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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Journal ArticleDOI
A novel attention-based domain adaptation model for intelligent bearing fault diagnosis under variable working conditions
Proceedings ArticleDOI
Multi-source Unsupervised Domain Adaptation for Machinery Fault Diagnosis under Different Working Conditions
TL;DR: In this paper, a multi-source domain adaptation framework is proposed for cross-domain fault diagnosis under different working conditions, where multiple specific feature spaces are obtained, then the distributions of each pair of source and target domains are aligned since it is difficult to extract the common domain-invariant features for all domains.
Journal ArticleDOI
BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions
TL;DR: In this article , a novel fault diagnosis framework is proposed to improve the efficiency of fault classification by using variational modal decomposition (VMD) to expand the features of the fault signal and principal component analysis (PCA) to select the most representative fault features.
Proceedings ArticleDOI
Integrated IPC for data-driven fault detection
TL;DR: This study presents a novel and rapid fault detection methodology using an integrated Industrial PC based on integrating existing hardware components and software libraries for efficient application of machine learning algorithms to an industrial process.
Journal ArticleDOI
CLUE-AI: A Convolutional Three-Stream Anomaly Identification Framework for Robot Manipulation
Dogan Altan,Sanem Sariel +1 more
TL;DR: A novel three-stream framework design that fuses visual, auditory and proprioceptive data streams to identify everyday object manipulation anomalies and achieves an f-score of 94% outperforming the other baselines in classifying anomalies that arise during runtime.
References
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Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.