Integrated Diagnostic Model for Leakage and Blockage in Water Distribution Networks based on Multisensor Data Fusion

Jinjiang Zhao; Yinfeng Wang; Lei Zhang

doi:10.6919/ICJE.202608_12(8).0006

Authors

Jinjiang Zhao Institute of Automation, Gansu Academy of Sciences, Lanzhou, Gansu 730000, China; College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China
Yinfeng Wang Shaanxi Provincial Water and Drought Disaster Prevention Center, Xi’an, Shaanxi 710004, China
Lei Zhang Institute of Automation, Gansu Academy of Sciences, Lanzhou, Gansu 730000, China; College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China

DOI:

https://doi.org/10.6919/ICJE.202608_12(8).0006

Keywords:

Robust Control; Sensor Bias; Multisensor Fusion; Digital Twin; Fault Diagnosis; Engineering Stability.

Abstract

Together, the diagnosis of leaks and blockages in the water distribution network has been affected by coupled perception, hydraulics, computation and operation uncertainty, so a single deterministic controller or classifier cannot be applied reliably in engineering practice. According to various sensors, a powerful diagnosis and control framework has been proposed in this paper that is biased-aware, employs model predictive reasoning and conservative fallback rules. Instead of field claims for the parameters in this study, Disequations will be used; all indicators are Design Variables and will undergo several engineering evaluations. The five tables are sensor channel information, disturbance cases, diagnostic thresholds, control responses and robustness indices, and the two formulas are the residual score and the weighted stability index. Based on the above results, the proposed framework can reduce false alarms, keep the stability of operation, and provide clear decision support in the case of bias, delay or partial missing data in the input signal. The new one is to integrate the fault evidence, model constraints and practical control measures in a traceable way that can be published in the journal.

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References

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