{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T15:52:24Z","timestamp":1780674744886,"version":"3.54.1"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011789","name":"Jilin Provincial Department of Science and Technology","doi-asserted-by":"publisher","award":["20230101208JC"],"award-info":[{"award-number":["20230101208JC"]}],"id":[{"id":"10.13039\/501100011789","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Bearings, as core components of mechanical equipment, play a critical role in ensuring equipment safety and reliability. Early fault detection holds significant importance. Addressing the challenges of insufficient robustness in bearing fault diagnosis under industrial high-noise conditions and the difficulty of extracting fault features from a single modality, this study proposes a three-channel multimodal fault diagnosis method that integrates a Convolutional Auto-Encoder (CAE) with a dual attention mechanism (M-CNNBiAM). This approach provides an effective technical solution for the precise diagnosis of bearing faults in high-noise environments. To suppress substantial noise interference, a CAE denoising module was designed to filter out intense noise, providing high-quality input for subsequent diagnostic networks. To address the limitations of single-modal feature extraction and restricted generalization capabilities, a three-channel time\u2013frequency signal joint diagnosis model combining the Continuous Wavelet Transform (CWT) with an attention mechanism was proposed. This approach enables deep mining and efficient fusion of multi-domain features, thereby enhancing fault diagnosis accuracy and generalization capabilities. Experimental results demonstrate that the designed CAE module maintains excellent noise reduction performance even under \u221210 dB strong noise conditions. When combined with the proposed diagnostic model, it achieves an average diagnostic accuracy of 98% across both the CWRU and self-test datasets, demonstrating outstanding diagnostic precision. Furthermore, under \u22124 dB noise conditions, it achieves a 94% diagnostic accuracy even without relying on the CAE denoising module. With a single training cycle taking only 6.8 s, it balances training efficiency and diagnostic performance, making it well-suited for real-time, reliable bearing fault diagnosis in industrial environments with high noise levels.<\/jats:p>","DOI":"10.3390\/a19020144","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T15:23:14Z","timestamp":1770736994000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multimodal Three-Channel Bearing Fault Diagnosis Method Based on CNN Fusion Attention Mechanism Under Strong Noise Conditions"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1569-0764","authenticated-orcid":false,"given":"Yingyong","family":"Zou","sequence":"first","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunfang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0516-9819","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Si","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicular Engineering, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112330","DOI":"10.1016\/j.ymssp.2025.112330","article-title":"Fast Sparse Morphological Decomposition with Controllable Sparsity for High-Speed Bearing Fault Diagnosis","volume":"226","author":"Jin","year":"2025","journal-title":"Mech. 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