Journal ArticleDOI
Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis.
TL;DR: An improved Constrained Sparse Autoencoder integrated with Correlation Analysis (CA-CSAE) is proposed, and a diagnosis scheme for multiple intermittent faults is formulated, with main strategies designed to improve the diversity and accuracy of SAE feature learning.
Journal ArticleDOI
A novel fusion diagnosis method for rotor system fault based on deep learning and multi-sourced heterogeneous monitoring data
Journal ArticleDOI
Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions
TL;DR: A deep transfer learning method is proposed for rolling bearings fault diagnosis under variable operating conditions that can learn features adaptively from noisy data and increase the accuracy rate by 2%–8% comparing with other models.
Journal ArticleDOI
Multi-label fault diagnosis of rolling bearing based on meta-learning
TL;DR: The experimental results exhibit that the trained MLCML has the capability of learning to learn few-shot fault attributes with outstanding diagnosis accuracy and generalization and can adapt to new fault categories rapidly owing to that only a few samples and update steps are required to fine-tune the network.
Journal ArticleDOI
Compound-Fault Diagnosis of Rotating Machinery: A Fused Imbalance Learning Method
TL;DR: A fused imbalance learning method is proposed in this article exploiting the nonlinear-mapping ability of neural networks to diagnosis compound faults accurately in rotating machinery.
References
More filters
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.
Journal ArticleDOI
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.