Multivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time

Name: Multivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time
Author: Khodabakhsh, Athar, Arı, İsmail, Bakır, M., Ercan, Ali Özer

İsim	Multivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time
Yazar	Khodabakhsh, Athar, Arı, İsmail, Bakır, M., Ercan, Ali Özer
Basım Tarihi:	2018
Basım Yeri	- IEEE
Konu	Complex event processing, Gross error classification, Gross error detection, Oil refinery, Sensor data, Stream data, System behavior
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2169-3536
Kayıt Numarası	1b1f841a-20a9-4d29-bed9-b758e1427636
Lokasyon	Electrical & Electronics Engineering, Computer Science
Tarih	2018
Notlar	Turkish Petroleum Refineries Inc. (TUPRAS) RD Center
Örnek Metin	Large-scale oil refineries are equipped with mission-critical heavy machinery (boilers, engines, turbines, and so on) and are continuously monitored by thousands of sensors for process efficiency, environmental safety, and predictive maintenance purposes. However, sensors themselves are also prone to errors and failure. The quality of data received from these sensors should be verified before being used in system modeling. There is a need for reliable methods and systems that can provide data validation and reconciliation in real-time with high accuracy. In this paper, we develop a novel method for real-time data validation, gross error detection and classification over multivariate sensor data streams. The validated and high-quality data obtained from these processes is used for pattern analysis and modeling of industrial plants. We obtain sensor data from the power and petrochemical plants of an oil refinery and analyze them using various time-series modeling and data mining techniques that we integrate into a complex event processing engine. Next, we study the computational performance implications of the proposed methods and uncover regimes where they are sustainable over fast streams of sensor data. Finally, we detect shifts among steady-states of data, which represent systems' multiple operating modes and identify the time when a model reconstruction is required using DBSCAN clustering algorithm.
DOI	10.1109/ACCESS.2018.2877097
Cilt	6

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Multivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time

Yazar Khodabakhsh, Athar, Arı, İsmail, Bakır, M., Ercan, Ali Özer

Basım Tarihi 2018

Basım Yeri - IEEE

Konu Complex event processing, Gross error classification, Gross error detection, Oil refinery, Sensor data, Stream data, System behavior

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2169-3536

Kayıt Numarası 1b1f841a-20a9-4d29-bed9-b758e1427636

Lokasyon Electrical & Electronics Engineering, Computer Science

Tarih 2018

Notlar Turkish Petroleum Refineries Inc. (TUPRAS) RD Center

Örnek Metin Large-scale oil refineries are equipped with mission-critical heavy machinery (boilers, engines, turbines, and so on) and are continuously monitored by thousands of sensors for process efficiency, environmental safety, and predictive maintenance purposes. However, sensors themselves are also prone to errors and failure. The quality of data received from these sensors should be verified before being used in system modeling. There is a need for reliable methods and systems that can provide data validation and reconciliation in real-time with high accuracy. In this paper, we develop a novel method for real-time data validation, gross error detection and classification over multivariate sensor data streams. The validated and high-quality data obtained from these processes is used for pattern analysis and modeling of industrial plants. We obtain sensor data from the power and petrochemical plants of an oil refinery and analyze them using various time-series modeling and data mining techniques that we integrate into a complex event processing engine. Next, we study the computational performance implications of the proposed methods and uncover regimes where they are sustainable over fast streams of sensor data. Finally, we detect shifts among steady-states of data, which represent systems' multiple operating modes and identify the time when a model reconstruction is required using DBSCAN clustering algorithm.

DOI 10.1109/ACCESS.2018.2877097

Cilt 6