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Yunan Yao

As seen in: MDPI
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Articles

A Bearing Fault Diagnosis Model Based on a Simplified Wide Convolutional Neural Network and Random Forrest

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By Qikai Zhang, Yunan Yao, Yage Huang, Yangbowen Liu
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network

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By Hongsen Ou, Yunan Yao, Yi He
| MDPI
1. Introduction With the development of science and technology, the world has entered the era of big data; equipment will generate massive time-series data during operation. Through data change analysis, equipment status changes can be evaluated and predicted, and equipment fault diagnosis and operation and maintenance support can be guided.
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Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder

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By Yi He, Yunan Yao, Hongsen Ou
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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