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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Predictive anomaly detection for marine diesel engine based on echo state network and autoencoder
TL;DR: Wang et al. as discussed by the authors proposed a predictive anomaly detection method based on echo state network (ESN) and deep autoencoder, which is used to improve the operation reliability of marine diesel engine.
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An optimal method based on HOG-SVM for fault detection
Panfeng Xu,Lidong Huang,Yan Song +2 more
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Multi-level features fusion network-based feature learning for machinery fault diagnosis
TL;DR: In this article , a multi-scale convolutional neural network (MLFNet) was proposed for feature learning of vibration signals for bearing defect detection. But the performance of the proposed model was not as good as the state-of-the-art methods.
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Early Warning of Critical Blockage in Coal Mills Based on Stacked Denoising Autoencoders
TL;DR: The results demonstrated that the proposed method can effectively detect critical blockage in a coal mill and issue a timely warning, which allows operators to detect potential faults.
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Monitoring multi-domain batch process state based on fuzzy broad learning system
TL;DR: In this article, a novel monitoring approach based on fuzzy broad learning system (FBLS) is employed to address the aforementioned issues, and the results demonstrate that FBLS can better capture the fuzzified feature and quickly update monitoring model to accomplish the self-increase of fault database without retraining the entire process.
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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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.