Machine Fault Detection for Intelligent Self-Driving Networks

Huakun Huang, Lingjun Zhao, Huawei Huang, Song Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)


To build the mechanism of a SelfDN is becoming an emerging direction for future IoT. The detection of device status, such as fault or normal, is a very fundamental module in SelfDN. In this article, we first review recent studies devoted to applying fault detection techniques in IoT networks. Taking the challenge of processing the real-valued IoT data into account, we propose a novel fault detection architecture for SelfDN. Under this architecture, we present an algorithm, named GBRBM-based deep neural network with auto-encoder (i.e., GBRBM-DAE) to transform the fault detection problem into a classification problem. The real-world trace-driven experimental results show that the proposed algorithm outperforms other popular machine learning algorithms, including linear discriminant analysis, support vector machine, pure deep neural network, and so on. Finally, we summarize some open issues of this study. We expect that this article will inspire successive studies on the related topics of SelfDN.

Original languageEnglish
Article number8970164
Pages (from-to)40-46
Number of pages7
JournalIEEE Communications Magazine
Issue number1
Publication statusPublished - Jan 2020

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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