本文關鍵詞： 船舶柴油機 灰色關聯(lián)分析 組合預測 排氣溫度 預警　出處：《大連海事大學》2014年碩士論文　論文類型：學位論文

【摘要】：船舶柴油機作為船舶的“心臟”,其健康狀態(tài)不僅會影響航行安全,還會影響船舶公司的成本和收益。為了解決船舶柴油機在健康維護和管理過程中出現(xiàn)的欠維修和過維修問題,現(xiàn)在柴油機的維修方式已由定時維修向故障預測與健康管理方式轉變。而柴油機熱力參數(shù)的趨勢預測分析為這一轉變提供了技術支持,實現(xiàn)了對柴油機的故障狀態(tài)進行預報。 本文以某實習船主機1號氣缸排氣溫度為主要預測對象,提出一種組合預測模型對其進行趨勢預測分析,以實現(xiàn)對柴油機進行故障預報。 首先,對柴油機常規(guī)的熱力參數(shù)進行分析研究,闡述了灰色關聯(lián)分析方法的用途和計算原理,并采用灰色關聯(lián)分析法對柴油機典型熱力參數(shù)進行聚類分析,得到排氣溫度的關聯(lián)參數(shù)。 其次,分析幾種常用預測方法的優(yōu)劣,提出組合預測模型是將來的發(fā)展趨勢,并建立經(jīng)四階龍格庫塔法改進的灰預測模型與時間序列AR模型相結合的組合預測模型,分別發(fā)揮了上述兩種預測模型的優(yōu)勢。 再次,通過對排氣溫度的報警限值和預警等級界定的計算方法進行研究,實現(xiàn)了預警功能,并以排氣溫度作為主序列,各缸平均排氣溫度、掃氣溫度、主軸承出口滑油溫度、氣缸冷卻水出口溫度作為輔序列,分別選擇柴油機排氣溫度在平穩(wěn)變化和上升變化時上述五個參數(shù)的樣本數(shù)據(jù),采用聯(lián)合預測的方法對排氣溫度進行趨勢預測分析。 最后,將組合預測模型應用于實船,并將實船排氣溫度的預測值與實測值進行比較和誤差分析,以驗證預測模型的有效性。
[Abstract]:As the heart of the ship, the health status of the marine diesel engine will not only affect the safety of navigation, but also the cost and income of the shipping company, in order to solve the problem of undermaintenance and overmaintenance of the marine diesel engine in the process of health maintenance and management. Now the maintenance mode of diesel engine has been changed from regular maintenance to fault prediction and health management, and the trend prediction analysis of diesel engine thermal parameters provides technical support for this change and realizes the prediction of diesel engine fault state. In this paper, the exhaust temperature of the main engine No. 1 of a practical ship is taken as the main prediction object, and a combined forecasting model is put forward to forecast the trend of the engine in order to realize the fault prediction of the diesel engine. Firstly, the conventional thermodynamic parameters of diesel engine are analyzed, the application and calculation principle of grey correlation analysis method are expounded, and the typical thermodynamic parameters of diesel engine are analyzed by cluster analysis. Correlation parameters of exhaust temperature are obtained. Secondly, the advantages and disadvantages of several commonly used forecasting methods are analyzed, and the combined forecasting model is proposed as the development trend in the future, and the combined prediction model which combines the grey prediction model with the AR model of time series improved by the fourth order Runge-Kutta method is established. The advantages of the above two prediction models are brought into play respectively. Thirdly, by studying the alarm limit value of exhaust temperature and the calculation method of warning grade, the function of early warning is realized, and the exhaust temperature is taken as the main sequence, the average exhaust temperature of each cylinder, the scavenging temperature, the oil temperature at the outlet of the main bearing, and the oil temperature at the outlet of the main bearing. The outlet temperature of cylinder cooling water is taken as the auxiliary sequence, and the sample data of the five parameters mentioned above are selected respectively when the exhaust temperature of diesel engine changes smoothly and rising, and the trend of exhaust temperature is predicted and analyzed by using the method of joint prediction. Finally, the combined prediction model is applied to the real ship, and the prediction value of the exhaust temperature of the ship is compared with the measured value and the error analysis is carried out to verify the validity of the prediction model.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U664.121

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