Name
Abstract | ||
|---|---|---|
Fault detection is a fundamental requirement for Industrial Internet of Things (IIoT), such as the process industry. This article first reviews the recent studies focusing on applying the fault detection techniques to the IIoT networks. However, we find that numerous studies focus on the resource utilization and workload allocation. The fault detection toward 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 (GBRBMs)-based deep neural network (DNN), to transform the fault detection into a classification problem. The 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JIOT.2019.2948396 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Feature extraction,Neural networks,Real-time systems,Production facilities,Internet of Things,Industries,Support vector machines | Journal | 7 |
Issue | ISSN | Citations |
7 | 2327-4662 | 1 |
PageRank | References | Authors |
0.35 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huakun Huang | 1 | 8 | 1.80 |
| Shuxue Ding | 2 | 235 | 33.84 |
| Lingjun Zhao | 3 | 176 | 22.10 |
| Huawei Huang | 4 | 223 | 28.55 |
| Liang Chen | 5 | 258 | 28.02 |
| Honghao Gao | 6 | 217 | 45.24 |
| Syed Hassan Ahmed | 7 | 505 | 65.70 |