Open Access

ARTICLE

Improved Symbiotic Organism Search with Deep Learning for Industrial Fault Diagnosis

Mrim M. Alnfiai^*

Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, Taif, 21944, Saudi Arabia

* Corresponding Author: Mrim M. Alnfiai. Email:

Computers, Materials & Continua 2023, 74(2), 3763-3780. https://doi.org/10.32604/cmc.2023.033448

Received 16 June 2022; Accepted 04 August 2022; Issue published 31 October 2022

Abstract

Developments in data storage and sensor technologies have allowed the cumulation of a large volume of data from industrial systems. Both structural and non-structural data of industrial systems are collected, which covers data formats of time-series, text, images, sound, etc. Several researchers discussed above were mostly qualitative, and ceratin techniques need expert guidance to conclude on the condition of gearboxes. But, in this study, an improved symbiotic organism search with deep learning enabled fault diagnosis (ISOSDL-FD) model for gearbox fault detection in industrial systems. The proposed ISOSDL-FD technique majorly concentrates on the identification and classification of faults in the gearbox data. In addition, a Fast kurtogram based time-frequency analysis can be used for revealing the energy present in the machinery signals in the time-frequency representation. Moreover, the deep bidirectional recurrent neural network (DBiRNN) is applied for fault detection and classification. At last, the ISOS approach was derived for optimal hyperparameter tuning of the DL method so that the classification performance will be improvised. To illustrate the improvised performance of the ISOSDL-FD algorithm, a comprehensive experimental analysis can be performed. The experimental results stated the betterment of the ISOSDL-FD algorithm over current techniques.

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Keywords

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