本文選題：無線傳感器網絡 + 故障診斷��； 參考：《上海電力學院》2015年碩士論文

【摘要】：隨著在各種監(jiān)測系統中越來越廣泛的應用無線傳感器網絡(Wireless Sensor Networks,簡稱WSN),對無線傳感器網絡的研究也愈發(fā)重要。無線傳感器網絡節(jié)點部署之初起,便處于無人監(jiān)控和檢查的狀態(tài),傳感器網絡節(jié)點本身運行的狀態(tài)我們無從得知,不可能對其進行實時監(jiān)控或者經常檢查,傳感器網絡一旦發(fā)生故障,就可能會對監(jiān)測產生影響。因此,準確并且及時診斷出無線傳感器網絡的故障節(jié)點,盡早排除故障,能提高無線傳感器網絡運行的可靠性,保證應用無線傳感器網絡的監(jiān)測系統完成預定的監(jiān)測任務。本文對無線傳感器網絡故障診斷方法進行深入研究,研究內容有以下幾個方面:(1)研究了粗糙集理論,將無線傳感器網絡故障節(jié)點的故障類型與對應故障特征屬性做成相應的決策表,運用粗糙集理論對無線傳感器網絡故障診斷決策表進行約簡,并對基于粗糙集理論的WSN故障診斷方法進行了仿真實驗,結果證明了該方法的優(yōu)越性,但同時也反應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算法的小波神經網絡,針對其由于采用梯度算法導致的進化速度緩慢且目標函數容易陷入局部極小的問題,提出了在基于BP算法的小波神經網絡中采用增加動量項和學習率自適應調整這種方法來對小波神經網絡進行改進,通過訓練實驗證明了這種改進措施的可行性。最后,在WSN的故障診斷中應用這種改進的小波神經網絡算法進行實驗,通過實驗不僅驗證了改進的小波神經網絡算法在WSN故障診斷中的可行性,更加體現出其良好的容錯性能。(3)針對基于粗糙集理論的WSN故障診斷方法的容錯能力不足和小波神經網絡不能識別多余數據知識的缺點,本文將粗糙集理論與改進的小波神經網絡集成來解決這個問題,并在WSN節(jié)點故障診斷仿真實驗上對兩者集成的RS-IWNN故障診斷算法進行仿真。與基于粗糙集理論的WSN故障診斷方法的實驗結果比較,證明了RS-IWNN故障診斷算法的優(yōu)越性。(4)針對DFD算法存在的能耗高及診斷為“正�！钡臈l件苛刻這兩個問題,對運行于CTP協議下的無線傳感器網絡所運用的DFD故障診斷算法進行改進,以減少故障診斷所消耗的能量,同時提高故障診斷正確率。通過仿真實驗證明了該改進的故障診斷算法取得的良好效果。(5)針對目前的研究大多數集中于對WSN故障診斷算法的研究上,而忽略了對故障診斷系統設計的問題,提出一種WSN故障診斷系統的設計,并對設計內容進行了介紹。
[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.
【學位授予單位】：上海電力學院
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN929.5;TP212.9

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