A Lightweight Multi-Sensor Feature-Level Fusion Framework for Noise-Robust Fault Diagnosis of High-Precision Milling Machines.
- 1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
- 2School of Mechanical and Electrical Engineering, Lianyungang Technical College, Lianyungang 222000, China.
Abstract
The operational reliability of milling machines is a critical factor affecting manufacturing quality and productivity in modern industrial systems. Although multi-sensor fusion can overcome the limitations of single-sensor monitoring, many existing methods rely on computationally intensive deep learning models, which restrict their applicability in resource-constrained industrial environments. To address this issue, this paper presents a multi-sensor feature-level fusion framework for fault diagnosis in high-precision milling machines. The proposed framework integrates vibration signals from three sensing directions through signal preprocessing, multi-domain feature extraction, random-forest-based feature selection, feature-level fusion, and lightweight neural classification. Within this framework, a lightweight multilayer perceptron (MLP) is adopted as the default classifier to achieve an effective balance between diagnostic performance and computational efficiency for edge-oriented deployment. The experiments consider five machine states, comprising two non-fault operating states (No_Load and Normal) and three abnormal or fault-related states (Eccentricity, Bearing_Housing_Shim_15, and Bearing_Housing_Loose). Experimental results show that the proposed framework substantially outperforms single-sensor approaches, improving diagnostic accuracy from 74.00-86.00% to 96.00%. Comparative analysis across different classifier backbones further demonstrates that the lightweight MLP provides the most favorable efficiency-performance trade-off, while the multi-domain fusion (MDF) mechanism exhibits the strongest robustness under noisy conditions, maintaining 94.38 ± 1.96% accuracy at 0 dB SNR and 88.88 ± 2.53% at -10 dB SNR. These results indicate that accurate, efficient, and noise-robust milling-machine fault diagnosis can be achieved through a coordinated combination of multi-sensor feature fusion, lightweight classification, and robust fusion design, thereby providing a practical solution for intelligent industrial condition monitoring.
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BibTeX
@article{xu2026lightweight,
title = {{A Lightweight Multi-Sensor Feature-Level Fusion Framework for Noise-Robust Fault Diagnosis of High-Precision Milling Machines.}},
author = {Zhi Xu and Ningyun Lu and Liguo Jin},
journal = {Sensors (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/s26206449},
url = {https://europepmc.org/article/MED/42944315},
note = {CC BY},
}
RIS
TY - JOUR
TI - A Lightweight Multi-Sensor Feature-Level Fusion Framework for Noise-Robust Fault Diagnosis of High-Precision Milling Machines.
AU - Zhi Xu
AU - Ningyun Lu
AU - Liguo Jin
PY - 2026
DA - 2026/10/12
JO - Sensors (Basel, Switzerland)
DO - 10.3390/s26206449
UR - https://europepmc.org/article/MED/42944315
AB - The operational reliability of milling machines is a critical factor affecting manufacturing quality and productivity in modern industrial systems. Although multi-sensor fusion can overcome the limitations of single-sensor monitoring, many existing methods rely on computationally intensive deep learning models, which restrict their applicability in resource-constrained industrial environments. To address this issue, this paper presents a multi-sensor feature-level fusion framework for fault diagnosis in high-precision milling machines. The proposed framework integrates vibration signals from three sensing directions through signal preprocessing, multi-domain feature extraction, random-forest-based feature selection, feature-level fusion, and lightweight neural classification. Within this framework, a lightweight multilayer perceptron (MLP) is adopted as the default classifier to achieve an effective balance between diagnostic performance and computational efficiency for edge-oriented deployment. The experiments consider five machine states, comprising two non-fault operating states (No_Load and Normal) and three abnormal or fault-related states (Eccentricity, Bearing_Housing_Shim_15, and Bearing_Housing_Loose). Experimental results show that the proposed framework substantially outperforms single-sensor approaches, improving diagnostic accuracy from 74.00-86.00% to 96.00%. Comparative analysis across different classifier backbones further demonstrates that the lightweight MLP provides the most favorable efficiency-performance trade-off, while the multi-domain fusion (MDF) mechanism exhibits the strongest robustness under noisy conditions, maintaining 94.38 ± 1.96% accuracy at 0 dB SNR and 88.88 ± 2.53% at -10 dB SNR. These results indicate that accurate, efficient, and noise-robust milling-machine fault diagnosis can be achieved through a coordinated combination of multi-sensor feature fusion, lightweight classification, and robust fusion design, thereby providing a practical solution for intelligent industrial condition monitoring.
SN - 1424-8220
ER -