Power Plants Failure Reports Analysis for Predictive Maintenance

The shifting from reactive to predictive maintenance heavily improves the assets management, especially for complex systems with high business value. This occurs in particular in power plants, whose functioning is a mission-critical task. In this work, an NLP-based analysis of failure reports in power plants is presented, showing how they can be effectively used to implement a predictive maintenance aiming to reduce unplanned downtime and repair time, thus increasing operational efficiency while reducing costs.

[1]  L. Bertling,et al.  Reliability-Centered Maintenance for Wind Turbines Based on Statistical Analysis and Practical Experience , 2012, IEEE Transactions on Energy Conversion.

[2]  Vincenza Carchiolo,et al.  Personal Health Record feeding via Medical Forums , 2015, 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[3]  Daoud Ait Kadi,et al.  A STATE-OF-THE-ART REVIEW OF FMEA/FMECA , 1994 .

[4]  Vincenza Carchiolo,et al.  Using Twitter Data and Sentiment Analysis to Study Diseases Dynamics , 2015, ITBAM.

[5]  S. Iniyan,et al.  Performance, reliability and failure analysis of wind farm in a developing Country , 2010 .

[6]  Yingning Qiu,et al.  Wind turbine SCADA alarm analysis for improving reliability , 2012 .

[7]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .

[8]  Vincenza Carchiolo,et al.  Multisource agent-based healthcare data gathering , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[9]  Yang Xin,et al.  Research on a knowledge modelling methodology for fault diagnosis of machine tools based on formal semantics , 2017, Adv. Eng. Informatics.

[10]  Sungjoo Lee,et al.  Patterns of technological innovation and evolution in the energy sector: A patent-based approach , 2013 .

[11]  Klaus-Dieter Thoben,et al.  Big Data Analytics in the Maintenance of Off-Shore Wind Turbines: A Study on Data Characteristics , 2016, LDIC.

[12]  Bin Wang,et al.  Condition monitoring of a wind turbine drive train based on its power dependant vibrations , 2017, Renewable Energy.

[13]  G. Mangioni,et al.  Tourism Websites Network: Crawling the Italian Webspace , 2016 .

[14]  Sule Selcuk,et al.  Predictive maintenance, its implementation and latest trends , 2017 .

[15]  Georg Helbing,et al.  Deep Learning for fault detection in wind turbines , 2018, Renewable and Sustainable Energy Reviews.

[16]  Mayorkinos Papaelias,et al.  Condition monitoring of wind turbines: Techniques and methods , 2012 .

[17]  Xu Chi,et al.  Text mining analysis of wind turbine accidents: An ontology-based framework , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[18]  Alexander Jung,et al.  PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING , 2018, 2018 IEEE Data Science Workshop (DSW).

[19]  Guolin He,et al.  A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique , 2016 .

[20]  Stefan Wagner Natural language processing is no free lunch , 2016, Perspectives on Data Science for Software Engineering.

[21]  M. Sayed-Mouchaweh,et al.  Fault Diagnosis Methods for Wind Turbines Health Monitoring : a Review , 2014 .

[22]  Dilek Küçük,et al.  Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles , 2014, ArXiv.

[23]  Nadège Bouchonneau,et al.  A review of wind turbine bearing condition monitoring: State of the art and challenges , 2016 .