Journal ArticleDOI
Bearing remaining useful life prediction based on deep autoencoder and deep neural networks
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TLDR
A novel eigenvector based on time–frequency-wavelet joint features is proposed to effectively represent bearing degradation process and a deep autoencoder based joint features compression and computing method is presented to retain effective information without increasing the scale of DNN.About:
This article is published in Journal of Manufacturing Systems.The article was published on 2018-07-01. It has received 250 citations till now. The article focuses on the topics: Autoencoder & Prognostics.read more
Citations
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Journal ArticleDOI
Predictive maintenance in the Industry 4.0: A systematic literature review
Tiago Zonta,Cristiano André da Costa,Rodrigo da Rosa Righi,Miromar Jose de Lima,Eduardo Silveira da Trindade,Guann-Pyng Li +5 more
TL;DR: It was concluded that computer science, including artificial intelligence and distributed computing fields, is more and more present in an area where engineering was the dominant expertise, so detaching the importance of a multidisciplinary approach to address Industry 4.0 effectively.
Journal ArticleDOI
Deep separable convolutional network for remaining useful life prediction of machinery
TL;DR: The experimental results show that the proposed deep separable convolutional network (DSCN) is able to provide accurate RUL prediction results based on the raw multi-sensor data and is superior to some existing data-driven prognostics approaches.
Journal ArticleDOI
A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction
Jialin Li,Xueyi Li,David He +2 more
TL;DR: A directed acyclic graph (DAG) network that combines long short term memory (LSTM) and a convolutional neural network (CNN) to predict the RUL to improve the prognostic accuracy of the network.
Journal ArticleDOI
A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions
Jun Zhu,Nan Chen,Changqing Shen +2 more
TL;DR: Wang et al. as discussed by the authors proposed a transfer learning method based on multiple layer perceptron (MLP) to solve distribution discrepancy problem, which can detect FOT adaptively, at the same time provide reliable transferable prognostics performance under different working conditions.
Journal ArticleDOI
Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty
TL;DR: Inspired by the idea of Bayesian machine learning, a Bayesian deep-learning-based (BDL-based) method is proposed in this paper for health prognostics with uncertainty quantification, and a variational-inference-based method is presented for the BNNs learning and inference.
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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Introduction to Fourier Optics
Joseph W. Goodman,Mary E. Cox +1 more
TL;DR: The second edition of this respected text considerably expands the original and reflects the tremendous advances made in the discipline since 1968 as discussed by the authors, with a special emphasis on applications to diffraction, imaging, optical data processing, and holography.
Book
Introduction to Fourier optics
TL;DR: The second edition of this respected text considerably expands the original and reflects the tremendous advances made in the discipline since 1968 as discussed by the authors, with a special emphasis on applications to diffraction, imaging, optical data processing, and holography.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising 1 criterion
TL;DR: This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.
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