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However, most data\u2010driven intelligent diagnosis methods neglect to incorporate domain knowledge, leading to a lack of interpretability. To address this limitation, a dual\u2010channel fault diagnosis model is proposed in this paper, which includes a time\u2010domain channel and a frequency\u2010domain channel network. The two channels can extract features independently, and the diagnostic results are fused into probability values for decision\u2010making so that the model has better diagnostic performance. In the frequency\u2010domain channel, a network architecture combining attention mechanisms is proposed based on the characteristics of bearing fault frequencies. A multi\u2010scale convolution module (MSCM) is employed to extract local features of the spectrum at multiple scales, while a global self\u2010attention module (GSAM) is utilised to capture global features. Since time\u2010domain signals only reflect the variation of vibration amplitude over time and cannot directly reveal fault features, a multi\u2010frequency band feature attention module (MBFAM) is introduced in the time\u2010domain channel to adaptively focus on different frequency band information. The proposed method integrates frequency information into the network model, providing higher interpretability, which is also confirmed by experimental validation.<\/jats:p>","DOI":"10.1111\/exsy.70270","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T01:49:37Z","timestamp":1777340977000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Frequency\u2010Informed Dual\u2010Channel Neural Network for Bearing Fault Diagnosis"],"prefix":"10.1111","volume":"43","author":[{"given":"Aijun","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering North China Electric Power University  Baoding China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering North China Electric Power University  Baoding China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuohao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering North China Electric Power University  Baoding China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianze","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering Taiyuan University of Science and Technology  Taiyuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8309-1977","authenticated-orcid":false,"given":"Ling","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering North China Electric Power University  Baoding China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2013.05.015"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/10589759.2025.2472292"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2022.04.043"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3177459"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13407"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3132327"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2022.3199985"},{"key":"e_1_2_10_9_1","doi-asserted-by":"crossref","unstructured":"He K. 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