near earth space vehicle simulation model. The simulation results show that the method proposed in this paper can be compared with the existing fuzzy clustering fault diagnosis technology. Good isolation of unknown faults and known faults can also better isolate different strengths of the same type of fault and have a certain anti-interference capability.
【學(xué)位授予單位】：南京航空航天大學(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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