/pdf/a-perspective-survey-on-deep-transfer-learning-for-fault-m5e3nrkv.pdf

A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

pdf/a-comprehensive-review-on-convolutional-neural-network-in-1f58bn8ncl.pdf

A comprehensive review on convolutional neural network in machine fault diagnosis

A review of the application of deep learning in intelligent fault diagnosis of rotating machinery

Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework

With the development of modern Intelligent Transportation System (ITS), reliable and efficient transportation information sharing becomes more and more important. Although there are promising wireless communication schemes such as Vehicle-to-Everything (V2X) communication standards, information sharing in ITS still faces challenges such as the V2X communication overload when a large number of vehicles suddenly appeared in one area. This flash crowd situation is mainly due to the uncertainty of traffic especially in the urban areas during traffic rush hours and will significantly increase the V2X communication latency. In order to solve such flash crowd issues, we propose a novel system that can accurately predict the traffic flow and density in the urban area that can be used to avoid the V2X communication flash crowd situation. By combining the existing grid-based and graph-based traffic flow prediction methods, we use a Topological Graph Convolutional Network (ToGCN) followed with a Sequence-to-sequence (Seq2Seq) framework to predict future traffic flow and density with temporal correlations. The experimentation on a real-world taxi trajectory traffic data set is performed and the evaluation results prove the effectiveness of our method.

Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction

Data-driven fault diagnosis is essential for the reliability and safety of industry equipment. However, the lack of real labeled fault data make the machine learning-based diagnosis methods difficult to carry out. To solve this problem, this article proposes a new fault diagnosis framework called multilabel one-dimensional (1-D) generation adversarial network (ML1-D-GAN). In our method, Auxiliary Classifier GAN is utilized first for real damage data generation. Then the generated and real damage data are both used to train the fault classifier. Experimental results reveal that the generated data is applicable, and ML1-D-GAN improves the diagnosing accuracy for real bearing faults from 95% to 98% when trained with the generated data. The scalability of the learning model is also proven in the experiment.

Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network

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Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network