{
  "abstract": "Bearing fault diagnosis for rotating machinery remains challenging due to strong background noise and variable operating conditions in harsh industrial environments. To address these issues, this paper proposes a robust and lightweight bearing fault diagnosis framework, termed Multi-scale and Dual-scale Wavelet Mamba (MDW-Mamba), for noisy and non-stationary industrial applications. The proposed method employs a multi-stage “noise-feature decoupling” strategy, comprising a statistically guided three-channel input, a Multi-scale Omni-kernel Convolutions (MOM-Conv) backbone, and a Dual-Scale Wavelet Mamba (DSW-Mamba) module. This architecture explicitly separates fault components from broadband noise while enabling efficient long-term dependency modeling. Experimental results on three public datasets under variable-speed conditions demonstrate that MDW-Mamba consistently outperforms state-of-the-art methods. It achieves an average classification accuracy exceeding 87% even at a signal-to-noise ratio of −6 dB, showing exceptional robustness against strong interference. Furthermore, the model maintains an ultra-lightweight footprint of 0.26 M parameters and a fast inference time of 1.285 ms, indicating strong potential for real-time, on-board deployment in resource-constrained industrial edge devices.",
  "abstract_shown": true,
  "affiliations": [
    "School of Railway Transportation, Shandong Jiaotong University, Jinan, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dayou Cui"
    },
    {
      "name": "Zhaoyan Xie"
    },
    {
      "name": "Zhixue Wang"
    },
    {
      "name": "Xiaowei Li"
    },
    {
      "name": "Xiangxuan Meng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4m360kh5w6af3.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0358759",
  "fulltext_url": null,
  "id": "4m360kh5w6af3",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0358759",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2207|Machine Fault Diagnosis Techniques"
    ],
    [
      "plos",
      "/Computer and information sciences/Information theory/Background signal noise"
    ],
    [
      "plos",
      "/Engineering and technology/Electrical engineering/Electrical faults"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Background signal noise"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Signal filtering"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Signal to noise ratio"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Damage mechanics"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Vibration"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Wavelet transforms"
    ]
  ],
  "openalex": "W7214619406",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358759&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/4m360kh5w6af3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0358759",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0358759"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "plos:physics",
      "subfield": "general"
    }
  ],
  "title": "Robust bearing fault diagnosis for rotating machinery under noisy and variable operating conditions using a Wavelet-Mamba Network",
  "title_text": "Robust bearing fault diagnosis for rotating machinery under noisy and variable operating conditions using a Wavelet-Mamba Network",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/4m360kh5w6af3",
  "venue": "PLOS One",
  "versions": []
}