本文選題：報警閾值 + 優(yōu)化��； 參考：《青島科技大學》2017年碩士論文

【摘要】：現(xiàn)代化工生產(chǎn)中,為了提高生產(chǎn)過程的安全性和穩(wěn)定性,通常需要使用報警管理系統(tǒng)對一些過程變量進行報警閾值設(shè)置。閾值設(shè)置不合理會產(chǎn)生過多無效報警,增加操作負荷,嚴重時會引發(fā)事故,導(dǎo)致報警系統(tǒng)失效。因此,對設(shè)置不合適的變量報警閾值進行優(yōu)化是十分必要的。本文通過TE(Tennessee Eastman,田納西-伊斯曼)過程和某工業(yè)原油常減壓操作實例,對多變量報警閾值優(yōu)化新方法進行了研究。針對多變量報警閾值優(yōu)化方法,本文主要做了兩方面研究。一方面,提出了基于PCA(Principal Component Analysis,主成分分析)權(quán)重和Johnson轉(zhuǎn)換的多變量報警閾值優(yōu)化方法。通過PCA計算變量權(quán)重,對變量數(shù)據(jù)進行Johnson正態(tài)轉(zhuǎn)換,利用概率密度估計求出FAR(False Alarm Rate,誤報率)和MAR(Missed Alarm Rate,漏報率),在滿足FAR降低且報警數(shù)目不超過國際標準中規(guī)定的單位時間內(nèi)限制的報警數(shù)目(通常平均每分鐘不超過一個報警)的情況下優(yōu)化報警閾值。另一方面,提出了基于報警聚類和ACO(Ant Colony Optimization,蟻群優(yōu)化)的多變量報警閾值優(yōu)化方法。通過標準化歐式距離(Euclidean Distance,歐幾里得距離)實現(xiàn)報警聚類,利用熵權(quán)法求出變量權(quán)重,擬合出變量在正、異常狀態(tài)下的概率密度函數(shù)。添加報警延時,建立關(guān)于誤報率、漏報率和AAD(Average Alarm Delay,平均報警延時)的目標函數(shù),利用ACO算法優(yōu)化目標函數(shù)。這兩種方法在一定程度上都實現(xiàn)了優(yōu)化閾值的目的。通過TE過程和某工業(yè)原油常減壓操作實例對本文研究方法進行了驗證。結(jié)果表明,與傳統(tǒng)方法相比,該方法更能有效減少報警次數(shù)和報警率,在報警閾值優(yōu)化方面具有優(yōu)勢。
[Abstract]:In modern chemical production, in order to improve the safety and stability of the production process, alarm management system is usually used to set the alarm threshold for some process variables. The unreasonable setting of threshold will cause too many invalid alarms, increase the operating load, and lead to accidents when serious, which will lead to the failure of alarm system. Therefore, it is necessary to optimize the setting of inappropriate variable alarm threshold. Based on the TE(Tennessee Eastman (Tennessee Eastman) process and an example of an industrial crude oil operating under atmospheric and vacuum pressure, a new method of multivariable alarm threshold optimization is studied in this paper. Aiming at the optimization method of multivariable alarm threshold, this paper mainly researches on two aspects. On the one hand, a multivariable alarm threshold optimization method based on PCA(Principal Component Analysis (PCA) weight and Johnson conversion is proposed. The variable weight is calculated by PCA, and the variable data is transformed by Johnson normality. Using probability density estimation to calculate FAR(False Alarm rate (false alarm rate) and MAR(Missed Alarm rate, false alarm rate, the number of alerts (usually not exceeding the average per minute per minute) within the limit of the number of alerts per unit time specified in the international standard for satisfying the decrease of FAR and not exceeding the limit per unit time specified in international standards An alarm) is optimized in the case of an alarm threshold. On the other hand, a multivariable alarm threshold optimization method based on alarm clustering and ACO(Ant Colony optimization (ant colony optimization) is proposed. The alarm clustering is realized by Euclidean distance (Euclidean distance), the weight of variables is calculated by entropy weight method, and the probability density function of variables in positive and abnormal state is fitted. The objective function of false alarm rate, false alarm rate and AAD(Average Alarm delay (average alarm delay) is established by adding alarm delay. ACO algorithm is used to optimize the objective function. To some extent, these two methods achieve the purpose of optimizing the threshold. The method of this paper is verified by te process and an example of an industrial crude oil operating at atmospheric and vacuum pressure. The results show that compared with the traditional method, this method can effectively reduce the alarm frequency and alarm rate, and has advantages in the optimization of alarm threshold.
【學位授予單位】：青島科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TQ086

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本文編號：1788010