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Article

Fault Diagnosis for CNC Machine Tool Feed Systems Based on Enhanced Multi-Scale Feature Network

Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
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Author to whom correspondence should be addressed.
Lubricants 2025, 13(8), 350; https://doi.org/10.3390/lubricants13080350
Submission received: 23 June 2025 / Revised: 30 July 2025 / Accepted: 31 July 2025 / Published: 5 August 2025
(This article belongs to the Special Issue Advances in Tool Wear Monitoring 2025)

Abstract

Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi‑scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) that addresses these limitations through three integrated modules designed to extract critical fault features from vibration signals. First, a Soft-Scale Denoising (S2D) module forms the backbone of the MSFN, capturing multi-scale fault features from input signals. Second, a Multi-Scale Adaptive Feature Enhancement (MS-AFE) module based on long-range weighting mechanisms is developed to enhance the extraction of periodic fault features. Third, a Dynamic Sequence–Channel Attention (DSCA) module is incorporated to improve feature representation across channel and sequence dimensions. Experimental results on two datasets demonstrate that the proposed MSFN achieves high diagnostic accuracy and exhibits robust generalization across diverse operating conditions. Moreover, ablation studies validate the effectiveness and contributions of each module.
Keywords: fault diagnosis; multi-scale feature extraction; attention mechanism; variable operating conditions; CNC feed system fault diagnosis; multi-scale feature extraction; attention mechanism; variable operating conditions; CNC feed system

Share and Cite

MDPI and ACS Style

Zhang, P.; Huang, M.; Sun, W. Fault Diagnosis for CNC Machine Tool Feed Systems Based on Enhanced Multi-Scale Feature Network. Lubricants 2025, 13, 350. https://doi.org/10.3390/lubricants13080350

AMA Style

Zhang P, Huang M, Sun W. Fault Diagnosis for CNC Machine Tool Feed Systems Based on Enhanced Multi-Scale Feature Network. Lubricants. 2025; 13(8):350. https://doi.org/10.3390/lubricants13080350

Chicago/Turabian Style

Zhang, Peng, Min Huang, and Weiwei Sun. 2025. "Fault Diagnosis for CNC Machine Tool Feed Systems Based on Enhanced Multi-Scale Feature Network" Lubricants 13, no. 8: 350. https://doi.org/10.3390/lubricants13080350

APA Style

Zhang, P., Huang, M., & Sun, W. (2025). Fault Diagnosis for CNC Machine Tool Feed Systems Based on Enhanced Multi-Scale Feature Network. Lubricants, 13(8), 350. https://doi.org/10.3390/lubricants13080350

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