Proceedings ArticleDOI
Design and development of a wind turbine test rig for condition monitoring studies
Sailendu Biswal,G. R. Sabareesh +1 more
- pp 891-896
TLDR
In this article, a Bench-Top Test Rigger (BTR) is designed to mimic the operating condition of an actual wind turbine and use it for monitoring its condition so as to diagnose the incipient faults in its critical components using latest machine learning algorithms such as Artificial Neural Network (ANN).Abstract:
Wind energy is an emerging, clean and renewable source of energy. It is estimated that by year 2035, wind energy will be generating more than 25% of the world's electricity according to International Energy Agency (IEA). With the increase in demand for wind energy, its maintenance issues are becoming more prominent. The scheduled maintenance is more economical than unscheduled repair resulting from failure. So a continuous condition monitoring of various critical components like bearings, gearbox, and shafts of wind turbine is essential in order to enable predictive maintenance. 10% of the total failure is contributed by the bearings, shaft and gear box failures, but the downtime is more than 50% of the total downtime. This paper discusses the development of a bench-top test rig which is designed to mimic the operating condition of an actual wind turbine and use it for monitoring its condition so as to diagnose the incipient faults in its critical components using latest machine learning algorithms such as Artificial Neural Network (ANN).read more
Citations
More filters
Journal ArticleDOI
A systematic literature review of machine learning methods applied to predictive maintenance
Thyago Peres Carvalho,Fabrizzio Soares,Fabrizzio Soares,Roberto Vita,Roberto da Piedade Francisco,Joao Pedro Tavares Vieira Basto,Symone Gomes Soares Alcalá +6 more
TL;DR: A systematic literature review of ML methods applied to PdM, showing which are being explored in this field and the performance of the current state-of-the-art ML techniques.
Journal ArticleDOI
Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0
Zeki Murat Cinar,Abubakar Abdussalam Nuhu,Qasim Zeeshan,Orhan Korhan,Mohammed Asmael,Babak Safaei +5 more
TL;DR: This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.
Journal ArticleDOI
A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics
TL;DR: In this paper , the authors proposed a Digital Twin predictive maintenance framework of air handling unit (AHU) to overcome the limitations of facility maintenance management (FMM) systems now in use in buildings.
Journal ArticleDOI
Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
Sara Barja-Martinez,Mònica Aragüés-Peñalba,Íngrid Munné-Collado,Pau Lloret-Gallego,Eduard Bullich-Massagué,Roberto Villafafila-Robles +5 more
TL;DR: A holistic analysis of artificial intelligence applications to distribution networks, ranging from operation, monitoring and maintenance to planning, finds that Reinforcement learning is being widely applied to energy management systems design, although more testing in real environments is needed.
Journal ArticleDOI
Comprehensive fault diagnostics of wind turbine gearbox through adaptive condition monitoring scheme
TL;DR: The integration of multi-sensor information in conjunction with ANFIS as a classification algorithm, owing to its efficiency in predicting every possible detail about the health/condition of the different gearbox components, demonstrates its potential to be used as an adaptive condition monitoring as it.
References
More filters
Journal ArticleDOI
Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
TL;DR: In this article, a mathematical analysis to select the most significant intrinsic mode functions (IMFs) is presented, and the chosen features are used to train an artificial neural network (ANN) to classify bearing defects.
Journal ArticleDOI
A new wind turbine fault diagnosis method based on the local mean decomposition
TL;DR: In this article, the authors proposed a novel wind turbine fault diagnosis method based on the local mean decomposition (LMD) technology, which is suitable for obtaining instantaneous frequencies in wind turbine condition monitoring and fault diagnosis.
Journal ArticleDOI
Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine
TL;DR: This paper addresses the feature selection process using decision tree and uses kernel based neighborhood score multi-class support vector machine (MSVM) for classification and the results of MSVM are compared with and binary support vectors machine (SVM).
Journal ArticleDOI
Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring
TL;DR: In this paper, a review of current progress in condition monitoring of wind turbine gearboxes and generators is presented, as an input to the design of a new continuous condition monitoring system with automated warnings based on a combination of vibrational and acoustic emission (AE) analysis.
Journal ArticleDOI
Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM
TL;DR: A wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree Support Vector Machines (SVM) can effectively extract features from nonstationary signals, and can obtain excellent results despite of less training samples.