Tesi etd-11192019-202923 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
ROCCHI, RICCARDO
URN
etd-11192019-202923
Titolo
ANOMALY DETECTION BASED ON DEEP AUTOENCODER FOR MANUFACTURING PROCESSES
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
COMPUTER ENGINEERING
Relatori
relatore Prof. Cimino, Mario Giovanni Cosimo Antonio
relatore Prof.ssa Vaglini, Gigliola
relatore Ing. Alfeo, Antonio Luca
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
Non consultabile
Data di rilascio
09/12/2089
Riassunto
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.
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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