本文關(guān)鍵詞： 陰性選擇算法 旋轉(zhuǎn)機(jī)械 無(wú)量綱指標(biāo) 遺傳編程 免疫檢測(cè)器 集成診斷　出處：《太原理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：人工免疫系統(tǒng)是對(duì)生物免疫系統(tǒng)的模擬,具有強(qiáng)大的信息處理能力,通過(guò)學(xué)習(xí)外界物質(zhì)的自然防御機(jī)理的學(xué)習(xí)技術(shù),提供噪聲忍耐、自學(xué)習(xí)、自組織、記憶等進(jìn)化學(xué)習(xí)機(jī)理,結(jié)合分類器、神經(jīng)網(wǎng)絡(luò)和機(jī)器推理等系統(tǒng)的一些優(yōu)點(diǎn)。受免疫系統(tǒng)“自己”與“非己”識(shí)別機(jī)理啟發(fā)得來(lái)的陰性選擇算法為故障診斷的研究提供了新思想和新方法。 本文研究一種基于陰性選擇算法和無(wú)量綱指標(biāo)的旋轉(zhuǎn)機(jī)械故障診斷方法。主要工作如下： (1)針對(duì)現(xiàn)有的無(wú)量綱指標(biāo)只對(duì)某些故障種類較為敏感,這導(dǎo)致了對(duì)其它一些故障種類分類效果可能不好。另外,隨著旋轉(zhuǎn)機(jī)械向集成化、精密化、復(fù)雜化的快速發(fā)展,旋轉(zhuǎn)機(jī)械出現(xiàn)的故障種類越來(lái)越多,必要的無(wú)量綱指標(biāo)也應(yīng)該越來(lái)越多,而目前可供使用的無(wú)量綱指標(biāo)數(shù)目有限,因此有必要針對(duì)旋轉(zhuǎn)機(jī)械構(gòu)造出一些新的無(wú)量綱指標(biāo),來(lái)克服傳統(tǒng)無(wú)量綱指標(biāo)診斷能力上的不足以及數(shù)量上的不足。本文利用遺傳編程方法對(duì)已有的五種無(wú)量綱指標(biāo)(波形指標(biāo)、峰值指標(biāo)、裕度指標(biāo)、脈沖指標(biāo)、峭度指標(biāo))進(jìn)行重新組合和優(yōu)化,構(gòu)建出對(duì)于旋轉(zhuǎn)機(jī)械設(shè)備較為常見(jiàn)的軸系、軸承座故障足夠敏感的新的復(fù)合無(wú)量綱指標(biāo)。結(jié)果表明,復(fù)合指標(biāo)對(duì)常見(jiàn)的軸系、軸承座故障具有很好的分類效果。 (2)針對(duì)經(jīng)典陰性選擇算法產(chǎn)生檢測(cè)器存在計(jì)算量大、盲目性強(qiáng)等問(wèn)題,本文利用一種變異搜索方法來(lái)產(chǎn)生檢測(cè)器,結(jié)果表明,該方法可以高效地產(chǎn)生檢測(cè)器。 (3)針對(duì)構(gòu)建無(wú)量綱指標(biāo)免疫檢測(cè)器過(guò)程中因進(jìn)行約簡(jiǎn)及聚類等分類處理導(dǎo)致了其中一部分有用故障特征信息丟失的問(wèn)題,本文研究一種簡(jiǎn)單、快速診斷的集成診斷算法來(lái)彌補(bǔ),以提高機(jī)械故障的診斷準(zhǔn)確率。最后在試驗(yàn)機(jī)組進(jìn)行驗(yàn)證,結(jié)果表明,該方法有效提高了診斷準(zhǔn)確率。
[Abstract]:Artificial immune system (AIS) is a simulation of biological immune system. It has powerful information processing ability. It provides evolutionary learning mechanisms such as noise tolerance, self-learning, self-organization, memory and so on by learning the natural defense mechanism of external substances. Combining the advantages of classifier, neural network and machine reasoning, the negative selection algorithm inspired by the recognition mechanism of immune system "self" and "non-self" provides a new idea and method for fault diagnosis. In this paper, a fault diagnosis method for rotating machinery based on negative selection algorithm and dimensionless index is studied. The main work is as follows:. 1) in view of the fact that the existing dimensionless indexes are more sensitive to some kinds of faults than others, the classification of other types of faults may not be effective. In addition, with the rapid development of integrated, precise and complicated rotating machinery, There are more and more kinds of faults in rotating machinery, and the necessary dimensionless indexes should be more and more. However, the number of dimensionless indexes available for use at present is limited, so it is necessary to construct some new dimensionless indexes for rotating machinery. In order to overcome the deficiency of traditional dimensionless index diagnosis ability and quantity, this paper uses genetic programming method to analyze five kinds of dimensionless indexes (waveform index, peak value index, margin index, pulse index). The kurtosis index) is recombined and optimized to construct a new composite dimensionless index which is sensitive enough to the shaft system which is more common for rotating machinery and equipment. The results show that the composite index is suitable for common shafting. Bearing bearing failure has good classification effect. 2) aiming at the problems of large computation and blindness in the generation detector of the classical negative selection algorithm, a mutation search method is used to generate the detector. The results show that this method can efficiently generate the detector. In order to solve the problem that some useful fault feature information is lost due to the reduction and clustering in the process of constructing a dimensionless index immune detector, a simple and fast integrated diagnosis algorithm is studied in this paper to make up for the loss of some useful fault feature information. In order to improve the accuracy of mechanical fault diagnosis, the test results show that the method can effectively improve the accuracy of diagnosis.
【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】：2013
【分類號(hào)】：TH165.3

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