{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.",
    "School of Mechanical and Electrical Engineering, Lianyungang Technical College, Lianyungang 222000, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Zhi Xu"
    },
    {
      "affs": [
        0
      ],
      "name": "Ningyun Lu"
    },
    {
      "affs": [
        0
      ],
      "name": "Liguo Jin",
      "orcid": "0009-0001-1986-1180"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/435mzaa8njxe3.bib",
  "cited_by": 0,
  "doi": "10.3390/s26206449",
  "fulltext_url": "https://europepmc.org/articles/PMC13709204",
  "id": "435mzaa8njxe3",
  "issns": [
    "1424-8220"
  ],
  "landing_url": "https://europepmc.org/article/MED/42944315",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2207|Machine Fault Diagnosis Techniques"
    ]
  ],
  "openalex": "W7221553222",
  "pdf_url": "https://europepmc.org/articles/PMC13709204?pdf=render",
  "pmcid": "PMC13709204",
  "pmid": "42944315",
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/435mzaa8njxe3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:12Z",
      "native_id": "MED:42944315",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42944315"
    }
  ],
  "subjects": [],
  "title": "A Lightweight Multi-Sensor Feature-Level Fusion Framework for Noise-Robust Fault Diagnosis of High-Precision Milling Machines.",
  "title_text": "A Lightweight Multi-Sensor Feature-Level Fusion Framework for Noise-Robust Fault Diagnosis of High-Precision Milling Machines.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:12Z",
  "url": "https://research.draconitic.com/paper/435mzaa8njxe3",
  "venue": "Sensors (Basel, Switzerland)",
  "versions": []
}