【摘要】：隨著網絡規(guī)模的不斷擴大,網絡中運行的網絡設備如路由器、交換機等設備日益增多,能夠確保網絡正常運行,維護網絡設備不出現(xiàn)故障,在出現(xiàn)故障之后能夠迅速、準確地定位問題并排除故障,對于網絡維護和管理人員是個很大的挑戰(zhàn)。 為了克服傳統(tǒng)維修方式的不足,隨著狀態(tài)監(jiān)測和故障診斷技術的不斷進步,逐漸發(fā)展起來一種新的維修方式——基于狀態(tài)的維修(CBM)。該維修方式綜合運用各種技術手段來獲取設備的運行狀態(tài)數據,然后運用故障預測和診斷技術對設備的運行狀態(tài)進行判別,并預測其發(fā)展趨勢以及診斷發(fā)生何種故障,實現(xiàn)了通過狀態(tài)監(jiān)測預測即將發(fā)生的故障,制訂合理的維修策略。故障預測技術是故障診斷技術的重要組成部分,是通過對歷史和當前的故障特征值進行分析,預測出未來的故障特征值,從而預測出設備在未來一段時間內的運行狀態(tài),預測設備可能出現(xiàn)的故障,并且依據這些特征值,判斷設備的故障級別,提前掌握設備故障的發(fā)展趨勢,為提早預防和修復故障提供依據,具有重要的理論研究價值和工程實踐意義。 本文提出了基于神經網絡的故障預測方法,引入基于狀態(tài)的維修技術,構建了基于多狀態(tài)在網運行設備故障預測模型。該模型根據故障的嚴重性將預警等級劃分為四層,對于不同的預警級別,分別構建神經網絡,解決了設備故障預測精度不高的難題,提升了基于多狀態(tài)的故障預測能力。通過收集網絡設備運行特征信息,得到設備的特征信息樣本集,應用設計完成的神經網絡對樣本集進行訓練,進一步優(yōu)化神經網絡的設計結構,建立基于神經網絡的故障預測模型,實現(xiàn)對設備故障的預測和診斷。 基于狀態(tài)的維修獲得主要是基于設備的狀態(tài)信息來預測設備的剩余壽命,以設定的優(yōu)化準則為目標對設備做出維修決策,即判斷設備是否需要進行預防性維修,如果需要,何時進行維修最合適。這種維修方式的維修間隔期是不固定的,其最大的特點是根據每個設備具體的狀態(tài),在設備故障發(fā)生前提早進行維修。對于設備,基于狀態(tài)的維修可以降低維護維修費用、提高設備的可用性和任務成功率；通過減少維修,尤其是計劃外的維修次數,縮短維修時間,提高設備運行效率；通過減少備品備件、維修人員等日常維護保障開支,降低維護和維修成本；通過狀態(tài)監(jiān)測,降低任務失敗的風險,進一步提高任務的成功率,極大的提升了設備維護和維修水平。
[Abstract]:With the continuous expansion of the network scale, the network equipment such as routers, switches and other devices running in the network is increasing day by day, which can ensure the normal operation of the network, maintain the network equipment without failure, and be able to quickly after the failure. It is a great challenge for network maintenance and management to locate and troubleshoot the problem accurately. In order to overcome the shortcomings of traditional maintenance methods, with the continuous progress of condition monitoring and fault diagnosis technology, a new maintenance mode, the condition based maintenance (CBM).), has been gradually developed. The maintenance method synthetically uses various technical means to obtain the running state data of the equipment, and then uses the fault prediction and diagnosis technology to distinguish the running state of the equipment, and predicts its development trend and what kind of fault to diagnose. Through the condition monitoring to predict the upcoming failure, a reasonable maintenance strategy is worked out. Fault prediction technology is an important part of fault diagnosis technology. By analyzing the history and current fault eigenvalues, it can predict the future fault eigenvalues, and then predict the running state of the equipment in a certain period of time. To predict the possible faults of the equipment, and to judge the fault level of the equipment according to these characteristic values, to grasp the development trend of the equipment faults in advance, and to provide the basis for the early prevention and repair of the faults. It has important theoretical research value and engineering practical significance. In this paper, a fault prediction method based on neural network is proposed, and the fault prediction model of equipment running in network based on multi-state is constructed by introducing the state-based maintenance technology. According to the severity of the fault, the model divides the warning level into four layers. For different early warning levels, neural networks are constructed, which solve the problem of low precision of equipment fault prediction and improve the ability of fault prediction based on multi-state. Through collecting the characteristic information of the network equipment, the characteristic information sample set of the equipment is obtained, and the designed neural network is used to train the sample set, and the design structure of the neural network is further optimized. The fault prediction model based on neural network is established to predict and diagnose the fault of equipment. Condition-based maintenance is mainly based on the state information of the equipment to predict the remaining life of the equipment, and make maintenance decisions on the equipment with the set optimization criteria as the goal, that is, to judge whether the equipment needs preventive maintenance, if so, When maintenance is most appropriate. The maintenance interval of this kind of maintenance method is not fixed, and its biggest characteristic is that according to the specific condition of each equipment, the maintenance should be carried out early before the failure of the equipment. For the equipment, the condition based maintenance can reduce the maintenance cost, improve the availability of the equipment and the success rate of the task, reduce the number of maintenance, especially the unplanned maintenance times, shorten the maintenance time, and improve the efficiency of the equipment operation. Reducing maintenance and maintenance costs by reducing daily maintenance support expenses such as spare parts and maintenance personnel, reducing the risk of mission failure through condition monitoring, and further improving the success rate of the task, Greatly improved the equipment maintenance and maintenance level.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.05;TP183

【引證文獻】

相關期刊論文 前2條

1 楊健華;;網絡環(huán)境大背景下的設備動態(tài)故障診斷與預測維修[J];西部廣播電視;2016年22期

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相關碩士學位論文 前3條

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3 王振華;基于日志分析的網絡設備故障預測研究[D];重慶大學;2015年

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本文編號：2177632