發(fā)布時(shí)間：2019-02-26 14:01

【摘要】：針對機(jī)坪地面空調(diào)間歇故障引起的使用效能低、維修滯后等問題,近年來通過預(yù)測來實(shí)時(shí)監(jiān)測設(shè)備的運(yùn)行狀態(tài),達(dá)到對設(shè)備的提前維修。內(nèi)容涉及數(shù)據(jù)挖掘算法中的關(guān)聯(lián)Apriori算法的改進(jìn),及其改進(jìn)算法與聚類k-means算法相結(jié)合的間歇故障預(yù)測方法,并基于此實(shí)現(xiàn)了延誤維修預(yù)測。首先對關(guān)聯(lián)Apriori算法進(jìn)行了改進(jìn)。其中針對關(guān)聯(lián)Apriori算法頻繁掃描事務(wù)數(shù)據(jù)庫低效的問題,通過實(shí)時(shí)構(gòu)造間歇故障數(shù)組并對其對應(yīng)項(xiàng)累加求和的方法來提高運(yùn)行效率。仿真表明:改進(jìn)后的算法的效率要明顯由于原算法。然后基于改進(jìn)后的AS-Apriori算法進(jìn)行二次關(guān)聯(lián),再與聚類k-means算法相結(jié)合進(jìn)行間歇故障預(yù)測。并且在初始條件更嚴(yán)格和數(shù)據(jù)集擴(kuò)大了10倍的同時(shí),對于處理數(shù)據(jù)類型和變量的不同,得到兩種故障預(yù)測結(jié)合方法(第二種是第一種的改進(jìn)方法),并且通過仿真得到:地面空調(diào)故障預(yù)測第二種結(jié)合方法更適合在實(shí)際現(xiàn)場海量故障數(shù)據(jù)的操作。最后,利用延誤維修預(yù)測估計(jì)出永久故障臨界區(qū)以安排合理維修,主要通過正態(tài)分布模型對間歇故障的維修延誤堆積預(yù)測出永久故障的臨界區(qū)。仿真表明:預(yù)測的維修波及延誤累加概率呈線性分布,即可預(yù)測性高的間歇故障更便于預(yù)先維護(hù)管理,減少永久故障的形成。
[Abstract]:Aiming at the problems of low efficiency and delayed maintenance caused by intermittent fault of apron ground air conditioning, in recent years, real-time monitoring of the running state of the equipment has been carried out through prediction, so as to achieve the advance maintenance of the equipment. This paper deals with the improvement of the associated Apriori algorithm in data mining algorithm and the intermittent fault prediction method based on the combination of the improved algorithm and the clustering k-means algorithm. Based on this, the delayed maintenance prediction is realized. Firstly, the associated Apriori algorithm is improved. In order to solve the problem that the associated Apriori algorithm scans transaction database frequently, the efficiency is improved by constructing the intermittent fault array in real-time and adding the corresponding terms to it. Simulation results show that the efficiency of the improved algorithm is obviously due to the original algorithm. Then the improved AS-Apriori algorithm is used to carry out the quadratic correlation, and then combined with the clustering k-means algorithm, the intermittent fault prediction is carried out. And while the initial conditions are stricter and the data set is 10 times larger, for the different data types and variables, two combined fault prediction methods (the second is the first improved method) are obtained. And the simulation results show that the second combination method is more suitable for the operation of mass fault data on the ground air conditioning system. Finally, the critical area of permanent fault is estimated by using the prediction of delay maintenance to arrange reasonable maintenance. The critical region of permanent fault is predicted by normal distribution model for the accumulation of maintenance delay of intermittent fault. Simulation results show that the predicted probability of maintenance and delay accumulation is linearly distributed, that is to say, intermittent faults with high predictability are easier to maintain and manage in advance and reduce the formation of permanent faults.
【學(xué)位授予單位】：中國民航大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：V351.3;TP311.13

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