@Article{cmc.2026.084014,
AUTHOR = {Yanan Wang, Xiaoying Yang, Zhijie Pei, Xin Yang, Bo Li},
TITLE = {A Data-Driven Fault Prediction Method for Bearing Ring CNC Grinding Machines},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27708},
ISSN = {1546-2226},
ABSTRACT = {Sudden faults in bearing ring computer numerical control (CNC) grinding machines significantly impact product processing quality and production efficiency, making precise state prediction urgent to avoid downtime risks. However, the numerous operational parameters collected on-site and the focus of existing methods on outputting fault labels without analyzing the evolution trends of the equipment’s operational state lead to unclear fault discrimination criteria and weak traceability, making it difficult to provide effective early-warning support during the incipient stages of a fault. To address these issues, this paper constructs a data-driven integrated algorithm adopting a “predict-then-classify” approach. First, the Pearson-ReliefF algorithm is utilized to eliminate redundant features and retain sensitive parameters. Second, the BiGRU-Attention algorithm is employed to capture bidirectional dependencies in time-series data, realizing the prediction of the equipment’s operational state trends. Finally, the Sparrow Search Algorithm (SSA) is introduced to optimize core Support Vector Machine (SVM) parameters, achieving precise identification of fault types in bearing ring CNC grinding machines. Experimental results indicate that the proposed algorithm exhibits robust performance in both the prediction and classification stages. The prediction metrics MAE, RMSE, and R<sup>2</sup> are 0.0124, 0.0152, and 0.975, respectively, and the average multi-fault identification accuracy based on 10 repeated experiments with random seeds reaches 98.25%. This verifies the effectiveness of the method, which is of significant importance for ensuring the processing quality of bearing rings, reducing operation and maintenance costs, achieving intelligent online predictive diagnosis, and supporting the preventive maintenance of equipment.},
DOI = {10.32604/cmc.2026.084014}
}