Predictive Analysis and Algorithmic Comparison for Faults in Laboratory Force-Measuring Equipment

This study aims to elevate digital equipment management and reduce laboratory management costs by developing a predictive model for laboratory equipment faults, thus making effective use of fault data. Operational fault data from force-measuring equipment was selected for analysis. A correlation analysis of influencing factors was conducted using statistical methods, and three regression models—RidgeCV, XGBoost, and LightGBM—were employed to fit the dataset. These models were compared to select the most appropriate algorithm for predicting the time before the first equipment fault. Model accuracy was evaluated using r², mean squared error, explained variance, and mean absolute error. The LightGBM algorithm, optimized through grid search and cross-validation, demonstrated the best predictive accuracy and operational speed. Key features for determining the time before the first fault included the equipment's service time and its original value. By effectively managing equipment fault data and leveraging big data analysis techniques, a tailored fault prediction model for various equipment types can be established, paving the way for enhanced laboratory management efficiency and quality.