Active Monitoring of Fatigue Crack in the Weld Zone of Bogie Frames Using Ultrasonic Guided Waves
Clicks: 220
ID: 112490
2019
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Steady Performance
30.0
/100
219 views
10 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
The bogie frame is an important structure of railway vehicles, transmitting the traction, braking force, lateral force, and vertical force during the traction operation. With the development of high speeds and heavy loads, the appearance of fatigue cracks in the bogie frames is increasing, which reduces the driving life of railway vehicles and even causes serious traffic accidents. Real-time monitoring on the integrity of the bogie is an inevitable requirement for ensuring the safe operation of railway vehicles. In this paper, ultrasonic guided wave-based active structural health monitoring (SHM) was developed to identify the fatigue crack of the bogie frame. Experiments were conducted on a welded T-shape specimen with a thickness of 12 mm. A total of 10 piezoelectric lead zirconate titanate (PZT) disks were mounted around the weld zone of the specimen, five of which were used as actuators, and the other five were used as sensors. Five-peak modulation narrow-band sine waves were input into the actuators to excite the specimen. From the sensor signals, the advanced damage index (DI) was calculated to identify the propagation of the crack. The experimental results demonstrate that crack damage as small as 2 mm in the weld zone of the bogie frame can be successfully detected. Some practical issues for implementing the SHM in real applications, such as crack quantification and environmental compensation, were also discussed.
Abstract Quality Issue:
This abstract appears to be incomplete or contains metadata (221 words).
Try re-searching for a better abstract.
| Reference Key |
yan2019sensorsactive
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Jiajia Yan;Hashen Jin;Hu Sun;Xinlin Qing;Yan, Jiajia;Jin, Hashen;Sun, Hu;Qing, Xinlin; |
| Journal | sensors |
| Year | 2019 |
| DOI |
10.3390/s19153372
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.