Applying Radial Basis Function Networks to Fault Diagnosis of Motorized Spindle
Zhe Li, Kesheng Wang, Jinghui Yang, Yavor Stefanov
Available Online November 2016.
- https://doi.org/10.2991/iwama-16.2016.44How to use a DOI?
- motorized spindle; Radial Basis Function; fault diagnosis
- 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.
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 -