{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:34:08Z","timestamp":1760146448398,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Steam turbine blades may crack, break, or suffer other failures due to high temperatures, high pressures, and high-speed rotation, which seriously threatens the safety and reliability of the equipment. The signal characteristics of different fault types are slightly different, making it difficult to accurately classify the faults of rotating blades directly through vibration signals. This method combines a one-dimensional convolutional neural network (1DCNN) and a channel attention mechanism (CAM). 1DCNN can effectively extract local features of time series data, while CAM assigns different weights to each channel to highlight key features. To further enhance the efficacy of feature extraction and classification accuracy, a projection head is introduced in this paper to systematically map all sample features into a normalized space, thereby improving the model\u2019s capacity to distinguish between distinct fault types. Finally, through the optimization of a supervised contrastive learning (SCL) strategy, the model can better capture the subtle differences between different fault types. Experimental results show that the proposed method has an accuracy of 99.61%, 97.48%, and 96.22% in the classification task of multiple crack fault types at three speeds, which is significantly better than Multilayer Perceptron (MLP), Residual Network (ResNet), Momentum Contrast (MoCo), and Transformer methods.<\/jats:p>","DOI":"10.3390\/e26110956","type":"journal-article","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T11:13:37Z","timestamp":1730891617000},"page":"956","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning"],"prefix":"10.3390","volume":"26","author":[{"given":"Qinglei","family":"Zhang","sequence":"first","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laifeng","family":"Tang","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyun","family":"Qin","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianguo","family":"Duan","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Zhou","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131633","DOI":"10.1016\/j.energy.2024.131633","article-title":"Design and optimization of slot number in supercooled vapor suction in steam turbine blades for reducing the wetness","volume":"301","author":"Hosseini","year":"2024","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1016\/j.gsf.2017.09.011","article-title":"Fatigue crack growth investigation on offshore pipelines with three-dimensional interacting cracks","volume":"9","author":"Zhang","year":"2018","journal-title":"Geosci. 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