Journal ArticleDOI
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
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TLDR
A novel deep autoencoder feature learning method is developed to diagnose rotating machinery fault and the results confirm that the proposed method is more effective and robust than other methods.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2017-10-01. It has received 481 citations till now. The article focuses on the topics: Autoencoder & Feature learning.read more
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Deep learning and its applications to machine health monitoring
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.
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Applications of machine learning to machine fault diagnosis: A review and roadmap
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
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Digital Twin: Values, Challenges and Enablers From a Modeling Perspective
TL;DR: This work reviews the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.
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Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
TL;DR: By analyzing the kernels of the convolutional layers of DNCNN via NAM algorithm, it is found that these kernels act as filters and they become complex when the layers go deeper, which may help to understand what DNCNN has learned in intelligent fault diagnosis of machinery.
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A survey on Deep Learning based bearing fault diagnosis
Duy-Tang Hoang,Hee-Jun Kang +1 more
TL;DR: The three popular Deep Learning algorithms for Bearing fault diagnosis including Autoencoder, Restricted Boltzmann Machine, and Convolutional Neural Network are briefly introduced and their applications are reviewed through publications and research works on the area of bearing fault diagnosis.
References
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
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Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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Representation Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.