Special Issue on Machine Condition Monitoring and Fault Diagnosis: From Theory to Application-Reference-Cited by-同舟云学术

Special Issue on Machine Condition Monitoring and Fault Diagnosis: From Theory to Application

Published:2023-10-22 Issue:20 Volume:13 Page:11550
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Cong Feiyun¹

Affiliation:

1. State Key Laboratory of Fluid Power & Mechatronic System, Zhejiang University, Hangzhou 310027, China

Abstract

Modern machines are becoming more complex in structure and are operating under harsher loading and operational conditions [...]

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/20/11550/pdf

Reference6 articles.

1. Lee, I., Park, H.J., Jang, J.-W., Kim, C.-W., and Choi, J.-H. (2023). System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes. Appl. Sci., 13.

2. Jankowska, K., Dybkowski, M., Petro, V., and Kyslan, K. (2023). Classification of Speed Sensor Faults Based on Shallow Neural Networks. Appl. Sci., 13.

3. Chen, L., Wei, L., Li, W., Wang, J., and Han, D. (2023). Fault Prediction of Mechanical Equipment Based on Hilbert–Full-Vector Spectrum and TCDAN. Appl. Sci., 13.

4. Gu, Y., Zhang, Y., Yang, M., and Li, C. (2023). Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information. Appl. Sci., 13.

5. Ye, Q., and Liu, C. (2023). Simultaneous Fault Diagnosis Based on Hierarchical Multi-Label Classification and Sparse Bayesian Extreme Learning Machine. Appl. Sci., 13.

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