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Tesi etd-11192019-202923


Thesis type
Tesi di laurea magistrale
Author
ROCCHI, RICCARDO
URN
etd-11192019-202923
Title
ANOMALY DETECTION BASED ON DEEP AUTOENCODER FOR MANUFACTURING PROCESSES
Struttura
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
COMPUTER ENGINEERING
Supervisors
relatore Prof. Cimino, Mario Giovanni Cosimo Antonio
relatore Prof.ssa Vaglini, Gigliola
relatore Ing. Alfeo, Antonio Luca
Parole chiave
  • tensorflow
  • smart
  • pca
  • discrimination
  • manufacturing
  • neural
  • network
  • autoencoders
  • data analysis
  • score
  • python
  • detection
  • anomaly
  • pearson
  • correlation
Data inizio appello
09/12/2019;
Consultabilità
Parziale
Data di rilascio
09/12/2089
Riassunto analitico
I have developed a project called AnomalyDetector, a software written in Python using Tensorflow 2.0 for the detection of anomalies in industrial machinery using deep learning techniques.
Anomalies are very rare, so it is necessary for "AnomalyDetector" to be able to recognize them using historical data relating to a few anomalous instances.
The high-level architecture is composed of 4 main components: 3 of these implement the functions required by the 3 phases of analysis (features extraction, anomaly score calculation and anomaly discriminator) while a fourth component orchestrates the first 3, for each case study.
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