FAULT PREDICTION OF FASTENER PRODUCTION EQUIPMENT VIA LIGHTWEIGHT REGRESSION MODELING WITH SCIKIT-LEARN: EVIDENCE FROM THE HAIYAN FASTENER INDUSTRY
Keywords:
Fault prediction, Regression modeling, Scikit-learn, Fastener manufacturing, Remaining useful lifeAbstract
In fastener manufacturing, an unexpected breakdown of a cold heading machine stops the entire production line, yet most small and medium-sized enterprises (SMEs) still maintain their equipment by rule of thumb. Targeting the SMEs in the Haiyan fastener industry cluster, this paper takes cold heading machines as the object and builds a low-cost equipment fault prediction method based on regression modeling and Scikit-learn. The remaining useful life (RUL) of key components is taken as the prediction target; four models, namely linear regression, random forest, gradient boosting regression tree (GBRT), and support vector regression (SVR), are compared, and grid search is used for parameter tuning. Since such enterprises generally lack usable historical failure data, the method is validated on real degradation data from the public XJTU-SY accelerated life test dataset of rolling bearings, which share the same data form as the key rotating components of cold heading machines. The results show that GBRT performs best (RMSE = 6.25 min, R² = 0.511), that degradation information is concentrated in a few amplitude-type statistical features such as mean amplitude, standard deviation and root mean square, and that in the early-warning replay on two test bearings, alarms are all raised ahead of failure with an average lead time of 8.0 min and no false alarm. The study indicates that lightweight AI tools are sufficient to support equipment fault prediction in county-level manufacturing SMEs, and the whole procedure can be replicated directly.References
[1] Carvalho T P, Soares F A A M N, Vita R, et al. A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 2019, 137: 106024.
[2] Lan J F, Zhou Y H, Gao Z L, et al. Machine learning based prediction model for degradation trend of hydropower units. Journal of Hydroelectric Engineering, 2022, 41(12): 135-144.
[3] Tong J F, Feng Z G, Jiang Y L, et al. Simulation and experimental study on cold heading of TB9 titanium alloy mandrel. Forging & Stamping Technology, 2023, 48(8): 32-40.
[4] Tang F, Xing B Y, He X C, et al. Sensitivity analysis and optimization of clinching process parameters. Journal of Plasticity Engineering, 2022, 29(6): 119-124.
[5] Cinar Z M, Nuhu A A, Zeeshan Q, et al. Machine learning in predictive maintenance towards sustainable smart manufacturing in Industry 4.0. Sustainability, 2020, 12(19): 8211.
[6] Zhang G H, Luo Z P. Data-driven fault prediction of the main feedwater pump in nuclear power plants. Atomic Energy Science and Technology, 2024, 58(9): 1982-1988.
[7] Lei Y, Yang B, Jiang X, et al. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 2020, 138: 106587.
[8] Lei Y, Li N, Guo L, et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 2018, 104: 799-834.
[9] Zhao R, Yan R, Chen Z, et al. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 2019, 115: 213-237.
[10] Huang R Y, Li J P, Wang Z, et al. Intelligent diagnosis and life prediction of equipment based on multi-task learning. Scientia Sinica (Technologica), 2022, 52(1): 123-137.
[11] Jin L J, Xu X, Wu S W, et al. Current status and prospects of deep learning driven predictive maintenance of equipment. Manufacturing Automation, 2025, 47(2): 123-131.
[12] Li K P, Zhang F L, Huang Z G, et al. Interpretable fault diagnosis method based on XGBoost and SHAP analysis. Manufacturing Technology & Machine Tool, 2025(6): 199-208.
[13] Jiang H G, Tang Y, Tang L F, et al. Research and application of motor vibration state monitoring based on decision tree. Computer Applications and Software, 2026, 43(4): 90-95, 109.
[14] Fathi K, van de Venn H W. Data, models, and performance: A comprehensive guide to predictive maintenance in industrial settings. Recent Topics in Maintenance Management. London: IntechOpen, 2024.
[15] Igbokwe N C, Nwamekwe C O, Aguh P S. Predictive modeling of manufacturing defects using machine learning: A comparative performance study in a manufacturing SME. African Journal of Advances in Engineering and Technology, 2025, 1(2): 93-115.
[16] Wang B, Lei Y, Li N, et al. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 2020, 69(1): 401-412.