Comparative study of sensor-generated operational data, equational calculation, and temporal data acquisition for optimal predictive maintenance decisions

Predictive maintenance greatly enhances equipment health monitoring and performance, this strategy predicts failures before they occur, allowing for focused and timely interventions; it does this by combining real-time sensor data trending, machine learning, and sophisticated data analytics. This study compares equational calculations with sensor-generated data, it further investigates the stability, accuracy, and reliability of electric motor RPM data collected at different intervals to find the most proficient data acquisition techniques for effective predictive maintenance decisions. Statistical metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Absolute Error (AE) were utilized to quantify variances, the assessment approach consisted of two distinct phases: an initial accuracy assessment to measure discrepancies between sensor-generated data and calculated value; this was followed by evaluating the stability, accuracy, and reliability of RPM data collected at short intervals and those collected at longer intervals. Key findings indicate that sensor-generated RPM readings at short intervals provide detailed insights into electric motor transient behaviours despite greater variability and error margins. This high-frequency data provides a detailed insight into motor function by capturing extensive deviation patterns that long-term data trending could not. Also, sensor-generated data shows a substantial ability to give precise insights into transitory behaviors compared to equationally computed value, making it an appropriate option for predictive maintenance. The study advances the subject of predictive maintenance by providing useful recommendations for improving maintenance procedures, increasing equipment reliability, and reducing downtime and costs.