Work

Data-Driven Fault Diagnosis in Water Pipelines Based on Neuro-Fuzzy Zonotopic Kalman Filters

Year: 2024

Type: article

Abstract: This work presents a data-driven approach for diagnosing sensor faults and leaks in hydraulic pipelines using neuro-fuzzy Zonotopic Kalman Filters (ZKF). The approach involves two key steps: first, identifying the nonlinear pipeline system using an adaptive neuro-fuzzy inference system (ANFIS), resulting in a set of Takagi–Sugeno fuzzy models derived from pressure and flow data, and second, implementing a neuro-fuzzy ZKF bench to detect pipeline leaks and sensor faults with adaptive thresholds. The learning phase of the neuro-fuzzy systems considers only fault-free data. Fault isolation is achieved by comparing zonotopic sets and evaluating a fault signature matrix. The method accounts for parametric uncertainty and measurement noise, ensuring robustness. Experimental validation on a hydraulic pipeline demonstrated high precision (up to 99.24%), recall (up to 99.20%), and low false positive rates (as low as 0.76%) across various fault scenarios and operational points.

Source: Mathematical and Computational Applications

DOI: 10.3390/mca30010002

Authors: Esvan-Jesús Pérez-Pérez, Yair González-Baldizón, José-Armando Fragoso-Mandujano, Julio-Alberto Guzmán-Rabasa, Ildeberto Santos-Ruiz

Institutions: Universitat Politècnica de Catalunya, Tuxtla Gutierrez Institute of Technology, Tecnológico Nacional de México, Universidad Autónoma de Chiapas

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Clean water and sanitation

Open Access status: gold

Estimated APC paid: $1,591