Journal ArticleDOI
Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification
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TLDR
An effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations, is investigated.About:
This article is published in Signal Processing.The article was published on 2017-01-01. It has received 591 citations till now. The article focuses on the topics: Robustness (computer science) & Deep learning.read more
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Cross-domain intelligent bearing fault diagnosis under class imbalanced samples via transfer residual network augmented with explicit weight self-assignment strategy based on meta data
TL;DR: In this paper , a transfer residual network augmented with explicit weight self-assignment strategy based on meta data (TRN-EWM) is proposed for cross-domain diagnosis using existing diagnosis models.
Reliable Information Exchange in IIoT : Investigation into the Role of Data and Data-Driven Modelling
TL;DR: The concept of Industrial Internet of Things (IIoT) is the tangible building block for the realisation of the fourth industrial revolution and should improve productivity, efficiency and reliabilitations in the industry.
Journal ArticleDOI
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
TL;DR: In this paper , a review of deep learning-based fault detection methods for vibration-based condition monitoring is presented. But, the focus of this paper is on the analysis of the vibration signals.
Journal ArticleDOI
An adversarial model for electromechanical actuator fault diagnosis under nonideal data conditions
Book ChapterDOI
Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0
TL;DR: The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.
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.
Book
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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A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
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Learning long-term dependencies with gradient descent is difficult
TL;DR: This work shows why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases, and exposes a trade-off between efficient learning by gradient descent and latching on information for long periods.