Towards soft real-time fault diagnosis for edge devices in industrial IoT using deep domain adaptation training strategy

Abstract

Artificial intelligence and industrial internet of things (IIoT) have been rejuvenating the fault diagnosis systems in Industry 4.0 for avoiding major financial losses caused by faults in rotating machines. Meanwhile, the diagnostic systems are provided with a number of sensory inputs that introduce variations in input space which causes difficulty for the algorithms in edge devices. This issue is generally dealt with bi-view cross-domain learning approach. We propose a soft real-time fault diagnosis system for edge devices using domain adaptation training strategy. The investigation is carried out using deep learning models that can learn representations irrespective of input dimensions. A comparative analysis is performed on a publicly available dataset to evaluate the efficacy of the proposed approach which achieved accuracy of 88.08%. The experimental results show that our method using long short-term memory network achieves the best results for the bearing fault detection in an IIoT environmental setting.