Deep Learning Based Intelligent Industrial Fault Diagnosis Model | Zendy

R Surendran | Zendy; Osamah Ibrahim Khalaf | Zendy; Carlos Andrés Tavera Romero | Zendy

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Deep Learning Based Intelligent Industrial Fault Diagnosis Model

Author(s) -

R Surendran,

Osamah Ibrahim Khalaf,

Carlos Andrés Tavera Romero

Publication year - 2021

Publication title -

computers, materials and continua/computers, materials and continua (print)

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.788

H-Index - 40

eISSN - 1546-2226

pISSN - 1546-2218

DOI - 10.32604/cmc.2022.021716

Subject(s) - computer science , feature extraction , artificial intelligence , perceptron , residual , fault (geology) , bearing (navigation) , pattern recognition (psychology) , fault detection and isolation , condition monitoring , process (computing) , feature (linguistics) , representation (politics) , signal (programming language) , multilayer perceptron , data mining , artificial neural network , engineering , algorithm , linguistics , philosophy , seismology , politics , law , political science , electrical engineering , actuator , geology , operating system , programming language

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