The implementation of the self-calibration, self-compensation and self-validation algorithms for foundation fieldbus sensors are presented using standard function blocks

Cagni, Eloi; Pereira, Danillo Roberto; Neto, Alexandre; Melo, Jorge Dantas de; Affonso, L.; Oliveira, H.

doi:10.1109/cimsa.2005.1522865

Cited by 5 publications

(9 citation statements)

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“…With another neural network function block, configuration of the agents can compensate an error, as shown in [7]. In his study the implementation of selfcalibration, self-compensation and self-validation algorithms for Foundation Fieldbus sensors are presented using standard function blocks.…”

Section: Figure 2 Function Blocks Organization As a Neural Networkmentioning

confidence: 99%

“…In the event of a tendency for loss of accuracy in the measured values, which is detected by the observation agents, the EA can act as a prediction system, anticipating presumed supervisory faults which may occur. Or the EA can act as a recalibration algorithm in detecting a decalibrated sensor [7].…”

Section: Execution Agentsmentioning

confidence: 99%

“…Combining these previously described technologies (multiagent systems, machine learning and industrial networks) is proposed solutions to avoid problems in automation systems and industrial networks. Many smart control strategies have been developed by allocating and connecting function blocks [7]. This approach is called Software Sensor.…”

Section: Introductionmentioning

confidence: 99%

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A neural network multiagent architecture applied to fieldbus intelligent control

Machado

Neto

Melo

et al. 2008

2008 IEEE International Conference on Emerging Technologies and Factory Automation

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This paper presents a multiagent architecture applied to factory automation. These agents detect faults in process automation and allocate intelligent algorithms in field device function blocks to solve these faults. It is also present a dynamic function block parameter exchange strategy which allows agent fieldbus allocation. The objective is to enable problem detection activities independent of user intervention. The use of Artificial Neural Network (ANN)-based algorithms enables the agents to learn about fault problem patterns and adapt an algorithm that can be used in fault situations. With this, the intention is reduce supervisor intervention in selecting and implementing an appropriate structure of function block algorithms. These algorithms, when implemented in device function blocks, provide a solution at fieldbus level, reducing data traffic between gateway and device.

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Section: Figure 2 Function Blocks Organization As a Neural Networkmentioning

confidence: 99%

Section: Execution Agentsmentioning

confidence: 99%

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A neural network multiagent architecture applied to fieldbus intelligent control

Machado

Neto

Melo

et al. 2008

2008 IEEE International Conference on Emerging Technologies and Factory Automation

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show abstract

“…And by linking of these neurons, neural networks are built. With another neural network function block, configuration of the agents can compensate the error as it is seen in Cagni et al (2005). In his work the implementation of the selfcalibration, self-compensation and self-validation algorithms for Foundation Fieldbus sensors are presented using standard function blocks.…”

Section: Function Block Intelligent Algorithmsmentioning

confidence: 99%

“…In the event of a tendency toward loss of accuracy in the measured values, detected by the observation agents, the EA can act as a prediction system, anticipating the presumed supervisory faults that may occur. The EA can also act as a recalibration algorithm in detecting a decalibrated sensor (Cagni et al, 2005).…”

Section: Factory Automation 380mentioning

confidence: 99%

A Multiagent Architecture Based in aFoundation Fieldbus Network Function Blocks

Machado¹,

Brandão²,

Neto³

et al. 2010

Factory Automation

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Implementing Intelligent Functionality into Embedded System

Costa

Cagni

Couto

et al. 2006

IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics

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In the last years it was observed that there has been a great evolution related to industrial automation as well as the evolution regarding DSP (Digital Signal Processor) and industrial networks. The use of DSP, related to artificial intelligence techniques and neural network, permits gradually the incorporation of intelligence onto terminal control units such as sensors and actuators. This paper presents the development of a intelligent functionality system to process sensor signals through remote parameterization of a neural network in a DSP board, enabling the increase in field device processing capability (intelligence).

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The implementation of the self-calibration, self-compensation and self-validation algorithms for foundation fieldbus sensors are presented using standard function blocks

Cited by 5 publications

References 0 publications

A neural network multiagent architecture applied to fieldbus intelligent control

A neural network multiagent architecture applied to fieldbus intelligent control

A Multiagent Architecture Based in aFoundation Fieldbus Network Function Blocks

Implementing Intelligent Functionality into Embedded System

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