{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T04:02:46Z","timestamp":1768622566078,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) of China","award":["311021013"],"award-info":[{"award-number":["311021013"]}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) of China","award":["51775037"],"award-info":[{"award-number":["51775037"]}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) of China","award":["FRF-BD-18-001A"],"award-info":[{"award-number":["FRF-BD-18-001A"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["311021013"],"award-info":[{"award-number":["311021013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51775037"],"award-info":[{"award-number":["51775037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["FRF-BD-18-001A"],"award-info":[{"award-number":["FRF-BD-18-001A"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018594","name":"Fundamental Research Funds for Central Universities of China","doi-asserted-by":"publisher","award":["311021013"],"award-info":[{"award-number":["311021013"]}],"id":[{"id":"10.13039\/501100018594","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018594","name":"Fundamental Research Funds for Central Universities of China","doi-asserted-by":"publisher","award":["51775037"],"award-info":[{"award-number":["51775037"]}],"id":[{"id":"10.13039\/501100018594","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018594","name":"Fundamental Research Funds for Central Universities of China","doi-asserted-by":"publisher","award":["FRF-BD-18-001A"],"award-info":[{"award-number":["FRF-BD-18-001A"]}],"id":[{"id":"10.13039\/501100018594","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Since failure of steam turbines occurs frequently and can causes huge losses for thermal plants, it is important to identify a fault in advance. A novel clustering fault diagnosis method for steam turbines based on t-distribution stochastic neighborhood embedding (t-SNE) and extreme gradient boosting (XGBoost) is proposed in this paper. First, the t-SNE algorithm was used to map the high-dimensional data to the low-dimensional space; and the data clustering method of K-means was performed in the low-dimensional space to distinguish the fault data from the normal data. Then, the imbalance problem in the data was processed by the synthetic minority over-sampling technique (SMOTE) algorithm to obtain the steam turbine characteristic data set with fault labels. Finally, the XGBoost algorithm was used to solve this multi-classification problem. The data set used in this paper was derived from the time series data of a steam turbine of a thermal power plant. In the processing analysis, the method achieved the best performance with an overall accuracy of 97% and an early warning of at least two hours in advance. The experimental results show that this method can effectively evaluate the condition and provide fault warning for power plant equipment.<\/jats:p>","DOI":"10.3390\/a16020098","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T05:51:06Z","timestamp":1676008266000},"page":"98","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhiguo","family":"Liang","sequence":"first","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5273-6867","authenticated-orcid":false,"given":"Lijun","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Innovation Group of Marine Engineering Materials and Corrosion Control, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China"},{"name":"Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Xizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yu, J., Jang, J., Yoo, J., Park, J.H., and Kim, S. (2018). A fault isolation method via classification and regression tree-based variable ranking for drum-type steam boiler in thermal power plant. Energies, 11.","DOI":"10.3390\/en11051142"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1109\/TLA.2018.8444388","article-title":"Fault diagnosis in sensors of boiler following control of a thermal power plant","volume":"16","author":"Madrigal","year":"2018","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_3","first-page":"1026","article-title":"Fault diagnosis method of peak-load-regulation steam turbine based on improved PCA-HKNN artificial neural network","volume":"235","author":"Wu","year":"2021","journal-title":"Proc. Inst. Mech. Eng. O J. Risk Reliab."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ymssp.2017.09.023","article-title":"Mechanical model development of rolling bearing-rotor systems: A review","volume":"102","author":"Cao","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106990","DOI":"10.1016\/j.ymssp.2020.106990","article-title":"Autocorrelated Envelopes for early fault detection of rolling bearings","volume":"146","author":"Xu","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kazemi, P., Ghisi, A., and Mariani, S. (2022). Classification of the Structural Behavior of Tall Buildings with a Diagrid Structure: A Machine Learning-Based Approach. Algorithms, 15.","DOI":"10.3390\/a15100349"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6248","DOI":"10.1109\/TIE.2020.2994868","article-title":"Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets","volume":"68","author":"Shi","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, P., Gao, Z., Cao, L., Dong, F., Zhou, Y., Wang, K., Zhang, Y., and Sun, P. (2022). Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy. Machines, 10.","DOI":"10.3390\/machines10020072"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s10033-019-0428-5","article-title":"Particle swarm optimization-Support Vector Machine model for machinery fault diagnoses in high-voltage circuit breakers","volume":"33","author":"Li","year":"2020","journal-title":"Chin. J. Mech. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1177\/0954406220951209","article-title":"Prediction of performance deterioration of rolling bearing based on JADE and PSO-SVM","volume":"235","author":"Zan","year":"2020","journal-title":"Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103678","DOI":"10.1016\/j.engappai.2020.103678","article-title":"Potential, challenges and future directions for deep learning in prognostics and health management applications","volume":"92","author":"Fink","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2445","DOI":"10.1007\/s00500-017-2940-9","article-title":"A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm","volume":"23","author":"Deng","year":"2019","journal-title":"Soft Comput."},{"key":"ref_13","first-page":"21","article-title":"Simulation study on fault diagnosis of power electronic circuits based on wavelet packet analysis and support vector machine","volume":"14","author":"Sun","year":"2018","journal-title":"J. Electr. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109434","DOI":"10.1016\/j.anucene.2022.109434","article-title":"An improved generative adversarial network for fault diagnosis of rotating machine in nuclear power plant","volume":"180","author":"Wang","year":"2023","journal-title":"Ann. Nucl. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MPE.2018.2790819","article-title":"Big Data Analytics in China\u2019s Electric Power Industry","volume":"16","author":"Kang","year":"2018","journal-title":"IEEE Power Energy Mag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"174612","DOI":"10.1109\/ACCESS.2019.2957181","article-title":"Review of Power Spatio-Temporal Big Data Technologies for Mobile Computing in Smart Grid","volume":"7","author":"Ma","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"124298","DOI":"10.1016\/j.jclepro.2020.124298","article-title":"A review on long-term electrical power system modeling with energy storage","volume":"280","author":"Lai","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e6647","DOI":"10.1002\/cpe.6647","article-title":"A systematic review of big data in energy analytics using energy computing techniques","volume":"34","author":"Dhanalakshmi","year":"2021","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"42006","DOI":"10.1088\/1755-1315\/300\/4\/042006","article-title":"Analysis and Treatment of Shutdown Due to Bearing Vibration Towards Ultra-supercritical 660MW Turbine","volume":"300","author":"Li","year":"2019","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1016\/j.aej.2021.07.039","article-title":"Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing","volume":"61","author":"Ashraf","year":"2022","journal-title":"Alex. Eng. J."},{"key":"ref_21","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.neucom.2013.11.045","article-title":"Parametric nonlinear dimensionality reduction using kernel t-SNE","volume":"147","author":"Gisbrecht","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"237","DOI":"10.32604\/iasc.2022.020665","article-title":"Applying t-SNE to Estimate Image Sharpness of Low-cost Nailfold Capillaroscopy","volume":"32","author":"Wang","year":"2022","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"620143","DOI":"10.3389\/fgene.2020.620143","article-title":"A t-SNE Based Classification Approach to Compositional Microbiome Data","volume":"11","author":"Xu","year":"2020","journal-title":"Front. Genet."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103856","DOI":"10.1016\/j.infrared.2021.103856","article-title":"Improved fuzzy C-means clustering algorithm based on t-SNE for terahertz spectral recognition","volume":"117","author":"Yi","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109158","DOI":"10.1016\/j.patcog.2022.109158","article-title":"Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles","volume":"135","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4801","DOI":"10.32604\/cmc.2022.025960","article-title":"MCBC-SMOTE: A Majority Clustering Model for Classification of Imbalanced Data","volume":"73","author":"Arora","year":"2022","journal-title":"CMC-Comput. Mater. Contin."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"113728","DOI":"10.1016\/j.dss.2021.113728","article-title":"Fraudulent review detection model focusing on emotional expressions and explicit aspects: Investigating the potential of feature engineering","volume":"155","author":"Kumar","year":"2022","journal-title":"Decis. Support Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1142\/S0218194019500074","article-title":"Identify Severity Bug Report with Distribution Imbalance by CR-SMOTE and ELM","volume":"29","author":"Guo","year":"2019","journal-title":"Int. J. Softw. Eng. Knowl. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2259014","DOI":"10.1142\/S0218001422590145","article-title":"Heavy Overload Prediction Method of Distribution Transformer Based on GBDT","volume":"36","author":"Duan","year":"2022","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s10115-022-01655-y","article-title":"An improved confusion matrix for fusing multiple K-SVD classifiers","volume":"64","author":"Liu","year":"2022","journal-title":"Knowl. Inf. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112988","DOI":"10.1016\/j.eswa.2019.112988","article-title":"Simultaneous feature selection and heterogeneity control for SVM classification: An application to mental workload assessment","volume":"143","author":"Maldonado","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100378","DOI":"10.1016\/j.cosrev.2021.100378","article-title":"Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)","volume":"40","author":"Anowar","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Khan, N., and Taqvi, S.A.A. (2023). Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview. ChemBioEng Rev.","DOI":"10.1002\/cben.202200030"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/2\/98\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:28:18Z","timestamp":1760120898000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/2\/98"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,9]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["a16020098"],"URL":"https:\/\/doi.org\/10.3390\/a16020098","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,9]]}}}