A Soft Fault Detection Mechanism with High Accuracy on Machine-to-Machine Communication Networks

Shih, Yuan-Kang; Wei, Hung‐Yu

doi:10.1109/ithings.2014.63

Cited by 4 publications

(1 citation statement)

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“…In [4] the application of data mining in fault detection of optical fiber communication is proposed. And [5] gives a mechanism with capability to detect and distinguish soft fault devices. Paper [6] addresses the major problem of link failure in the WSN by proposing a new routing algorithm with local detection and recovery for the link failure that provides better solution for fault detection and fault management along the QoS paths.…”

Section: Introductionmentioning

confidence: 99%

POWERLINK communication fault detection method based on data frames sequence analysis

Wang

Xie

Dong

2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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Fault diagnosis, location and maintenance of industrial networks are extremely important components of network management, which can effectively improve network availability, shorten network failure time, help improve network performance, satisfy network service quality and ensure network security. This paper proposes a communication fault detection method based on the analysis of communication protocol message flow for POWERLINK, an industrial Ethernet protocol widely used in the field of industrial communication at present. By monitoring network status and building fault judgment logic, it can analyze the rationality of message sequence and protocol consistency in the message flow so as to effectively detect the dynamic upper layer protocol fault in the network and determine the location of the fault. In order to verify the effectiveness of this method, we designed a POWERLINK network communication fault diagnosis system, which can correctly identify and analyze the wrong data frame sequence in the data message flow and finally achieve the diagnosis and location of POWERLINK network communication fault.

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Section: Introductionmentioning

confidence: 99%