Real-Time Fault-Detection for IIoT Facilities using GBRBM-based DNN

Fault-detection is a fundamental requirement for industrial Internet-of-Things (IIoT), such as the process industry sammaknejad2019review. This paper first reviews recent studies focusing on applying fault-detection techniques to IIoT networks. However, we find out that numerous studies focus on resource utilization and workload allocation. The fault-detection towards IIoT facilities is still in its immature stage, because the existing approaches are not accurate enough for the stringent fault-detection in IIoT networks. To this end, we present a novel algorithm, named Gaussian Bernoulli restricted Boltzmann machines (GBRBM) based Deep Neural Network (DNN), to transform the fault-detection into a classification problem. Real trace-driven experiments show that the proposed scheme outperforms other baseline machine learning methods. We anticipate that this article can inspire blooming studies on the related topics of smart IIoT networks.