Proposed Centralized Data Fusion Algorithms
- 1.1k Downloads
The trend in oil companies nowadays is to integrate the entire well sensors (modern and legacy sensors) with wireless sensor network (WSN). In this work, we introduced a new framework from such sensors using a heterogeneous network of sensors taking in our consideration the WSN’s constraints. The framework combined two modules: a Wireless Sensor Data Acquisition (WSDA) module and a Central Data Fusion (CDF) module. A test bed was established from ten acoustic sensors mounted on a closed loop pipeline. The flow rate and the differential pressure were monitored as well. The CDF module was implemented in the gateway using four fusion methods; Fuzzy Art (FA), Maximum Likelihood Estimator (MLE), Moving Average Filter (MAF) and Kalman Filter (KF). The results show that the KF fusion method is the most accurate method. Unlike the other methods, Kalman filter algorithm does not lent itself for easy implementation; this is because it involves many matrix multiplication, division and inversion. Among these 17 matrix operations, there are 10 matrix multiplications, 2 matrix inversions, 4 matrix additions and 1 matrix subtraction. Moreover, these tasks are computationally intensive and strain the energy resources of any single computational node in a WSN. In other words, most sensor nodes do not have the computational resources to complete a central KF task repeatedly. Furthermore, the computational complexity of the centralized KF is not scalable in terms of the network size.
KeywordsWireless Sensor Network Kalman Filter Maximum Likelihood Estimator Transmission Control Protocol Acoustic Sensor
- 1.I. A. Allahar, “Acoustic Signal Analysis for Sand Detection in Wells with Changing Fluid Profiles,” in Society of Petroleum Engineers, pp. 103–111, October 2003.Google Scholar
- 2.J. Sheldon, R. Kube, and Z. Hong, “Oil sand screen modelling using partial least squares regression,” in Proceeding of the IEEE International Conference on Automation and Logistics, Oingdao, China, September 2008, pp. 2936–2940.Google Scholar
- 3.A. I. Shamma’a, R. T. A. Shaw, and J. Lucas, “On line EM wave sand monitoring sensor for oil industry,” in Proceeding of the 33rd European Microwave Conference, Munich, Germany, October 2003, pp. 535–538.Google Scholar
- 4.A. Huser and O. Kvernvold, “Prediction of Sand Erosion in Process and Pipe Components,” in Proceeding of the 1st North American Conference on Multiphase Technology, Banff, Canada, August 1998, pp. 134–139.Google Scholar
- 5.Milltronics Inc., “Senaco AS100 Acoustic Sensor,” http://www.lesman.com/unleashd/catalog/belt/0460-en-00.pdf.
- 6.NuFlo Measurement Systems, “User manual,” http://www.cam.com/content/products.
- 7.Yokogawa Electronic Corporation, “EJA110A Differential Pressure Transmitter,” http://www.yokogawa.com/fld/PRESSURE/EJA/fld-eja110a-01en.htm.
- 8.P. Levis and S. Madden, “TinyOS: An operating system for wireless sensor networks,” in Ambient Intelligence Conference, Eindhoven, Netherlands, November 2004, pp. 123–129.Google Scholar
- 9.Crossbow Technology, “MICA2 Datasheet,” http://www.xbow.com.
- 10.F. Rincon, F. Moya, and J. Barbra, “Model Reuse through Hardware Design Patterns,” in Proceeding of the IEEE Design, Automation, and Test in Europe Conference and Exhibition, Munich, Germany, March 2005, pp. 324–329.Google Scholar
- 11.D. Gay, P. Levis, and D. Culler, “Software Design Patterns for TinyOS,” in ACM Transactions on Embedded Computing Systems, May 2007.Google Scholar
- 12.J. Hauer, P. Levis, V. Handziski, and D. Gay, “TinyOS Extension Proposal 101: Analog-to-Digital Convertors (ADCs),” http://www.tinyos.net/tinyos-2.x/doc/pdf/tep101.pdf.
- 13.National Semiconductor Corporation, “LMC6484 CMOS Quad Rail-to-Rail Input and Output Operational Amplifier,” http://www.datasheetcatalog.com/.
- 14.A. Abdelgawad, A. Lewis, M. Elgamel, F. Issa, N. F. Tzeng, and M. Bayoumi, “Remote Measuring of Flow Meters for Petroleum Engineering and Other Industrial Applications,” in International Workshop on Computer Architecture for Machine Perception and Sensing, Montreal, Canada, March 2007, pp. 99–103.Google Scholar
- 15.National Semiconductor Corporation, “LM741 Operational Amplifier,” http://www.national.com/.
- 16.Analog Microelectronics, “Vlotage/Current Converter AM422,” http://www.analogmicro.de/.
- 17.Microchip Technology, “PIC18F8720 datasheet,” http://ww1.micr-ochip.com/downloads/en/devicedoc/39609b.pdf.
- 18.GridSphere, http://www.gridsphere.org.
- 19.A. Abdelgawad, Z. Merhi, M. Elgamel, and M. Bayoumi, “Multisensor data fusion methods for petroleum engineering applications,” in Proceeding of the IEEE Sensors Applications Symposium, New Orleans, Louisiana, USA, March 2009, pp. 265–268.Google Scholar
- 20.M. H. DeGroot and M. J. Schervish, Probability and Statistics: Addison Wesley 2002.Google Scholar
- 21.A. Abdelgawad, Z. Merhi, M. Elgamel, M. Bayoumi, and A. Zaki, “Data fusion framework for sand detection in pipelines,” in Proceeding of the IEEE International Symposium on Circuits and Systems, Tibia, Taiwan, May 2009, pp. 2173–2176.Google Scholar
- 22.A. Abdelgawad and M. Bayoumi, “Sand Monitoring in Pipelines Using Distributed Data Fusion Algorithm,” IEEE Sensors Applications Symposium, SAS 2011, 22–24 Feb. 2011.Google Scholar