Maintenance of industrial reactors supported by deep learning driven ultrasound tomography
Ładowanie...
Data
2020
Inny tytuł
Typ
Artykuł recenzyjny
Redaktor
dc.contributor.advisor
Dyscyplina PBN
Informatyka techniczna i telekomunikacja
Czasopismo lub seria
Eksploatacja i Niezawodność – Maintenance and Reliability
ISSN
1507-2711
2956-3860
2956-3860
ISBN
DOI
10.17531/ein.2020.1.16
Strona internetowa
Wydawca
Wydawca
Wydanie
Numer
Strony od-do
Tytuł monografii
item.page.defence
Tytuł tomu
Opis
Rodzaj licencji
Abstrakt (en)
Monitoring of industrial processes is an important element ensuring the proper maintenance of equipment and high level of processes reliability. The presented research concerns the application of the deep learning method in the field of ultrasound tomography (UST). A novel algorithm that uses simultaneously multiple classification convolutional neural networks (CNNs) to generate
monochrome 2D images was developed. In order to meet a compromise between the number of the networks and the number of
all possible outcomes of a single network, it was proposed to divide the output image into 4-pixel clusters. Therefore, the number
of required CNNs has been reduced fourfold and there are 16 distinct outcomes from single network. The new algorithm was first
verified using simulation data and then tested on real data. The accuracy of image reconstruction exceeded 95%. The results
obtained by using the new CNN clustered algorithm were compared with five popular machine learning algorithms: shallow
Artificial Neural Network, Linear Support Vector Machine, Classification Tree, Medium k-Nearest Neighbor classification and
Naive Bayes. Based on this comparison, it was found that the newly developed method of multiple convolutional neural networks
(MCNN) generates the highest quality images.