A feature extraction and machine learning framework for bearing fault diagnosis

TL;DR: This work comprehensively explores various ML techniques and algorithms in the context of Renewable Energy Systems (RES) to contribute to developing environmentally sustainable energy systems.

TL;DR: In this paper , a one-dimensional deep convolutional neural network (1D-DCNN) was proposed to learn features directly from the vibrational signals and identify the gear fault under different health conditions.

TL;DR: This study systematically reviews machine learning applications in health monitoring of renewable energy systems, comparing ML algorithms and extracting trends, challenges, and prospects for photovoltaic and wind power systems defect identification and fault diagnosis.

TL;DR: In this paper, variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available, such as t...

TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.

TL;DR: This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications.

TL;DR: A review of the state-of-the-art in the condition monitoring of wind turbines can be found in this article, which describes the different maintenance strategies, condition monitoring techniques and methods, and highlights in a table the various combinations of these that have been reported in the literature.