Journal ArticleDOI
Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet.
TLDR
A novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed and applied to the fault diagnosis of rolling bearings, confirming that the proposed method is more effective than the existing methods.Abstract:
Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT. An adaptive DBN is constructed to improve the convergence rate and identification accuracy with multiple stacked adaptive restricted Boltzmann machines (RBMs). The proposed method is applied to the fault diagnosis of rolling bearings. The results confirm that the proposed method is more effective than the existing methods.read more
Citations
More filters
Journal ArticleDOI
Applications of machine learning to machine fault diagnosis: A review and roadmap
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
Journal ArticleDOI
A survey on Deep Learning based bearing fault diagnosis
Duy-Tang Hoang,Hee-Jun Kang +1 more
TL;DR: The three popular Deep Learning algorithms for Bearing fault diagnosis including Autoencoder, Restricted Boltzmann Machine, and Convolutional Neural Network are briefly introduced and their applications are reviewed through publications and research works on the area of bearing fault diagnosis.
Journal ArticleDOI
Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network
TL;DR: A novel convolutional deep belief network (CDBN) is proposed for bearing fault diagnosis with an auto-encoder used to compress data and reduce the dimension and exponential moving average is employed to improve the performance of the constructed deep model.
Journal ArticleDOI
Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application.
TL;DR: Wang et al. as mentioned in this paper proposed a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, by extending the marginal distribution adaptation to joint distribution adaptation (JDA).
Journal ArticleDOI
Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review
TL;DR: A brief review of conventional ML methods is provided, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications and many new functionalities enabled by DL techniques are also summarized.
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
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.