color rgb, text-indent px, letter-spacing normal, font-family Helvetica, font-size px, font-style normal, font-weight, word-spacing px, display inline important, white-space normal, orphans, widows, background-color rgb, font-variant-ligatures normal, font-variant-ligatures normal, webkit-text-stroke-width px, text-decoration-style initial, text-decoration-color initial, P. H. F. D. Sousa, Navar de Medeiros M. e Nascimento sup, Jefferson S. Almeida sup, Pedro P. Rebouças Filho sup, Victor Hugo C. de Albuquerque sup, span
{"title":"Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment","authors":"color rgb, text-indent px, letter-spacing normal, font-family Helvetica, font-size px, font-style normal, font-weight, word-spacing px, display inline important, white-space normal, orphans, widows, background-color rgb, font-variant-ligatures normal, font-variant-ligatures normal, webkit-text-stroke-width px, text-decoration-style initial, text-decoration-color initial, P. H. F. D. Sousa, Navar de Medeiros M. e Nascimento sup, Jefferson S. Almeida sup, Pedro P. Rebouças Filho sup, Victor Hugo C. de Albuquerque sup, span","doi":"10.33969/AIS.2019.11001","DOIUrl":null,"url":null,"abstract":"The eagerness and necessity to develop so-called smart applications has taken the Internet of Things (IoT) to a whole new level. Industry has been implementing services that use IoT to increase productivity as well as management systems over the past couple of years. Such services are now encroaching on wind energy, which nowadays is the most acceptable source among renewable energies for electricity generation. This work proposes an intelligent system to identify incipient faults in the electric generators of wind turbines to improve maintenance routines. Four feature extraction methods were applied to vibration signals, and different classifiers were used to predict the running status of the wind turbine. We correctly identified 94.44% of normal conditions, reducing the false positive and negative rates to 0.4% and 1.84%, respectively; a better result than other approaches already reported in the literature.","PeriodicalId":273028,"journal":{"name":"Journal of Artificial Intelligence and Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"62","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Artificial Intelligence and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33969/AIS.2019.11001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 62
Abstract
The eagerness and necessity to develop so-called smart applications has taken the Internet of Things (IoT) to a whole new level. Industry has been implementing services that use IoT to increase productivity as well as management systems over the past couple of years. Such services are now encroaching on wind energy, which nowadays is the most acceptable source among renewable energies for electricity generation. This work proposes an intelligent system to identify incipient faults in the electric generators of wind turbines to improve maintenance routines. Four feature extraction methods were applied to vibration signals, and different classifiers were used to predict the running status of the wind turbine. We correctly identified 94.44% of normal conditions, reducing the false positive and negative rates to 0.4% and 1.84%, respectively; a better result than other approaches already reported in the literature.
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