Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

Applying Radial Basis Function Networks to Fault Diagnosis of Motorized Spindle

Authors
Zhe Li, Kesheng Wang, Jinghui Yang, Yavor Stefanov
Corresponding Author
Zhe Li
Available Online November 2016.
DOI
https://doi.org/10.2991/iwama-16.2016.44How to use a DOI?
Keywords
motorized spindle; Radial Basis Function; fault diagnosis
Abstract
In a motorized spindle, due to the complexity of the system and nonlinear relationship between features and types of faults, it is difficult and inefficient to use traditional methods or physical models for the fault diagnosis. This paper focuses on the research on applying Radial Basis Function (RBF) Networks for fault detection and classification in the motorized spindle. As a data driven model with high efficiency, RBF networks has the advantage solving the nonlinear problems and dealing with the contradictory samples in the training process. In this research, the data, including rotating speed, temperature, and acceleration signals with three axes (X, Y and Z), are collected from a dynamic balancing platform to evaluate the working condition and detect the potential faults of the motorized spindle.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
6th International Workshop of Advanced Manufacturing and Automation
Part of series
Advances in Economics, Business and Management Research
Publication Date
November 2016
ISBN
978-94-6252-243-5
ISSN
2352-5428
DOI
https://doi.org/10.2991/iwama-16.2016.44How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhe Li
AU  - Kesheng Wang
AU  - Jinghui Yang
AU  - Yavor Stefanov
PY  - 2016/11
DA  - 2016/11
TI  - Applying Radial Basis Function Networks to Fault Diagnosis of Motorized Spindle
BT  - 6th International Workshop of Advanced Manufacturing and Automation
PB  - Atlantis Press
SN  - 2352-5428
UR  - https://doi.org/10.2991/iwama-16.2016.44
DO  - https://doi.org/10.2991/iwama-16.2016.44
ID  - Li2016/11
ER  -