本文選題：復(fù)合故障 + K-S匹配��； 參考：《太原理工大學(xué)》2017年碩士論文

【摘要】：隨著現(xiàn)代工業(yè)系統(tǒng)和制造裝備逐漸向著大型化、復(fù)雜化和精密化的方向發(fā)展,大型旋轉(zhuǎn)機(jī)械設(shè)備在現(xiàn)代工業(yè)中占有舉足輕重的地位,它們良好的運(yùn)行狀況直接關(guān)系到整個(gè)工廠安全正常的生產(chǎn)運(yùn)作。人們迫切希望能及時(shí)了解系統(tǒng)的運(yùn)行狀態(tài),提高系統(tǒng)的可靠性和有效性。目前有許多的學(xué)者致力于對(duì)旋轉(zhuǎn)機(jī)械復(fù)合故障診斷進(jìn)行研究,復(fù)合故障已經(jīng)成為當(dāng)前研究的熱點(diǎn)。由于多重故障并發(fā)時(shí),其表現(xiàn)形式是多種多樣的,不同故障特征相互混雜呈現(xiàn)出多耦合、模糊性等特征,給故障診斷帶來了極大的挑戰(zhàn)。隨著信息技術(shù)和人工智能的逐漸發(fā)展,新的技術(shù)不斷地移植、應(yīng)用到機(jī)械故障診斷中,豐富了故障診斷的理論與技術(shù),推動(dòng)著故障診斷向跟高層次發(fā)展。國(guó)內(nèi)外相關(guān)研究工作大多集中在故障預(yù)測(cè)、故障模型設(shè)計(jì)、故障目標(biāo)跟蹤等方面。本文在研讀大量文獻(xiàn)的基礎(chǔ)上,結(jié)合課題的相關(guān)研究背景,采用的是一種基于K-S和證據(jù)理論相結(jié)合的集成診斷方法,進(jìn)行的工作主要如下:(1)在實(shí)際工況下運(yùn)行的旋轉(zhuǎn)機(jī)械,其發(fā)生的故障通常都是復(fù)合故障,現(xiàn)有的診斷方法對(duì)這一問題很難處理。本文通過K-S匹配計(jì)算故障的相似度,然后通過證據(jù)理論處理其沖突,結(jié)果表明,該方法能快速地判斷機(jī)組上常見的軸系復(fù)合故障。(2)鑒于Kolmogorov Smilnov在雙樣本數(shù)據(jù)匹配具有的優(yōu)點(diǎn),本文用K-S對(duì)無量綱數(shù)據(jù)樣本的累積分布函數(shù)曲線檢驗(yàn),通過線線匹配,能較好地識(shí)別出故障。(3)針對(duì)旋轉(zhuǎn)機(jī)械復(fù)合故障的復(fù)雜性以及不確定性,本文充分利用證據(jù)理論處理不確定信息方面的優(yōu)勢(shì),對(duì)K-S匹配后的結(jié)果進(jìn)行證據(jù)理論數(shù)據(jù)融合,最后在大機(jī)組上多次實(shí)驗(yàn),驗(yàn)證了K-S匹配與證據(jù)理論數(shù)據(jù)融合方法能快速、準(zhǔn)確地判斷大機(jī)組上的復(fù)合故障。
[Abstract]:With the development of modern industrial system and manufacturing equipment towards the direction of large scale, complexity and precision, large-scale rotating machinery and equipment play an important role in modern industry.Their good operation condition is directly related to the safe and normal production operation of the whole plant.People are eager to know the running state of the system in time and improve the reliability and effectiveness of the system.At present, many scholars devote themselves to the research of complex fault diagnosis of rotating machinery, and compound fault has become a hot research topic.When multiple faults are concurrent, their forms are various, and different fault features present multiple coupling and fuzziness, which brings a great challenge to fault diagnosis.With the gradual development of information technology and artificial intelligence, new technologies are constantly transplanted and applied to mechanical fault diagnosis, which enriches the theory and technology of fault diagnosis and promotes the development of fault diagnosis to a higher level.Most of the related researches at home and abroad focus on fault prediction, fault model design, fault target tracking and so on.On the basis of reading a large number of documents and combining the related research background of the subject, this paper adopts an integrated diagnosis method based on K-S and evidence theory. The main work of this paper is as follows: 1) rotating machinery running under actual working conditions.The fault occurring is usually a complex fault, which is difficult to deal with by the existing diagnosis methods.In this paper, the similarity of faults is calculated by K-S matching, and then the conflict is dealt with by evidence theory. The results show that this method can quickly judge the common complex fault of shafting on the unit. (2) in view of the advantages of Kolmogorov / Smilnov in double sample data matching,In this paper, we use K-S to test the cumulative distribution function curve of dimensionless data samples. By line and line matching, we can better identify the fault.This paper makes full use of the advantage of evidence theory in dealing with uncertain information, carries on the evidence theory data fusion to the K-S matching result after the result, finally many experiments on the big unit, has verified the K-S matching and the evidence theory data fusion method to be quick,Accurate judgment of complex faults on large units.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH17

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