Calibration Method of Viscosity Sensor for Oil Monitoring Based on Data-Driven Approach

In order to convert oil monitoring data into reliable equipment health status information and achieve accurate calibration of viscosity sensors, it has become an important bridge connecting monitoring technology and practical applications. This paper proposes a data-driven calibration method for viscosity sensors in oil monitoring. First, based on the principle of viscosity measurement, a new data processing algorithm model was designed. By utilizing the effective information in the monitoring data, the sensor data were processed and analyzed in a data-driven manner to accurately calibrate the viscosity sensor. Design and build an experimental testing platform, process oil monitoring viscosity sensor data through data-driven methods, measure fluid viscosity over a wide temperature range and validate the proposed calibration algorithm. The experimental results show that the viscosity sensor calibrated using this method has successfully controlled the average absolute error of viscosity measurement within 5%, demonstrating excellent performance. In addition, through data-driven approaches, algorithms can adaptively adjust and optimize sensor parameters, effectively responding to external interference factors such as changes in oil composition and temperature fluctuations, thereby ensuring the stability and reliability of sensors during long-term operation.