Journal ArticleDOI
A machine learning approach for condition monitoring of wind turbine blade using autoregressive moving average (ARMA) features through vibration signals: a comparative study
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This article is published in Progress in Industrial Ecology, An International Journal.The article was published on 2018-01-01. It has received 8 citations till now. The article focuses on the topics: Autoregressive–moving-average model & Turbine blade.read more
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Journal ArticleDOI
Bayesian Network Modelling for the Wind Energy Industry: An Overview
TL;DR: The review findings reveal that the applications of BNs in the wind energy industry are quite diverse, ranging from wind power and weather forecasting to risk management, fault diagnosis and prognosis, structural analysis, reliability assessment, and maintenance planning and updating.
Book ChapterDOI
Predicting Wind Turbine Blade Fault Condition to Enhance Wind Energy Harvest Through Classification via Regression Classifier
TL;DR: In this article, a pattern recognition technology is used to predict a different fault condition which happens in wind turbine sharp edge using vibration signals. But, the fault condition is not considered in this paper.
Journal ArticleDOI
Digital Twins-Based Online Monitoring of TFE-731 Turbofan Engine Using Fast Orthogonal Search
TL;DR: In this paper , a fault diagnosis method for the TFE-731 turbofan engine and an online diagnosis system for aircraft engines is presented. But, due to the complicated structure of the aircraft engine, it is hard to observe the damage and determine its status with the traditional diagnosis method, which sets a fixed threshold for some specific parameters.
Journal ArticleDOI
A credal decision tree classifier approach for surface condition monitoring of friction stir weldment through vibration patterns
Joshuva Arockia Dhanraj,Bhavya Lingampalli,Meenakshi Prabhakar,Aravinth Sivakumar,Balachandar Krishnamurthy,Kuppan Chetty Ramanathan +5 more
TL;DR: In this article, the credal decision tree classifier (CDT) was used to perform the classification of the faults like tunnel defect (TD), kissing bonds (KB), root sticking (RS), incomplete fusion (IF), flash (FL), weld root-flaw crack (WC), oxidation (OX) and lack of fill defects (LF) and the prediction accuracy was achieved to be 97.44% with the computational time of 0.46
Journal ArticleDOI
Semi-supervised networks integrated with autoencoder and pseudo-labels propagation for structural condition assessment
TL;DR: Li et al. as discussed by the authors proposed a semi-supervised network for condition assessment integrated with deep autoencoder and pseudo-labels propagation, which achieved a powerful effectiveness and robustness.
References
Related Papers (5)
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