{
  "abstract": "Wheel polygonization is a typical out-of-roundness fault in high-speed trains, and multi-order roughness spectra can quantitatively describe its fault condition. Variations in operating speed, wheel diameter, and reprofiling state cause differences in axle-box vibration responses, making the diagnosis of multi-order roughness amplitudes difficult. A fractional-order-enhanced transform-domain multi-instance learning method with FiLM modulation is proposed for quantitative fault diagnosis of wheel polygonization. First, axle-box vibration acceleration, operating speed, and measured roughness data are jointly processed to construct multi-instance samples containing angular-domain time series, order-domain spectra, vibration statistics, and operating-condition information. Second, a model integrating transform-domain multi-instance learning and FiLM modulation is constructed, combining a state-adaptive multi-regression mechanism with order-aware correlation mapping to achieve quantitative diagnosis of roughness amplitudes across orders 1–32. Finally, the Grünwald–Letnikov fractional-order difference is introduced into the loss function to enhance the model’s ability to learn inter-order correlations and overall spectral-shape features, thereby reducing diagnostic errors in multi-order roughness amplitudes. Experimental results show that the proposed method achieves an RMSE of 1.157 dB, 60.5% lower than that of CNN-LSTM, the best-performing comparison method, providing a quantitative diagnostic method for high-speed train wheel condition assessment and reprofiling evaluation.",
  "abstract_shown": true,
  "affiliations": [
    "School of Transportation and Electrical Engineering, Hunan University of Technology, Zhuzhou 412007, China",
    "National Key Laboratory for Heavy-Duty High-Power Electric Locomotives Zhuzhou, Zhuzhou 412001, China",
    "CRRC Zhuzhou Locomotive Co., Ltd., Zhuzhou 412001, China",
    "Zhuzhou Times New Material Technology Co., Ltd., Zhuzhou 412007, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tao Li",
      "orcid": "0000-0002-6679-3969"
    },
    {
      "affs": [
        0
      ],
      "name": "Xuyuan Gao"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Zhoncheng Jiang"
    },
    {
      "affs": [
        3
      ],
      "name": "Wei Liu"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Dengke Li"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Xuancheng Yuan"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Bowen Yao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/23gav5gqvk5sd.bib",
  "cited_by": 0,
  "doi": "10.3390/fractalfract10110774",
  "fulltext_url": null,
  "id": "23gav5gqvk5sd",
  "issns": [
    "2504-3110"
  ],
  "landing_url": "https://www.mdpi.com/2504-3110/10/11/774",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/23gav5gqvk5sd.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T00:21:06Z",
      "native_id": "10.3390/fractalfract10110774",
      "source": "crossref",
      "url": "https://www.mdpi.com/2504-3110/10/11/774"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2504-3110",
      "subfield": "analysis-pdes"
    }
  ],
  "title": "Quantitative Fault Diagnosis of Railway Wheel Polygonization Based on Fractional-Order-Enhanced Transform-Domain Multi-Instance Learning and FiLM Modulation",
  "title_text": "Quantitative Fault Diagnosis of Railway Wheel Polygonization Based on Fractional-Order-Enhanced Transform-Domain Multi-Instance Learning and FiLM Modulation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T00:21:05Z",
  "url": "https://research.draconitic.com/paper/23gav5gqvk5sd",
  "venue": "Fractal and Fractional",
  "versions": []
}