Design of gas flow control system based on ARM and information fusion

Design of gas flow control system based on ARM and information fusion LIUYongchun 1st

School of Automation & electronic information Sichuan University of Science & Engineering

Zigong, P.R.China [email protected]

YU Xiaohong 2nd School of Electronic Information Engineering

Chengdu University Chengdu, P.R.China

YANG Jing 3rd School of Foreign Languages

Sichuan University of Science & Engineering Zigong, P.R.China

Abstract— Gas flow relates to multi-information such as temperature, pressure, differential pressure in gas flow control system. A complete description of flow can be obtained by using ART2 network and BT network for secondary treatment of sensor data as temperature, pressure, differential sensor related to flow. And then a gas flow automation control program is proposed according to the prior description. The Processor S3C2410 with ARM920T kernel has the characteristics of strong information processing capability and abundant expanded functional modules. It takes the processor as the center of the whole system.

Keywords-sensor information fusion; flow control; ARM; Neural Networks

I. INTRODUCTION China’s natural gas metering is under the statutory

requirements of the quality indicators in terms of volume or method of energy measurement, mainly in terms of volume measurement currently. Orifice metering law is a common method used for natural gas flow measurement. Parameters like gas differential pressure, pressure and temperature affect the size of flow from different sides respectively, but they can not completely determine the size of the actual flow of natural gas. In traditional natural flow control system, information from different sensors is analyzed and dealt with separately without consideration of the relevance of information among certain sensors，which wasted a lot of very useful information resources. In this paper, data fusion technology is used to optimize the combination of observational information from differential pressure transmitter, pressure sensors and temperature sensors, which produce a consistent explanation and description of natural gas flow, and also it uses processor Samsung S3C2410 with ARM920T kernel as the center of the system [1].

II. MULTI-SENSOR INFORMATION FUSION

A. Overview of multi-sensor information fusion In the multi-sensors system, information from different sensors may has a different feature such as time varying or non time-varying, real-time or non real-time，vague or

determined，accurate or not complete, mutually supportive or complementary etc. Multi-sensor information fusion

means making full use of multiple sensors resources, using appropriate fusion algorithm to optimize and synthesize observational information afforded by multiple sensors with different location and different function to look for the inner relationship and law in all kinds of information .It eliminates redundant information between the multi-sensors and keep accurate and useful information to obtain a consistent and complete description about tested thing finally. Multi-sensor information fusion has the advantage in a shorter period of time a smaller pay to obtain more accurate and complete information which can be not obtained by a single sensor, to enhance the effectiveness of the entire system. Information fusion techniques involving multi-disciplinary theory and techniques such as information processing, pattern recognition, neural network and artificial intelligence. Common information fusion method concludes estimation methods, statistical methods, neural networks, genetic algorithms, etc. Among these, neural network is a highly non-linear super-large-scale parallel information processing system, which has the ability of network mapping transformation, network learning and network generalization. It has a strong information processing ability to intelligent monitoring of complex industrial control systems with information uncertainty and affords a good method for information fusion. In this paper, this method is used to make gas flow test data secondary fusion [2].

B. The fusion structure of gas flow test data In natural gas flow control system, the flow relates to

the temperature, pressure, differential pressure of the gas. A single sensor output can just reflect one aspect of flow information, but fusing multiple sensors information can obtain a complete description of flow and control the flow effectively according to the description. Fig 1 shows the control principal of gas flow based on data fusion, whose structure includes:

1) Sensor information collection. Temperature, pressure, differential pressure sensors

collect temperature, pressure, differential pressure information related to flow separately, corresponding A/D converter module and detection module which reflects the

2010 International Conference on Intelligent Computation Technology and Automation

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working state of sensors. 2) Sensor information fusion.

It consists of two neural network fusion algorithms. The first one uses three ART-2 neural networks to perform data fusion of time-series from three sensors. The second one uses one BP neural network to perform secondary fusion to the output of the first fusion.

3) Automation control module. It is composed by control decision and actuators

(natural gas pipeline valves) The mathematical model of the natural gas flow is：

PHFFFCEdAQ TzGs ε2= (1) Where Q is the standard state gas volume flow, sm /3

, sA is the flow coefficient，d is the diameter of Orifice, GF

is the relative density coefficient, ε is the expansion coefficient, zF is the super-compression factor, TF is the temperature coefficient of flow, P is the absolute static pressure of flow of measuring hole on orifice upstream，H is the differential pressure generated by flow through the orifice; βis the ratio of Orifice diameter to pipe diameter, T is the air temperature.

Q is the control output, H is the output of differential pressure sensor, P is the output of pressure sensor, and temperature sensor output T acts as the output of observation. This mathematical model will be the control process of the fusion control system and achieve automation control of natural gas flow [3].

III. SYSTEM DESIGN

A. hardware design of System This system uses processor Samsung S3C2410 with

ARM920T kernel as the center of the system, whose block diagram shows as Fig 2:

The system consists of such modules as S3C2410,

sensor information collection, level converter circuit, oscillator circuit, external expansion SDRAM, D/A converter. S3C2410 is the core module of the system, whose main function is the control and coordination the normal work of the various peripheral modules, as well as the implementation of temperature, pressure, differential pressure sensor fusion algorithm. Sensor voltage signals collected from three sensors are often very small and can not meet the requirement I / O interface level range for S3C2410. So a level translator is needed. The sensor output voltage is converted into electricity to meet the requirements and enter into the S3C2410 level range of I / O Ports. And it is no need of external expansion A/D converter because there is an inner 8-channel 10-bit A/D converter in S3C2410 chip. After receiving the digital temperature, pressure, differential signal, S3C2410 perform secondary fusion processing using the corresponding fusion algorithm, and output a control signal to D/A converter according to the fusion information and control decision. Then the analog signal after D/A converter is sent to the electric valve on the gas pipeline to control the opening of the valve. So the automation control of natural gas flow has achieved [4].

B. Software design of system The algorithm design of fusion for temperature, pressure, differential pressure sensor information and the control decision of the system are the cores contents of the software design. The sensors information will pass two degrade fusion processing to reflect the gas flue states accurately and completely.

1) ART2 network clustering ART2 network is an excellent data clustering process method, its network structure shows as Fig.3

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TTTTTX ],,[ 2019211 K= ； TPPPPX ],,[ 2019212 K= ；

THHHHX ],,[ 2019213 K= .Three ART2 networks are used in the system to process the first grade clustering fusion to temperature, pressure, differential pressure separately. According to gas flow rate change information，the length

of sampling window of this model is 60s，the interval time of sensors information collection is 3s, so there are 20 data in one model window, the model characteristics of temperature, pressure, differential pressure are:

TTTTTX ],,[ 2019211 K= ； TPPPPX ],,[ 2019212 K= ；

THHHHX ],,[ 2019213 K= ⎩⎨⎧

>≤≤

=0,

0,0)(

xxx

xfθ

Because the dimension of each X1, X2, X3 is 20, that is N=20, the number of the output port of ART2 network is 20. Other parameters can be set by experiments as: Contrast constant a=b=10, adjustment subsystem Constant c=0.2，the output layer F2 field gain d =1, filtering threshold 5.05.0 20/1/1 == Nθ , filtering transfer function:

⎩⎨⎧

>≤≤

=0,

0,0)(

xxx

xfθ (2)

],,,,,,,,[ 987654321 CCCCCCCCCC = Input X1,X2,X3 pattern vectors to ART2 network and

use HCM clustering algorithm based on objective function to classify information from each sensor into 8 classical categories and encode them as: nearly unchanged(100), decreased(001), decreased after increased(010).decreased rapidly(011),alarm(100),increase after decreased(101), increased(110), increased rapidly(111).The fusion space C of temperature, pressure, differential pressure sensors can be obtained after fusion processing:

],,,,,,,,[ 987654321 CCCCCCCCCC = (3) Where C1, C2, C3 is temperature sensors information

coding, C4, C5, C6 is pressure sensors information coding, C7, C8, C9 is differential pressure sensors information coding [5].

2) BP network fusion This system uses BP network for a secondary fusion for

sensors information space C which has already been fused.

Its structure diagram shows as Fig.4

As it shown, the BP network input layer has 9 neurons

，which corresponds to the nine variables of vector C respectively (from C1 to C9) and the output includes the range of gas flow (from B1 to B4) and sensors states. Gas flow rate in the range 10, their coding is max/100 QQQb = respectively (where maxQ is the maximum flow, bQ is the flow state coding, that is 0000，0001，….,1000,1001). There are four kinds of sensors states (B5,B6) (Temperature anomalies(00), pressure anomalies(01), differential pressure anomalies(10), all normal (11) ).

3) Control decision System exercise incremental control of the gas pipeline on the electric control valve opening, and the output of the fusion control is the adjustment of the electric valve: )1()()( −−=Δ iViViV zzz (4)

Where i is the number of the control pulses. With the combination of gas flow control rules, that is zHTz VCkPPkCkV /])([ 3021 +−−=Δ (5)

Where k1,k2,k3 are the proportional control weighting coefficients, their values are different in different B space,

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CT,CH are trend constants of temperature, differential pressure sensors respectively, P0 is the desired pressure value of system [6].

Control flow of the fusion of natural gas flow as shown in Figure 5

IV. CONCLUSIONS Information collected from Multi-sensor reflects the state of controlled process from different sides respectively, and fusion technology can take advantage of this information effectively to enable them to complete, accurate and reliable reflection of the characteristics of controlled process. S3C2410 with ARM920T kernel has the advantages as high integration, high reliability, high performance computing power etc. Comparing the gas control system based on ARM and multi-sensors information fusion with the traditional control system, we can see obvious higher control quality and exercise a real-time control on gas flow.

REFERENCES [1] Peng Jian-hua. Standard orifice measurement methods of gas flow

and its affect to accurate factors. Metrology & Measurement Technique.[J] 1999. No.2

[2] Lv Xiu-jiang,Zheng Bin, YU Hai-tao, Wang De-yuan. Application of multi-sensor fusion based on ARM in industrial control. Ordnance Industry Automation.[J] Feb.2009 Vol.28,No.2

[3] Duan Xia-xia, Liu Yang-ming, Li Xiao-ping, Yang Yi-zhan. Study of fault cluster based on ART2 progressed algorithm. Computer Engineering and Applications. [J]2009,44(15)

[4] Qian Xiao-dong, Wang Zheng-ou. Research of data clustering based on improved algorithm of ART2. Journal of Harbin Institute of Technology.[J] Sep. 2006 Vol 38 No.2

[5] Preda M D, Christodorescu M.A semanties-based approach to malware detection[C]//POPL’07, Nice, France, 2007.

[6] Brumley D, Newsome J.Alias analysis for assembly Technical Report[R].USA: School of Computer Science, Camegie Mellon University, 2006.

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