DEVELOPMENT OF AN ARTIFICIAL NEURAL NETWORK FOR MONITORING AND DIAGNOSIS OF SENSOR FAULT AND DETECTION IN THE IEA-R1 RESEARCH REACTOR AT IPEN / "Utilização de redes neurais artificiais na monitoração e detecção de falhas em sensores do Reator IEA-R1"

AUTOR(ES)

Elaine Inacio Bueno

DATA DE PUBLICAÇÃO

2006

RESUMO

The increasing demand on quality in production processes has encouraged the development of several studies on Monitoring and Diagnosis Systems in industrial plant, where the interruption of the production due to some unexpected change can bring risk to the operator’s security besides provoking economic losses, increasing the costs to repair some damaged equipment. Because of these two points, the economic losses and the operator’s security, it becomes necessary to implement Monitoring and Diagnosis Systems. In this work, a Monitoring and Diagnosis Systems was developed based on the Artificial Neural Networks methodology. This methodology was applied to the IEA-R1 research reactor at IPEN. The development of this system was divided in three stages: the first was dedicated to monitoring, the second to the detection and the third to diagnosis of failures. In the first stage, several Artificial Neural Networks were trained to monitor the temperature variables, nuclear power and dose rate. Two databases were used: one with data generated by a theoretical model and another one with data to a typical week of operation of the IEA-R1 reactor. In the second stage, the neural networks used to monitor the variables was tested with a fault database. The faults were inserted artificially in the sensors signals. As the value of the maximum calibration error for special thermocouples is , it had been inserted faults of in the sensors for the reading of the variables T3 and T4. In the third stage a Fuzzy System was developed to carry out the faults diagnosis, where were considered three conditions: a normal condition, a fault of , and a fault of . This system will indicate which thermocouple is faulty. Cº5,0±Cº1Cº1±−Cº1+

ASSUNTO(S)

monitoring and diagnosis monitoracao e diagnostico neural network sensor sensores redes neurais

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