Banca de DEFESA: EMERSON VILAR DE OLIVEIRA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : EMERSON VILAR DE OLIVEIRA
DATE: 28/08/2020
TIME: 09:00
LOCAL: videoconferência
TITLE:

Performance Evaluation of LSTM Network-Based Method for Failt Classification in a Level Control Process

KEY WORDS:

Fault classification, neural networks, recurrent neural network, long short-term memory, pilot plant.

PAGES: 5
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUBÁREA: Eletrônica Industrial, Sistemas e Controles Eletrônicos
SPECIALTY: Automação Eletrônica de Processos Elétricos e Industriais
SUMMARY:

Due to the increasing demands in the operation monitoring of industrial plants, methodologies for fault detect and diagnose in the operation of these processes are gaining more and more importance, because they can contribute to more assertive and even predictive repairs in the components that generated such disturbances to the proper functioning of the system. With the growth of data-oriented approaches, Artificial Neural Networks have become considerable allies in solving these problems, and Recurrent Neural Networks, in particular, have gained strength due to their affinity in dealing with series that have temporal links between their samples, which is the case of industrial process variables monitoring. Due to this relevance, this dissertation analyzes the performance of Long Short-Term Memory (LSTM) recursive neural network for the detection and classification of faults in a pilot-scaled level control process. For the performance evaluation, a methodology based on Monte Carlo statistical tests was used, in which the influence of the LSTM network hyperparameters, such as number of layers and size of the input and regressors, was analyzed. The accuracy was the metric chosen to quantify the fault classification performance. The data set obtained from the operation of the pilot plant contained 23 situations of disturbances in this process, which resulted from disturbances applied to components such as sensor, valves and the water tank itself. The adopted methodology proved to be quite efficient to analyze both the performance and the robustness of these neural networks for the fault classification activity, in addition to indicating the best network architecture configurations.

BANKING MEMBERS:
Presidente - 1153006 - LUIZ AFFONSO HENDERSON GUEDES DE OLIVEIRA
Interno - 1345674 - LUIZ MARCOS GARCIA GONCALVES
Externo à Instituição - CLAUBER GOMES BEZERRA - IFRN