Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2019.1654652
An online fault-diagnosis of electromagnetic actuator based on variation characteristics of load current
Xin Cheng
; School of Mechanical & Electronic Engineering, Wuhan University of Technology, Wuhan, People’s Republic of China
Baixin Cheng
; School of Mechanical & Electronic Engineering, Wuhan University of Technology, Wuhan, People’s Republic of China
Meiqian Lu
; School of Mechanical & Electronic Engineering, Wuhan University of Technology, Wuhan, People’s Republic of China
Rougang Zhou
; School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, People’s Republic of China
Lin Zhang
; School of Mechanical & Electronic Engineering, Wuhan University of Technology, Wuhan, People’s Republic of China
Sažetak
Accurate and fast fault-diagnosis is the foundation of fault-tolerance. To develop the fault-tolerance of magnetic-levitated bearing system, this paper presents an online fault-diagnosis approach of electromagnetic actuator based on variation characteristics of sampled load current in the modulation to identify the time constant of the electromagnetic coil, and then to diagnose the broken circuit or partial short-circuit faults. After analysing the variation characteristics of the load current theoretically, the simulation is constructed to verify the effectiveness of the proposed approach. Considering the real-time requirement of fault-diagnosis, we develop a fast sampling and calculating method for the equivalent slope of the load current in the modulation, which represents the variation characteristics of the load current. The experimental results demonstrate that the proposed approach is effective for diagnosing broken circuit and partial short-circuit faults, and the execution time for the fault-diagnosis is about 2 ms, proving its excellent real-time performance.
Ključne riječi
Fault-diagnosis; electromagnetic actuator; variation characteristics of current; magnetic-levitated bearings
Hrčak ID:
239846
URI
Datum izdavanja:
3.12.2019.
Posjeta: 934 *