{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T01:05:05Z","timestamp":1775178305541,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T00:00:00Z","timestamp":1674259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Siemens Energy"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today\u2019s Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance; thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance.<\/jats:p>","DOI":"10.3390\/s23031236","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T01:36:26Z","timestamp":1674437786000},"page":"1236","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture"],"prefix":"10.3390","volume":"23","author":[{"given":"Mart\u00ed","family":"de Castro-Cros","sequence":"first","affiliation":[{"name":"Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Automatic Control Department, Universitat Polit\u00e8cnica de Catalunya, Campus Nord, Carrer de Jordi Girona, 1, 3, 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manel","family":"Velasco","sequence":"additional","affiliation":[{"name":"Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Automatic Control Department, Universitat Polit\u00e8cnica de Catalunya, Campus Nord, Carrer de Jordi Girona, 1, 3, 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9589-8199","authenticated-orcid":false,"given":"Cecilio","family":"Angulo","sequence":"additional","affiliation":[{"name":"Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Automatic Control Department, Universitat Polit\u00e8cnica de Catalunya, Campus Nord, Carrer de Jordi Girona, 1, 3, 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,21]]},"reference":[{"key":"ref_1","unstructured":"Boyce, M.P. (2011). Gas Turbine Engineering Handbook, Elsevier."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101307","DOI":"10.1016\/j.jup.2021.101307","article-title":"Payback of natural gas turbines: A retrospective analysis with implications for decarbonizing grids","volume":"73","author":"Carvalho","year":"2021","journal-title":"Util. Policy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.rser.2018.03.006","article-title":"Liquid biofuels utilization for gas turbines: A review","volume":"90","author":"Enagi","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1115\/1.1340629","article-title":"Degradation in Gas Turbine Systems","volume":"123","author":"Kurz","year":"2000","journal-title":"J. Eng. Gas Turbines Power"},{"key":"ref_5","unstructured":"Kurz, R., Meher-Homji, C., Brun, K., Moore, J.J., and Gonzalez, F. (2013). Proceedings of the 42nd Turbomachinery Symposium, Texas A&M University, Turbomachinery Laboratories."},{"key":"ref_6","unstructured":"Holmberg, K., Komonen, K., Oedewald, P., Peltonen, M., Reiman, T., Rouhiainen, V., Tervo, J., and Heino, P. (2004). Safety and Reliability\u2014Technology Review, VTT Technical Research Centre of Finland. Number BTUO43-031209 in VTT Research Report."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cheng, C., Wang, J., Chen, H., Chen, Z., Luo, H., and Xie, P. (2021). A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches. Entropy, 23.","DOI":"10.37247\/ETNI.1.2021.23"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s13634-016-0355-x","article-title":"A survey of machine learning for big data processing","volume":"2016","author":"Qiu","year":"2016","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_9","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ymssp.2015.10.025","article-title":"Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data","volume":"72","author":"Jia","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109838","DOI":"10.1016\/j.chaos.2020.109838","article-title":"Sparse stacked autoencoder network for complex system monitoring with industrial applications","volume":"137","author":"Deng","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_12","unstructured":"Farahani, M. (2022, December 01). Anomaly Detection on Gas Turbine Time-Series\u2019 Data Using Deep LSTM-Autoencoder. Available online: https:\/\/www.diva-portal.org\/smash\/record.jsf?pid=diva2:1527608."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104199","DOI":"10.1016\/j.engappai.2021.104199","article-title":"A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection","volume":"101","author":"Fu","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_14","unstructured":"Yan, W., and Yu, L. (2019). On accurate and reliable anomaly detection for gas turbine combustors: A deep learning approach. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"114159","DOI":"10.1016\/j.apenergy.2019.114159","article-title":"Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network","volume":"259","author":"Han","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107371","DOI":"10.1016\/j.measurement.2019.107371","article-title":"Extracting degradation trends for roller bearings by using a moving-average stacked auto-encoder and a novel exponential function","volume":"152","author":"Xu","year":"2020","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1007\/s13042-022-01583-x","article-title":"Fault detection and diagnosis for industrial processes based on clustering and autoencoders: A case of gas turbines","volume":"13","author":"Barrera","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100067","DOI":"10.1016\/j.egyai.2021.100067","article-title":"3D convolutional selective autoencoder for instability detection in combustion systems","volume":"4","author":"Gangopadhyay","year":"2021","journal-title":"Energy AI"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","article-title":"Deep learning: Methods and applications","volume":"7","author":"Deng","year":"2014","journal-title":"Found. Trends Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MGRS.2018.2853555","article-title":"A review of the autoencoder and its variants: A comparative perspective from target recognition in synthetic-aperture radar images","volume":"6","author":"Dong","year":"2018","journal-title":"IEEE Geosci. Remote. Sens. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"de Castro-Cros, M., Rosso, S., Bahilo, E., Velasco, M., and Angulo, C. (2021). Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based in Autoencoder. Sensors, 21.","DOI":"10.3390\/s21082708"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1236\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:12:34Z","timestamp":1760119954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,21]]},"references-count":21,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031236"],"URL":"https:\/\/doi.org\/10.3390\/s23031236","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,21]]}}}