Industrial Fault Diagnosis using Hilbert Transform and Texture Features

Mahe Zabin, Ho-Jin Choi, Jia Uddin, Md Hasan Furhad, Abu Barkat ullah

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

2 Citations (Scopus)


An automated fault detection is a vital issue in smart industries of Industry 4.0. This paper presents a model of industrial fault diagnosis using deep learning algorithms. In the proposed model, a standard induction motor dataset that consists of six different types of fault is used as an input. Then as a preprocessing method we utilized Hilbert transform to extract the pre-processed signals with absolute values. After that, texture images are generated from the pre-processed signals. The texture pattern of the images is used for training and testing the deep convolutional neural networks. For analyzing the performance of the proposed system, we used the F1-score which is derived from precision and recall. Experimental results demonstrate that the proposed model exhibited average 98.48% F1 score for the dataset, where HC (98.33%), IRF (98.57%), BF (98.41%), and ORF (98.56%), respectively. In addition, the proposed model shows comparatively higher classification accuracy compared to the four state-of-art models by showing the higher F1 score.
Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021
EditorsHerwig Unger, Jinho Kim, U Kang, Chakchai So-In, Junping Du, Walid Saad, Young-guk Ha, Christian Wagner, Julien Bourgeois, Chanboon Sathitwiriyawong, Hyuk-Yoon Kwon, Carson Leung
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728189246
ISBN (Print)9781728189253
Publication statusPublished - 10 Mar 2021
EventInternational Conference on Big Data and Smart Computing (BIGCOMP) -
Duration: 1 Jan 2011 → …


ConferenceInternational Conference on Big Data and Smart Computing (BIGCOMP)
Abbreviated titleBIGCOMP
Period1/01/11 → …


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