Artificial Intelligence-Driven Quality Control in Mechanical Manufacturing: Vibration-Based Multiclass Gear Fault Detection Using LightGBM

Abstract

Artificial intelligence is increasingly used to improve industrial quality control, but its practical value depends on whether models remain accurate under different operating conditions and fault classes. This study evaluates an artificial-intelligence-based workflow for gear quality control using vibration signals measured on a real two-stage reduction gearbox. Two orthogonal vibration channels were analyzed for six health states, three shaft speeds, and two load levels. Because the time-series data were only partly stationary, the dataset was divided chronologically into training and test segments. A 54-feature representation was built from rolling-window statistics and operating variables, and six classifiers were compared: Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBM), random forest, decision tree, multilayer perceptron (MLP), and logistic regression. LGBM achieved the best overall accuracy (0.9728) while maintaining substantially lower training time than several competing nonlinear models. Class-wise precision, recall, and F1-score ranged from 0.95 to 1.00, and the nominal response time for most operating-condition transitions was approximately 0.0998 s. The results show that vibration-based machine learning can support robust, near-real-time fault identification in mechanical manufacturing environments. The study also highlights the importance of chronological validation, feature engineering over multiple time windows, and the trade-off between predictive performance and deployment efficiency. Because the validation dataset originates from one gearbox platform, the results should be interpreted as promising internal evidence rather than as proof of universal industrial robustness.