Design of PROFINET I/O Real-time Communication System between PLC Based on S7-1200

Chen, Yun-Hai; Zhang, Dong-Ying; Ji, Jun; Chen, Xin

doi:10.1088/1742-6596/2569/1/012013

Cited by 1 publication

(1 citation statement)

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“…As Figure 4 shows, sensors are used to sample the information of the devices and instruments such as thermometers, flow meters, water level meters, pH concentration meters, frequency converters, and so on. The remote I/O unit collects sensor data and provides the sensor data to gateways [35] based on the different industrial control protocols [36][37][38]. The IIoT gateway is responsible for sending sensor data to the cloud server via the MQTT format.…”

Section: Iiot Architecture Of Sewage Treatment Plantmentioning

confidence: 99%

Research on Fault Detection by Flow Sequence for Industrial Internet of Things in Sewage Treatment Plant Case

Lei,

Zhao,

Chen

2024

Sensors

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Classifying the flow subsequences of sensor networks is an effective way for fault detection in the Industrial Internet of Things (IIoT). Traditional fault detection algorithms identify exceptions by a single abnormal dataset and do not pay attention to the factors such as electromagnetic interference, network delay, sensor sample delay, and so on. This paper focuses on fault detection by continuous abnormal points. We proposed a fault detection algorithm within the module of sequence state generated by unsupervised learning (SSGBUL) and the module of integrated encoding sequence classification (IESC). Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the different network cards in the IIoT gateway, and then combined the multiple code sequences into one integrated sequence. Next, we classified the integrated sequence by comparing the integrated sequence with the encoding fault type. The results obtained from the three IIoT datasets of a sewage treatment plant show that the accuracy of the SSGBUL–IESC algorithm exceeds 90% with subsequence length 10, which is significantly higher than the accuracies of the dynamic time warping (DTW) algorithm and the time series forest (TSF) algorithm. The proposed algorithm reaches the classification requirements for fault detection for the IIoT.

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Section: Iiot Architecture Of Sewage Treatment Plantmentioning

confidence: 99%