本文選題：κ近鄰 + 間歇過程��； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：間歇過程作為現(xiàn)代工業(yè)生產(chǎn)的一種重要方式,已經(jīng)被廣泛地應(yīng)用于染料、食品、制藥等眾多領(lǐng)域。目前,將過程監(jiān)控技術(shù)應(yīng)用到間歇過程中,建立監(jiān)測系統(tǒng)對間歇過程進行異常檢測,已成為工業(yè)控制領(lǐng)域的一個重要研究方向。本文基于κ近鄰方法,開展有關(guān)間歇過程實時監(jiān)測的研究,具體的研究內(nèi)容如下:(1)提出一種基于κ近鄰的實時監(jiān)測間歇過程方法。該方法首先將間歇過程的歷史數(shù)據(jù)沿著時間軸切割成多個時間片數(shù)據(jù);然后,利用κ近鄰方法基于各個時間片數(shù)據(jù)建立對應(yīng)的單時刻模型,并基于這些模型實時地監(jiān)測間歇過程。(2)提出一種提高過程監(jiān)控實時性的快速κ近鄰方法�；贓2LSH加速κ近鄰算法的近鄰搜索過程,利用E2LSH算法從訓(xùn)練數(shù)據(jù)集中剔除與當前查詢樣本差異較大的樣本,在剩余樣本中建立κ近鄰監(jiān)控模型進行異常檢測。(3)提出一種適合過程多時段特性的κ近鄰實時監(jiān)測方法。首先,基于隨機投影和K-均值聚類提出一種時段劃分方法,該方法基于隨機投影后的測量數(shù)據(jù),利用K-均值聚類算法將間歇過程的整個生產(chǎn)周期劃分成多個子時段;然后,利用κ近鄰方法建立子時段監(jiān)控模型實時地監(jiān)測間歇過程。
[Abstract]:As an important way of modern industrial production, batch process has been widely used in many fields, such as dyes, food, pharmacy and so on. At present, it has become an important research direction in the field of industrial control to apply process monitoring technology to batch process and to establish monitoring system to detect the anomaly of batch process. In this paper, the real-time monitoring of batch processes is carried out based on 魏 nearest neighbor method. The specific research contents are as follows: 1) A real-time monitoring method for batch processes based on 魏 nearest neighbor is proposed. In this method, the historical data of the batch process are first cut along the time axis into a plurality of time slice data, and then the corresponding single time model is established based on each time slice data by using the 魏 nearest neighbor method. Based on these models, a fast 魏 nearest neighbor method is proposed to improve the real-time performance of process monitoring. Based on E2LSH to speed up the nearest neighbor search process of 魏 nearest neighbor algorithm, the E2LSH algorithm is used to remove samples from the training dataset that are quite different from the current query samples. In this paper, a 魏 nearest neighbor monitoring model is established for anomaly detection in the remaining samples. (3) A real-time 魏 nearest neighbor monitoring method is proposed, which is suitable for multi-time process characteristics. Firstly, a time division method based on random projection and K-means clustering is proposed. Based on the measured data after random projection, the whole production cycle of batch process is divided into several sub-periods by using K-means clustering algorithm. A subperiod monitoring model is established by using 魏 -nearest neighbor method to monitor batch processes in real time.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP274

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