Uddin, JiaAshraf, Faisal BinMorshed, AtiqueSiraj, Farhan Md.Munim, Abu Sadat MD.Ayon, Tasnimul KarimGoni, Saad Rafsan2021-12-262021-12-2620212021-09ID 21141067ID 17101155ID 21341046ID 21141075ID 17301003http://hdl.handle.net/10361/15759Cataloged from PDF version of thesis.Includes bibliographical references (pages 29-30).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.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.30 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Deep learningTransfer learningFault detectionRNNLSTMGammatone Filter BankArtificial intelligenceCognitive learning theoryThesis