本文選題：控制系統(tǒng) + 故障診斷　； 參考：《華北電力大學(xué)》2017年碩士論文

【摘要】：隨著科學(xué)技術(shù)的不斷推進(jìn),現(xiàn)代控制系統(tǒng)的規(guī)模逐漸大型化,復(fù)雜程度也日益增大。為了提高控制系統(tǒng)的安全性和可靠性,需要對(duì)整個(gè)系統(tǒng)的運(yùn)行狀態(tài)進(jìn)行監(jiān)控,及時(shí)發(fā)現(xiàn)系統(tǒng)的故障信息,進(jìn)而采取相應(yīng)的措施,防止災(zāi)難性事故的發(fā)生。復(fù)雜的控制系統(tǒng)很難得到精確的數(shù)學(xué)模型,這導(dǎo)致傳統(tǒng)的基于模型的故障診斷方法在應(yīng)用上存在很大的局限性,而由于計(jì)算機(jī)技術(shù)的迅速發(fā)展,使得控制系統(tǒng)能夠獲得越來(lái)越多的數(shù)據(jù)信息,基于數(shù)據(jù)驅(qū)動(dòng)的故障診斷方法就是在這種情況下應(yīng)運(yùn)而生的。本文以控制系統(tǒng)為研究對(duì)象,利用了基于數(shù)據(jù)驅(qū)動(dòng)的方法進(jìn)行故障診斷研究。首先,需要對(duì)控制系統(tǒng)進(jìn)行故障檢測(cè),判斷有無(wú)故障的發(fā)生。針對(duì)這一問(wèn)題,提出了基于特征提取與聚類相結(jié)合的故障檢測(cè)方法。該方法利用慢特征分析對(duì)控制系統(tǒng)的正常數(shù)據(jù)與故障數(shù)據(jù)進(jìn)行特征提取,再采用模糊C-均值聚類算法,獲得正常數(shù)據(jù)的聚類中心,并選取簇類半徑作為閾值,構(gòu)建待檢測(cè)數(shù)據(jù)與聚類中心的差值模型,判斷控制系統(tǒng)的待檢測(cè)數(shù)據(jù)相對(duì)于正常狀態(tài)是否發(fā)生了偏離。其次,構(gòu)建控制系統(tǒng)故障庫(kù),對(duì)故障類型進(jìn)行分類。整個(gè)系統(tǒng)由于引入了閉環(huán)控制規(guī)律,使得故障在系統(tǒng)內(nèi)傳播,最終可能使不同的故障產(chǎn)生相近的數(shù)據(jù),因此將上述提取的故障特征與控制系統(tǒng)的故障特性相結(jié)合,提高故障特征的區(qū)分性。利用減法聚類的自適應(yīng)特性,確定待檢測(cè)的故障是否為故障庫(kù)中已知的故障類型。然后再對(duì)待檢測(cè)的故障數(shù)據(jù)進(jìn)行模式匹配,判斷故障類型。最后,將上述方法應(yīng)用到電廠的過(guò)熱汽溫控制系統(tǒng)中進(jìn)行方法驗(yàn)證。
[Abstract]:With the continuous advancement of science and technology, the scale of modern control system is becoming larger and more complex. In order to improve the security and reliability of the control system, it is necessary to monitor the running state of the whole system, find out the fault information in time, and then take corresponding measures to prevent the occurrence of catastrophic accidents. The precise mathematical model is rare in the hybrid control system, which leads to the limitation of the traditional model based fault diagnosis method, and because of the rapid development of computer technology, the control system can obtain more and more data information. The method based on the data driven fault diagnosis is in this case. This paper takes the control system as the research object and uses the data driven method to study the fault diagnosis. First, it needs to detect the fault in the control system and judge whether there is a fault. The characteristics of the normal data and the fault data of the control system are extracted, and then the fuzzy C- mean clustering algorithm is used to obtain the clustering center of the normal data, and the cluster radius is selected as the threshold, and the difference model of the data to be detected and the cluster center is constructed to determine whether the data to be detected in the control system occurs relative to the normal state. Secondly, the fault Bank of the control system is constructed, and the fault types are classified. The whole system is introduced the closed loop control law, which makes the fault spread in the system, and may eventually cause the different faults to produce the similar data. Therefore, the fault characteristics mentioned above are combined with the fault characteristics of the control system to improve the fault characteristics. By using the adaptive characteristic of subtraction clustering, it determines whether the fault to be detected is a known fault type in the fault bank. Then, the fault data is matched by mode and the type of fault is judged. Finally, the above method is applied to the superheated steam temperature control system of the power plant to verify the method.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP273;TP277

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