August 16, 2025

Remote Real-Time Gas Data Monitoring with Predictive Maintenance of Turbine Flow Meters

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The study found that SARIMA outperformed regression techniques with a 2.14% reduction in RMSE, improving predictions for turbine flow meters.
Random Forest Regressor was the best among regression models for analyzing operational data from turbine meters, ensuring efficient monitoring.
Data collection was achieved through a real-time system designed with a Raspberry Pi 4 and various components for gas flow parameters.
These findings highlight the potential of predictive maintenance systems to minimize disruptions and enhance reliability in gas distribution.

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Observational analysis improved predictive maintenance of turbine flow meters in gas distribution, indicating enhanced operational reliability.

Quadri et al. (2025) studied this question.

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Observational analysis improved predictive maintenance of turbine flow meters in gas distribution, indicating enhanced operational reliability.