本文關(guān)鍵詞：模糊診斷法在風(fēng)機(jī)故障診斷中的研究與應(yīng)用　出處：《遼寧科技大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 風(fēng)機(jī) 模糊理論 故障診斷 輔助系統(tǒng)

【摘要】：風(fēng)機(jī)廣泛應(yīng)用于國(guó)民生產(chǎn)的各個(gè)部門(mén),對(duì)國(guó)家經(jīng)濟(jì)發(fā)展起重要作用,因此對(duì)風(fēng)機(jī)開(kāi)展故障診斷和狀態(tài)監(jiān)測(cè)工作,保證風(fēng)機(jī)良好的運(yùn)行,具有重大的理論意義和現(xiàn)實(shí)意義 本論文將模糊理論應(yīng)用到風(fēng)機(jī)故障診斷中,依據(jù)風(fēng)機(jī)故障的模糊癥狀進(jìn)行狀態(tài)識(shí)別并進(jìn)行模糊推理,從而做出診斷。此方法克服了傳統(tǒng)診斷法需要獲取精確信息的困難,適應(yīng)了風(fēng)機(jī)在連續(xù)運(yùn)行過(guò)程中,由于受到工藝參數(shù)、溫度變化、周?chē)h(huán)境等因素影響,導(dǎo)致其運(yùn)行狀態(tài)和故障表象的隨機(jī)性和模糊性。 通過(guò)對(duì)風(fēng)機(jī)故障機(jī)理的研究,確定以振動(dòng)烈度和倍頻峰值作為待檢故障的征兆庫(kù),利用Matlab曲線擬合法和模糊分布法得出隸屬函數(shù)。參考專(zhuān)家經(jīng)驗(yàn)與大量診斷實(shí)例結(jié)果,初步建立模糊矩陣,選擇加權(quán)平均型模型完成模糊方程。使用大連理工大學(xué)研制的PDM2000數(shù)據(jù)采集分析儀對(duì)故障風(fēng)機(jī)進(jìn)行振動(dòng)信號(hào)采集,然后進(jìn)行時(shí)域和頻域分析,得出所需數(shù)值進(jìn)行模糊故障診斷。經(jīng)實(shí)例診斷結(jié)果對(duì)比,此方法是可行的。 采用C++語(yǔ)言開(kāi)發(fā)出風(fēng)機(jī)模糊故障診斷系統(tǒng),輔助求解隸屬函數(shù)和模糊方程并實(shí)現(xiàn)模糊矩陣的自學(xué)習(xí)功能,不斷對(duì)模糊診斷矩陣加以修正,逐步提高模糊診斷矩陣的適應(yīng)能力和準(zhǔn)確性。 使用該模型對(duì)某鋼廠內(nèi)引風(fēng)機(jī)和鼓風(fēng)機(jī)進(jìn)行測(cè)試診斷,經(jīng)信號(hào)采集、數(shù)據(jù)提取、參照選定、模糊診斷系統(tǒng)診斷等步驟,確定引風(fēng)機(jī)正常,鼓風(fēng)機(jī)為轉(zhuǎn)軸裂紋故障。停機(jī)進(jìn)行檢修,顯示診斷結(jié)果正確,證明了此模型的實(shí)用性。
[Abstract]:The fan is widely used in various departments of national production and plays an important role in the development of national economy . Therefore , it is of great theoretical and practical significance to carry out fault diagnosis and state monitoring on the fan so as to ensure the good operation of the fan . In this paper , the fuzzy theory is applied to the fault diagnosis of the fan , the state recognition is carried out according to the fuzzy symptom of the fan fault and fuzzy reasoning is carried out , so that the diagnosis is made . The method overcomes the difficulty that the traditional diagnosis method needs to acquire accurate information , and the method overcomes the influence of the factors such as process parameters , temperature changes and the surrounding environment in the continuous operation process of the fan , and leads to the randomness and the ambiguity of the operation state and the fault appearance . By studying the mechanism of fan failure , it is determined that the vibration intensity and the frequency doubling peak are used as the symptom database of the fault to be detected . The fuzzy matrix is obtained by using the Matlab curve fitting method and the fuzzy distribution method . The fuzzy matrix is established by using the PDM2000 data acquisition analyzer developed by Dalian University of Technology . The fuzzy fault diagnosis is carried out by using the PDM2000 data acquisition analyzer developed by Dalian University of Science and Technology . In this paper , the fuzzy fault diagnosis system of the fan is developed by using C ++ language , the membership function and the fuzzy equation are solved , the self - learning function of the fuzzy matrix is realized , the fuzzy diagnosis matrix is corrected , and the adaptability and the accuracy of the fuzzy diagnosis matrix are gradually improved . This model is used to diagnose the induced draft fan and blower in a steel plant . After signal acquisition , data extraction , reference selection and diagnosis of the fuzzy diagnosis system , it is determined that the fan is normal and the blower is the crack fault of the rotating shaft . The shutdown is repaired , the diagnosis result is correct , and the practicability of the model is proved .

【學(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TH165.3

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