本文選題：故障預(yù)警　切入點：模糊系統(tǒng)　出處：《東北大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來我國金融行業(yè)穩(wěn)定發(fā)展,越來越多的人也意識到金融安防工作的重要性。因此逐漸衍生出了一些專業(yè)的金融押運公司,并發(fā)展成為一種新興的行業(yè)。專業(yè)押運公司所采用的押運車不同于普通車輛,其安全性與可靠性要求相對較高,并且由于其自身不易拆解等特點出現(xiàn)故障不能去普通4S店進行維修,通常要返廠維修。如果一部押運車因本身故障返廠維修,那么較長的維修周期可能導(dǎo)致整個押運調(diào)度的調(diào)整,這無論從經(jīng)濟方面還是安全性角度考慮都是不利的。 發(fā)動機是押運車的核心部位,也是押運車中故障頻發(fā)的設(shè)備系統(tǒng)。隨著汽車技術(shù)的進步,押運車電子化程度越來越高,發(fā)動機電控系統(tǒng)的功能不斷強大,同時也帶來故障節(jié)點數(shù)的節(jié)節(jié)攀升,傳統(tǒng)的故障診斷系統(tǒng)與人工經(jīng)驗的方法已經(jīng)不能滿足押運車快速準(zhǔn)確的診斷要求。此外其只能確定故障的存在與否,而對故障的趨勢判定無能為力,已難以滿足押運車的安全的需要。因此,有必要對傳統(tǒng)的故障診斷系統(tǒng)加以改進,以適應(yīng)押運車的要求。 論文的主要研究成果包括： (1)從押運車目前存在的實際問題出發(fā),綜述了國內(nèi)外現(xiàn)有的主流的故障診斷設(shè)備及方法； (2)根據(jù)押運車發(fā)動機的故障信號、故障原因、狀態(tài)信號之間的關(guān)系具有模糊性、非線性,以及傳感器采集數(shù)據(jù)具有周期性,并不能完全做到“實時”的問題。提出將狀態(tài)信息模糊化的方法,即將模糊系統(tǒng)和BP神經(jīng)網(wǎng)絡(luò)串聯(lián)結(jié)合起來,組成模糊神經(jīng)網(wǎng)絡(luò)的故障預(yù)警方法； (3)針對押運車故障預(yù)警快速準(zhǔn)確的要求,詳細設(shè)計了BP網(wǎng)絡(luò)拓撲結(jié)構(gòu),利用蟻群算法的全局搜索性優(yōu)化網(wǎng)絡(luò)連接權(quán)值,解決了訓(xùn)練過程易陷入局部極小點的問題,并提高了收斂速度； (4)針對押運車電控系統(tǒng)復(fù)雜,參數(shù)多的問題,提出將整個系統(tǒng)切分的辦法。然后以切分后的電子燃油噴射控制系統(tǒng)為例完成了基于蟻群算法的模糊神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)設(shè)計。主要包括：故障征兆信息的采集與模糊化處理,BP網(wǎng)絡(luò)結(jié)構(gòu)與參數(shù)的優(yōu)化設(shè)計。并實現(xiàn)了此網(wǎng)絡(luò)在押運車故障預(yù)警中的應(yīng)用。 論文的研究結(jié)果可以為押運車的智能故障預(yù)警系統(tǒng)的進一步開發(fā)提供依據(jù)。論文所得模型和算法對其他優(yōu)化系統(tǒng)的建設(shè)具有一定的參考價值。
[Abstract]:In recent years, with the steady development of the financial industry in our country, more and more people are also aware of the importance of financial security work. As a result, some professional financial escort companies have gradually emerged. And it has developed into a new industry. The transportation vehicles used by professional escort companies are different from ordinary vehicles, and their safety and reliability requirements are relatively high. And because it is not easy to disassemble and other characteristics such as failure can not go to the ordinary 4S shop for maintenance, usually return to the factory maintenance. If a transport vehicle due to its own failure to return to the factory maintenance, So the long maintenance period may lead to the adjustment of the entire escort scheduling, which is unfavorable both from the economic aspect and the security point of view. The engine is the core part of the truck, and it is also the equipment system with frequent faults. With the development of the automobile technology, the electronic degree of the escort truck is becoming higher and higher, and the function of the engine electronic control system is constantly powerful. At the same time, the number of fault nodes has been rising. The traditional fault diagnosis system and manual experience can not meet the requirements of rapid and accurate diagnosis of the transport vehicle. In addition, it can only determine whether the fault exists or not. Therefore, it is necessary to improve the traditional fault diagnosis system to meet the requirements of the escort vehicle. The main research results include:. 1) based on the practical problems existing in the transport vehicle at present, the existing mainstream fault diagnosis equipment and methods at home and abroad are summarized. (2) according to the fuzziness and nonlinearity of the relationship between the fault signals, the causes and the state signals of the engine, and the periodicity of the data collected by the sensor, The method of fuzzifying state information is put forward, which combines the fuzzy system and BP neural network in series to form the fault early warning method of fuzzy neural network. 3) aiming at the requirement of fast and accurate fault warning for escort vehicle, the topology structure of BP network is designed in detail, and the network connection weight is optimized by using the global search of ant colony algorithm, which solves the problem that the training process is easy to fall into local minima. The convergence rate is improved. 4) aiming at the problem of complex electric control system and many parameters of the transport vehicle, The method of dividing the whole system is put forward. Then taking the electronic fuel injection control system as an example, the design of fuzzy neural network structure based on ant colony algorithm is completed, including: fault symptom information collection and fuzziness. The optimization design of BP network structure and parameters is realized, and the application of this network in the early warning of the vehicle faults is realized. The results of this paper can provide the basis for the further development of intelligent fault early warning system. The models and algorithms obtained in this paper have some reference value for the construction of other optimization systems.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TP18;F830.91

【參考文獻】

相關(guān)期刊論文 前10條

1 張素兵,劉澤民;基于螞蟻算法的分級QoS路由調(diào)度方法[J];北京郵電大學(xué)學(xué)報;2000年04期

2 鄒政達,孫雅明,張智晟;基于蟻群優(yōu)化算法遞歸神經(jīng)網(wǎng)絡(luò)的短期負荷預(yù)測[J];電網(wǎng)技術(shù);2005年03期

3 王春生;李朝強;李素娟;;基于故障診斷專家系統(tǒng)的數(shù)控機床故障診斷[J];裝備制造技術(shù);2009年08期

4 洪炳熔;金飛虎;高慶吉;;基于蟻群算法的多層前饋神經(jīng)網(wǎng)絡(luò)[J];哈爾濱工業(yè)大學(xué)學(xué)報;2003年07期

5 陳朝陽,張代勝;神經(jīng)網(wǎng)絡(luò)技術(shù)在汽車故障診斷專家系統(tǒng)中的應(yīng)用[J];合肥工業(yè)大學(xué)學(xué)報(自然科學(xué)版);2000年01期

6 張建國;陳宜參;孫洪龍;;談國內(nèi)發(fā)動機故障診斷專家系統(tǒng)的現(xiàn)狀及發(fā)展[J];江蘇航空;2006年02期

7 張揚;曲延濱;;基于蟻群算法與神經(jīng)網(wǎng)絡(luò)的機械故障診斷方法[J];機床與液壓;2007年07期

8 姜長元;蟻群算法的理論及其應(yīng)用[J];計算機時代;2004年06期

9 陳朝陽,張代勝,任佩紅;汽車故障診斷專家系統(tǒng)的現(xiàn)狀與發(fā)展趨勢[J];機械工程學(xué)報;2003年11期

10 吳明強;史慧;朱曉華;肖開清;;故障診斷專家系統(tǒng)研究的現(xiàn)狀與展望[J];計算機測量與控制;2005年12期

相關(guān)博士學(xué)位論文 前1條

1 徐新黎;生產(chǎn)調(diào)度問題的智能優(yōu)化方法研究及應(yīng)用[D];浙江工業(yè)大學(xué);2009年

，

本文編號：1597080