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 diagno-sis (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.