發(fā)布時(shí)間：2018-05-24 23:27

  本文選題：無(wú)線傳感器網(wǎng)絡(luò) + 故障診斷��； 參考：《上海電力學(xué)院》2015年碩士論文

【摘要】：隨著在各種監(jiān)測(cè)系統(tǒng)中越來(lái)越廣泛的應(yīng)用無(wú)線傳感器網(wǎng)絡(luò)(Wireless Sensor Networks,簡(jiǎn)稱WSN),對(duì)無(wú)線傳感器網(wǎng)絡(luò)的研究也愈發(fā)重要。無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)部署之初起,便處于無(wú)人監(jiān)控和檢查的狀態(tài),傳感器網(wǎng)絡(luò)節(jié)點(diǎn)本身運(yùn)行的狀態(tài)我們無(wú)從得知,不可能對(duì)其進(jìn)行實(shí)時(shí)監(jiān)控或者經(jīng)常檢查,傳感器網(wǎng)絡(luò)一旦發(fā)生故障,就可能會(huì)對(duì)監(jiān)測(cè)產(chǎn)生影響。因此,準(zhǔn)確并且及時(shí)診斷出無(wú)線傳感器網(wǎng)絡(luò)的故障節(jié)點(diǎn),盡早排除故障,能提高無(wú)線傳感器網(wǎng)絡(luò)運(yùn)行的可靠性,保證應(yīng)用無(wú)線傳感器網(wǎng)絡(luò)的監(jiān)測(cè)系統(tǒng)完成預(yù)定的監(jiān)測(cè)任務(wù)。本文對(duì)無(wú)線傳感器網(wǎng)絡(luò)故障診斷方法進(jìn)行深入研究,研究?jī)?nèi)容有以下幾個(gè)方面:(1)研究了粗糙集理論,將無(wú)線傳感器網(wǎng)絡(luò)故障節(jié)點(diǎn)的故障類型與對(duì)應(yīng)故障特征屬性做成相應(yīng)的決策表,運(yùn)用粗糙集理論對(duì)無(wú)線傳感器網(wǎng)絡(luò)故障診斷決策表進(jìn)行約簡(jiǎn),并對(duì)基于粗糙集理論的WSN故障診斷方法進(jìn)行了仿真實(shí)驗(yàn),結(jié)果證明了該方法的優(yōu)越性,但同時(shí)也反應(yīng)出基于粗糙集理論的WSN故障診斷方法的不足之處。(2)研究了基于BP算法的小波神經(jīng)網(wǎng)絡(luò),針對(duì)其由于采用梯度算法導(dǎo)致的進(jìn)化速度緩慢且目標(biāo)函數(shù)容易陷入局部極小的問(wèn)題,提出了在基于BP算法的小波神經(jīng)網(wǎng)絡(luò)中采用增加動(dòng)量項(xiàng)和學(xué)習(xí)率自適應(yīng)調(diào)整這種方法來(lái)對(duì)小波神經(jīng)網(wǎng)絡(luò)進(jìn)行改進(jìn),通過(guò)訓(xùn)練實(shí)驗(yàn)證明了這種改進(jìn)措施的可行性。最后,在WSN的故障診斷中應(yīng)用這種改進(jìn)的小波神經(jīng)網(wǎng)絡(luò)算法進(jìn)行實(shí)驗(yàn),通過(guò)實(shí)驗(yàn)不僅驗(yàn)證了改進(jìn)的小波神經(jīng)網(wǎng)絡(luò)算法在WSN故障診斷中的可行性,更加體現(xiàn)出其良好的容錯(cuò)性能。(3)針對(duì)基于粗糙集理論的WSN故障診斷方法的容錯(cuò)能力不足和小波神經(jīng)網(wǎng)絡(luò)不能識(shí)別多余數(shù)據(jù)知識(shí)的缺點(diǎn),本文將粗糙集理論與改進(jìn)的小波神經(jīng)網(wǎng)絡(luò)集成來(lái)解決這個(gè)問(wèn)題,并在WSN節(jié)點(diǎn)故障診斷仿真實(shí)驗(yàn)上對(duì)兩者集成的RS-IWNN故障診斷算法進(jìn)行仿真。與基于粗糙集理論的WSN故障診斷方法的實(shí)驗(yàn)結(jié)果比較,證明了RS-IWNN故障診斷算法的優(yōu)越性。(4)針對(duì)DFD算法存在的能耗高及診斷為“正�！钡臈l件苛刻這兩個(gè)問(wèn)題,對(duì)運(yùn)行于CTP協(xié)議下的無(wú)線傳感器網(wǎng)絡(luò)所運(yùn)用的DFD故障診斷算法進(jìn)行改進(jìn),以減少故障診斷所消耗的能量,同時(shí)提高故障診斷正確率。通過(guò)仿真實(shí)驗(yàn)證明了該改進(jìn)的故障診斷算法取得的良好效果。(5)針對(duì)目前的研究大多數(shù)集中于對(duì)WSN故障診斷算法的研究上,而忽略了對(duì)故障診斷系統(tǒng)設(shè)計(jì)的問(wèn)題,提出一種WSN故障診斷系統(tǒng)的設(shè)計(jì),并對(duì)設(shè)計(jì)內(nèi)容進(jìn)行了介紹。
[Abstract]:With the more and more extensive application of wireless sensor networks (WSNs) in various monitoring systems, the research on wireless sensor networks (WSNs) is becoming more and more important. Since the beginning of the deployment of wireless sensor network nodes, they have been in a state of unattended monitoring and inspection. We have no way to know the state of the nodes running in the sensor networks themselves, and it is impossible to monitor them in real time or to check them frequently. Sensor networks may have an impact on monitoring once they fail. Therefore, accurate and timely diagnosis of wireless sensor network fault nodes, early troubleshooting, can improve the reliability of the operation of wireless sensor networks, ensure the application of wireless sensor networks monitoring system to complete the scheduled monitoring tasks. In this paper, the methods of fault diagnosis in wireless sensor networks are deeply studied. The research contents are as follows: 1) the rough set theory is studied. The fault types of the fault nodes and the corresponding fault feature attributes of wireless sensor networks are made into the corresponding decision tables, and the rough set theory is used to reduce the fault diagnosis decision tables of wireless sensor networks. The simulation results of WSN fault diagnosis method based on rough set theory prove the superiority of the method. But it also reflects the deficiency of WSN fault diagnosis method based on rough set theory.) the wavelet neural network based on BP algorithm is studied. In view of the slow evolution speed caused by using gradient algorithm and the problem that the objective function is prone to fall into local minima, An improved wavelet neural network based on BP algorithm is proposed, which is based on the adaptive adjustment of momentum and learning rate. The feasibility of the improved method is proved by the training experiment. Finally, the improved wavelet neural network algorithm is applied to the fault diagnosis of WSN. The experiment not only verifies the feasibility of the improved wavelet neural network algorithm in WSN fault diagnosis. The fault tolerance ability of WSN fault diagnosis method based on rough set theory and the shortcoming of wavelet neural network can not recognize redundant data knowledge can be realized. In this paper, the rough set theory is integrated with the improved wavelet neural network to solve this problem, and the integrated RS-IWNN fault diagnosis algorithm is simulated in the WSN node fault diagnosis simulation experiment. Compared with the experimental results of WSN fault diagnosis method based on rough set theory, it is proved that the superiority of RS-IWNN fault diagnosis algorithm is to solve the two problems of high energy consumption and "normal" condition of DFD algorithm. In order to reduce the energy consumption of fault diagnosis and improve the accuracy of fault diagnosis, the DFD fault diagnosis algorithm used in wireless sensor networks running under CTP protocol is improved. Simulation results show that the improved fault diagnosis algorithm has a good effect. Aiming at the current research, most of the researches focus on the WSN fault diagnosis algorithm, but the design of the fault diagnosis system is ignored. The design of a WSN fault diagnosis system is presented, and the design content is introduced.
【學(xué)位授予單位】：上海電力學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN929.5;TP212.9

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