Unsupervised Real Time and Early Anomalies Detection Method for Sewer Networks Systems

Qiu, Chunming; Shao, Guoxiang; Zhang, Zhenyu; Zhou, Chichun; Hou, Yuejie; Zhao, Enming; Guo, Xiao; Guan, Xiaolin

doi:10.1109/access.2024.3359302

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2024

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“…For sewer systems, some methods are based on data analysis [32]. Furthermore, there are methods using closed-circuit television (CCTV) inspections and artificial intelligence to classify defects automatically [33].…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Model Predictive Control Applied to a Sewer Network

Cembellín,

Fuente,

Vega

et al. 2024

Applied Sciences

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This paper presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The aim of this work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. For this purpose, a fault detection and diagnosis system (FDD) based on a moving window principal component analysis technique (MWPCA) will be developed to provide an online fault monitoring solution for large-scale complex processes (e.g., sewer systems) with dynamically changing characteristics, and a reconfiguration algorithm for the MPC controller taking advantage of its own features such as constraint handling. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

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Section: Introductionmentioning

confidence: 99%