| Heydar Suleyman Rzayev, Arzu Agababa Akhundov, Natavan Mammad Khasayeva, Mehriban Bakhtiyar Mammadova Intelligent Information-Measuring System for Scanning Liquid Flows |
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| Abstract. This study presents the development and validation of an Automated Liquid Flow Scanning and Intelligent Measurement System (ALFSS) designed to provide high-accuracy, adaptive, and real-time monitoring of liquid flow processes in complex industrial and scientific environments. Traditional liquid flow measurement systems, including mechanical flow meters, analog sensors, and conventional digital measurement platforms, often suffer from limitations such as low adaptability, high sensitivity to noise, calibration drift, and reduced reliability in nonlinear and dynamically changing flow conditions. These limitations significantly affect the stability, accuracy, and robustness of measurement results, particularly in large-scale industrial infrastructures and safety-critical systems. To address these challenges, the proposed system integrates multisensor measurement architectures, adaptive filtering techniques, intelligent data processing and real-time deci-sion-making algorithms into a unified intelligent measurement platform. The system archi-tecture is based on a layered structure consisting of a sensor layer, signal conditioning layer, data acquisition layer, processing unit, intelligent analysis layer, decision system, and visu-alization and control interface. This modular design ensures scalability, flexibility, and compatibility with different industrial and laboratory environments. A comprehensive mathematical modeling framework is developed, including fluid flow dynamics models, state-space representations, measurement equations, and integrated error models. The system incorporates stochastic modeling approaches to represent uncertainties, sensor noise, calibration errors, and environmental disturbances. Adaptive estimation is achieved through Kalman filtering and extended data fusion techniques, enabling optimal state estimation under noisy and uncertain conditions. Multisensor data fusion strategies are implemented to enhance measurement reliability, improve accuracy, and provide redundan-cy against sensor failures. An intelligent algorithmic framework is proposed, including automated flow scanning, adaptive filtering, sensor fusion, fault detection, anomaly analysis, and intelligent decision generation. The system is capable of detecting abnormal flow behaviors, identifying system faults, and generating corrective control actions in real time. Simulation models are devel-oped using MATLAB/Simulink environments to validate system performance, stability, and robustness. Performance evaluation is carried out using quantitative metrics such as root mean square error (RMSE), accuracy indices, response time, and stability criteria based on Lyapunov theory. The obtained results demonstrate that the proposed ALFSS significantly outperforms classical measurement systems in terms of accuracy, adaptability, robustness, and reliability. The system shows strong resistance to noise, high sensitivity to dynamic flow variations, and stable performance under nonlinear operating conditions. The developed framework is suitable for applications in water supply systems, oil and gas pipelines, chemical process industries, energy systems, hydraulic infrastructures, laboratory measurement platforms, and aerospace fluid systems. The proposed smart measurement system provides a scalable, adaptive, and future-oriented solution for next-generation automated fluid flow monitoring and control technolo-gies, providing higher accuracy, stability, and adaptability compared to classical measure-ment methods [1–5]. |
| Keywords: Intelligent, measurement system, fluid flow, scanning, multisensor, test measurement, Kalman filter, adaptive control |
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| DOI: https://doi.org/10.54381/itta2026.1.10 |