{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T10:53:38Z","timestamp":1769165618930,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T00:00:00Z","timestamp":1592956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61803329"],"award-info":[{"award-number":["61803329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2016203496, F2018203413"],"award-info":[{"award-number":["F2016203496, F2018203413"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Research and Development Program of Hebei Province","award":["19214306D"],"award-info":[{"award-number":["19214306D"]}]},{"name":"the China Postdoctoral Science Foundation","award":["2018M640247"],"award-info":[{"award-number":["2018M640247"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sensor fault detection of wind turbines plays an important role in improving the reliability and stable operation of turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides promising insights into sensor fault detection due to the accessibility of the data and the abundance of sensor information. However, SCADA data are essentially multivariate time series with inherent spatio-temporal correlation characteristics, which has not been well considered in the existing wind turbine fault detection research. This paper proposes a novel classification-based fault detection method for wind turbine sensors. To better capture the spatio-temporal characteristics hidden in SCADA data, a multiscale spatio-temporal convolutional deep belief network (MSTCDBN) was developed to perform feature learning and classification to fulfill the sensor fault detection. A major superiority of the proposed method is that it can not only learn the spatial correlation information between several different variables but also capture the temporal characteristics of each variable. Furthermore, this method with multiscale learning capability can excavate interactive characteristics between variables at different scales of filters. A generic wind turbine benchmark model was used to evaluate the proposed approach. The comparative results demonstrate that the proposed method can significantly enhance the fault detection performance.<\/jats:p>","DOI":"10.3390\/s20123580","type":"journal-article","created":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T10:54:59Z","timestamp":1592996099000},"page":"3580","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2146-546X","authenticated-orcid":false,"given":"Hong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China"}]},{"given":"Hongbin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China"}]},{"given":"Guoqian","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China"}]},{"given":"Yueling","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China"}]},{"given":"Shuang","family":"Ren","sequence":"additional","affiliation":[{"name":"China Academy of Information and Communication Institute, Beijing 100089, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.renene.2014.11.045","article-title":"Maintenance logistics organization for offshore wind energy: Current progress and future perspectives","volume":"77","author":"Shafiee","year":"2015","journal-title":"Renew. 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