本文選題：退化系統(tǒng)　切入點：Wiener過程　出處：《北京交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：設(shè)備在長期運行過程中,受磨損、疲勞、腐蝕等影響,零部件喪失最初規(guī)定的功能,其性能及健康狀態(tài)不可避免的發(fā)生退化,最終導(dǎo)致設(shè)備失效。此類具有緩慢失效現(xiàn)象的系統(tǒng)稱之為緩慢退化系統(tǒng)。在緩慢退化過程中,設(shè)備質(zhì)量下降、運維成本提高;零部件失效會增加事故發(fā)生概率,造成經(jīng)濟(jì)損失、人員傷亡等不可估量的后果。因此,在系統(tǒng)運行過程中,監(jiān)測退化數(shù)據(jù)并實施預(yù)測與健康管理,及時對退化零部件采取有效的維修措施,對于切實保障退化系統(tǒng)的運行安全性、可靠性與經(jīng)濟(jì)性具有重要意義。鑒于此,本文以緩慢退化系統(tǒng)為研究對象,研究基于間歇狀態(tài)監(jiān)測的剩余壽命預(yù)測方法及控制限維修策略,主要進(jìn)行了以下研究工作:首先,獲取的退化數(shù)據(jù)受測量誤差的影響,因此,選擇卡爾曼濾波去除間歇監(jiān)測數(shù)據(jù)的測量誤差,通過實例驗證該方法用于去除緩慢退化系統(tǒng)測量誤差的可行性。其次,建立Wiener過程退化模型,并將連續(xù)Wiener過程退化模型轉(zhuǎn)化為離散形式,然后提出一種基于Wiener過程與離散卡爾曼濾波的混合退化模型的剩余壽命預(yù)測方法。以某編組閘片間歇狀態(tài)監(jiān)測數(shù)據(jù)為例驗證上述方法的可行性,為后續(xù)緩慢退化系統(tǒng)的維修策略研究奠定基礎(chǔ)。再次,在SMDP的框架下,將閘片的退化過程進(jìn)行離散化處理,轉(zhuǎn)化成有限的退化狀態(tài),以單位期望成本最低為目標(biāo)構(gòu)建預(yù)防性控制限維修優(yōu)化模型,由Wiener過程的性質(zhì)確定狀態(tài)轉(zhuǎn)移概率并采用策略迭代算法求解模型。最后,與傳統(tǒng)的基于壽命的維修策略作對比研究,驗證本文所提出的控制限維修策略的正確性與有效性,并針對預(yù)防性控制限維修策略對相關(guān)參數(shù)進(jìn)行靈敏度分析。
[Abstract]:During the long term operation, the equipment is affected by wear, fatigue, corrosion and so on. The components lose their original function, and their performance and health state inevitably degrade. This kind of system with slow failure phenomenon is called slow degradation system. In the process of slow degradation, the quality of equipment decreases and the cost of operation and maintenance increases; the failure of parts increases the probability of accidents and causes economic losses. Therefore, monitoring degradation data and implementing prediction and health management, timely and effective maintenance measures for degraded parts and components can effectively guarantee the operational safety of degraded system. The reliability and economy are of great significance. In view of this, the residual life prediction method based on intermittent state monitoring and the control limit maintenance strategy are studied in this paper. The main research work is as follows: first of all, The acquired degraded data is affected by the measurement error. Therefore, Kalman filter is selected to remove the measurement error of intermittent monitoring data, and the feasibility of this method to remove the measurement error of slow degradation system is verified by an example. The degenerate model of Wiener process is established, and the degenerate model of continuous Wiener process is transformed into discrete form. Then a method for predicting the residual life of a hybrid degradation model based on Wiener process and discrete Kalman filter is proposed. The feasibility of the method is verified by taking the intermittent state monitoring data of a marshalling sluice as an example. It lays a foundation for the following research on the maintenance strategy of slow degradation system. Thirdly, under the framework of SMDP, the degradation process of the gate is discretized and transformed into a finite degradation state. A preventive control limited maintenance optimization model is constructed with the lowest expected cost per unit. The state transition probability is determined by the nature of the Wiener process and the model is solved by the strategy iterative algorithm. Compared with the traditional maintenance strategy based on service life, the correctness and effectiveness of the proposed control limit maintenance strategy are verified, and the sensitivity analysis of the related parameters is carried out for the preventive control limit maintenance strategy.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TB114.3

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