Name
Abstract | ||
|---|---|---|
This paper discusses a method for the application of acoustic emission (AE) techniques for detecting and monitoring wheel flattening issues for the rail industry. We propose a novel wheel flattening detection system based on Parametric Constraint Optimization methods, which can easily be adapted to various remote monitoring systems. We introduce a novel "defect score curve" to identify wheel flattening defects with a computationally efficient and effective method. The defect condition is identified by comparing the measured defect score curve with the threshold curve that is defined by desired detection and false alarm rates. To analyze the performance of our proposed method, we perform several field tests for various wheel flattening conditions and train speeds. The flat faults were successfully detected during the field tests proving the capability of the system for detecting various types of faults. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/PIMRC.2018.8580762 | 2018 IEEE 29TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC) |
Field | DocType | Citations |
Microsoft Windows,Flattening,False alarm,Monitoring system,Computer science,Effective method,Real-time computing,Parametric statistics,Acoustic emission,Constrained optimization | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aktas, Metin | 1 | 4 | 3.44 |
| Ethem Hakan Gunel | 2 | 0 | 0.34 |
| Pinar Yilmazer | 3 | 0 | 0.34 |
| Toygar Akgun | 4 | 90 | 9.39 |