Key result
Neural network predictive maintenance detects early warning signs of flow instrumentation malfunction with ~91% accuracy.
Why the study?
Flow instruments are susceptible to sensor drift, clogging, and degradation, but traditional maintenance methods relying on periodic calibrations or reactive repairs often fail to prevent unexpected equipment failures.
Effect estimate: Loss 0.17
A neural network machine learning model can effectively predict flow instrumentation malfunctions with 91.4% accuracy, potentially reducing maintenance costs and improving process reliability.
May support proactive maintenance of critical instrumentation; leaves open prospective clinical validation before adoption.
Instrumentation is critical for maintaining the accuracy and stability of industrial processes, particularly in measuring and controlling key variables such as flow, pressure, and temperature. However, over time, flow instruments are susceptible to issues like sensor drift, clogging, and degradation, leading to inaccurate readings and operational inefficiencies. Traditional maintenance methods, relying on periodic calibrations or reactive repairs, often fail to prevent unexpected equipment failures, resulting in costly downtime and process disruptions. This paper presents the development of a neural network machine learning-based predictive maintenance system designed to address these challenges by shifting from reactive to proactive maintenance. Historical data, including transmitter readings, system outputs, and maintenance logs, are utilized to train a machine learning model capable of identifying early warning signs of equipment malfunction, such as gradual sensor drift and anomalous flow patterns. The system analyzes data trends and delivers real-time alerts to maintenance teams, enabling timely interventions. Simulation results demonstrate the model's effectiveness, achieving a high accuracy of 91.4% and a low loss of 0.17, ensuring improved process reliability and reduced maintenance costs. This study highlights the potential of the neural network model in enhancing instrumentation reliability and operational efficiency in industrial environments.
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Benotsmane et al. (2025) studied Flow instrumentation maintenance in DCS and SCADA systems. Neural network machine learning-based predictive maintenance system vs. Traditional maintenance methods was evaluated on Model accuracy and loss in identifying early warning signs of equipment malfunction (Loss 0.17). A neural network machine learning-based predictive maintenance system achieved an accuracy of 91.4% and a loss of 0.17 in identifying early warning signs of flow instrumentation malfunction.
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