Banca de DEFESA: PAULO VICTOR QUEIROZ CORREIA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : PAULO VICTOR QUEIROZ CORREIA
DATE: 30/06/2021
TIME: 09:00
LOCAL: Videoconferência: meet.google.com/jiv-nwam-zcx
TITLE:

DETECTION AND CLASSIFICATION PERFORMANCE ANALYSIS OF FAILURES IN INDUSTRIAL PROCESSES USING LSTM NEURAL NETWORKS WITH DATA COMPRESSION TECHNIQUES

KEY WORDS:

Industry 4.0, Fault Detection and Identification, Symbolic Aggregate Approximation, Long Short-Term Memory, Edge Computing, Pruning Compression, Tennessee Eastman Process.

PAGES: 60
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUBÁREA: Eletrônica Industrial, Sistemas e Controles Eletrônicos
SPECIALTY: Controle de Processos Eletrônicos, Retroalimentação
SUMMARY:

Industry 4.0 set a paradigm shift in industrial process monitoring and control. It installed several sensors in different parts of the plant, connecting these industrial processes with the Internet of Things and Cloud Computing. Although, the data generation growth demanded engineers build proper environments to process and store the absurd amount of data. This growth caused an increasing energy consumption, computational complexity and environmental degradation. Therefore to address these demands, this dissertation proposes efficient approaches to perform Fault Detection and Identification in industrial processes. The first approach consists of using Symbolic Aggregate Approximation (SAX) to compress process variables to reduce the load on data warehouses. Then, we train a Long Short-Term Memory (LSTM) neural network with those compressed inputs to perform fault detection. Finally, the second approach addresses efficient edge computing systems, performing LSTM neural network compression with pruning technique. The compression reduces the memory usage and number of operations of these networks, saving energy and accelerating inference speed in edge computation. To assess the performance of both approaches, we use the Tennessee Eastman Process (TEP) as the benchmark with classification metrics of accuracy, precision, recall and F1-Score. We are also going to analyze the compression efficiency of both approaches, studying their viability and parameter reduction in LSTM networks.

BANKING MEMBERS:
Presidente - 1153006 - LUIZ AFFONSO HENDERSON GUEDES DE OLIVEIRA
Interno - 1837240 - MARCELO AUGUSTO COSTA FERNANDES
Externo à Instituição - CELSO JOSÉ MUNARO - UFES