{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T05:09:02Z","timestamp":1789016942668,"version":"build-2803163510"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T00:00:00Z","timestamp":1788652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China National Machinery Industry Corporation","award":["ZDZX2024-66"],"award-info":[{"award-number":["ZDZX2024-66"]}]},{"award":["ZDZX2024-66"],"award-info":[{"award-number":["ZDZX2024-66"]}],"id":[{"id":"https:\/\/ror.org\/04wd74742","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cross-condition fault diagnosis of rotating machinery remains challenging under variable speed and load because vibration signals have strong non-stationarity and exhibit distribution shifts across operating conditions. To address this problem, a tacholess diagnosis framework combining time\u2013frequency ridge extraction with an uncertainty-guided distribution-regularised convolutional Wasserstein autoencoder (UDR-CWAE) is proposed. Firstly, instantaneous rotational frequency is estimated directly from vibration signals using harmonic-amplitude-based ridge initialisation, edge-constrained search, and cost-function-based tracking. Then, the estimated rotational frequency is integrated to construct single-rotation-cycle vibration samples, which are normalised to reduce the discrepancy of amplitude scale among samples. Finally, UDR-CWAE regularises the aggregated latent distribution using maximum mean discrepancy, while uncertainty weighting adaptively balances reconstruction, distribution-regularisation, and classification losses. Cross-condition experiments on the Ottawa bearing dataset and the SQI test-rig dataset achieved classification accuracies of 99.98% and 99.23%, respectively. These results demonstrate that the proposed framework provides accurate tacholess speed estimation and robust fault recognition under unseen rotational-frequency conditions.<\/jats:p>","DOI":"10.3390\/s26175666","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T12:12:25Z","timestamp":1788783145000},"page":"5666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Condition Fault Diagnosis of Rolling Bearings Based on Time\u2013Frequency Ridge Extraction and Uncertainty-Guided Distribution-Regularised Convolutional Wasserstein Autoencoder"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0635-9513","authenticated-orcid":false,"given":"Junshen","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2032an Jiaotong University, Xi\u2032an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jixing","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2032an Jiaotong University, Xi\u2032an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinfa","family":"Shi","sequence":"additional","affiliation":[{"name":"Guangzhou Mechanical Engineering Research Institute Co., Ltd., Guangzhou 510700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9861-1173","authenticated-orcid":false,"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Technology, Xi\u2032an Jiaotong University, Xi\u2032an 710049, China"},{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e70278","DOI":"10.1002\/cpe.70278","article-title":"A Fault Diagnosis Method for Centrifugal Compressors Based on Ontology and Bayesian Network Fusion Reasoning","volume":"37","author":"Zhou","year":"2025","journal-title":"Concurr. 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