Desenvolvimento de uma metodologia utilizando rede neural artificial na detecção e diagnóstico de falhas para válvula de controle pneumática
Competition and regulations in the industrial sector determine the productivity and safety of industrial plant control systems, thus satisfying the market. When a failure occurs, the functioning of the system can be compromised. Therefore, FDD (Fault Detection and Diagnostics) methods contribute t...
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| Formato: | doctoralThesis |
| Idioma: | pt_BR |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/47038 |
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| Resumo: | Competition and regulations in the industrial sector determine the productivity and safety of industrial plant control systems, thus satisfying the market. When a failure occurs,
the functioning of the system can be compromised. Therefore, FDD (Fault Detection and
Diagnostics) methods contribute to avoid unwanted events, as there are techniques and
methods that study the detection, isolation, identification and, consequently, the diagnosis
of faults. In this work, a new methodology was developed that uses faults emulation to
obtain parameters similar to the benchmark model DAMADICS (Development and Application of Methods for Actuators Diagnosis in Industrial Control Systems), with the
main purpose of detecting and diagnosing emulated faults. This methodology uses previous information from tests on sensors with and without faults to detect and classify
the situation of the plant and, in the presence of faults, perform the diagnosis through
a process of elimination in a hierarchical manner. In this way, the definition of residue
signature is used as well as the creation of a decision tree. The whole process is carried
out incorporating FDD techniques, through the ANN (Artificial Neural Network) model
NARX (Nonlinear Autoregressive with Exogenous Inputs), in the diagnosis of the behavioral prediction of the signals to generate the residual values. Then, it is applied to the
construction of the decision tree based on the most significant residue of a certain signal,
enabling the process of acquisition and formation of the signature matrix. With the procedures in this study, it is possible to demonstrate a practical and systematic method of
how to emulate faults for control valves and the possibility of carrying out an analysis of
the data to acquire signatures of the fault behavior. Finally, simulations resulting from
the most sensitized variables for the production of residuals that is generated by neural
networks are presented, which are used to obtain signatures and isolate the flaws. The
process proves to be efficient in computational time and easy to present a fault diagnosis
strategy that can be reproduced in other processes. |
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