**Deep Learning-Based Machinery Fault Diagnostics**

Editors

**Hongtian Chen Kai Zhong Guangtao Ran Chao Cheng**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Hongtian Chen Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada

Kai Zhong Institutes of Physical Science and Information Technology, Anhui University, Anhui 230601, China

Guangtao Ran Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Chao Cheng School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Machines* (ISSN 2075-1702) (available at: https://www.mdpi.com/journal/machines/special issues/dl faul).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-5173-9 (Hbk) ISBN 978-3-0365-5174-6 (PDF)**

© 2022 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.
