A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning
Date
2021-09Publisher
Brac UniversityAuthor
Morshed, AtiqueSiraj, Farhan Md.
Munim, Abu Sadat MD.
Ayon, Tasnimul Karim
Goni, Saad Rafsan
Metadata
Show full item recordAbstract
Rapid industrial growth has increased the vulnerability of systems to malfunction
and permanent damage. Fault detection systems have been installed to prevent such
occurrences. In order to eliminate potential life-threatening dangers or unforeseen
obstacles that may jeopardize the manufacturing process, early fault detection has
become an essential aspect of modern industry. Because artificial intelligence has
become increasingly successful across numerous different domains, many researchers
have employed deep learning models to classify faults and are always trying to find
faster, more accurate ones. In this paper, we present a deep transfer learning architecture
that consists of long short-term memory (LSTM) layers of Recurrent Neural
Network to extract features enhanced by gammatone like spectrogram. For the
dataset, we have used malfunctioning industrial machine investigation and inspection
(MIMII) and ToyADMOS datasets. Our experimented results show that the
proposed model detect the different faults with precision. Also, our modified gammatone
fast fourier method outperforms traditional gammatone accurate method
with consistent performance across all environments.