Proof of concept of impact detection in composites using fiber bragg grating arrays

Shajihan SA, Mechitov K, Spencer BF. An Event-Classification Neural Network Approach for Rapid Railroad Bridge Impact Detection

Railroads are a critical part of the United States' transportation sector. Over 40 percent (by weight) of the nation's freight is transported by rail, and according to the Bureau of Transportation statistics, railroads moved $186.5 billion of freight in 2021. A vital part of the freight network is railroad bridges, with a good number being low-clearance bridges that are prone to impacts from over-height vehicles; such impacts can cause damage to the bridge and lead to unwanted interruption in its usage. Therefore, the detection of impacts from over-height vehicles is critical for the safe operation and maintenance of railroad bridges. While some previous studies have been published regarding bridge impact detection, most approaches utilize more expensive wired sensors, as well as relying on simple threshold-based detection. The challenge is that the use of vibration thresholds may not accurately distinguish between impacts and other events, such as a common train crossing. In this paper, a machine learning approach is developed for accurate impact detection using event-triggered wireless sensors. The neural network is trained with key features which are extracted from event responses collected from two instrumented railroad bridges. The trained model classifies events as impacts, train crossings, or other events. An average classification accuracy of 98.67% is obtained from cross-validation, while the false positive rate is minimal. Finally, a framework for edge classification of events is also proposed and demonstrated using an edge device.

Ultrasonic Structural Health Monitoring Using Fiber Bragg Grating

The fiber Bragg grating (FBG) sensor, which was developed over recent decades, has been widely used to measure manifold static measurands in a variety of industrial sectors. Multiple experiments have demonstrated its ability in ultrasonic detection and its potential in ultrasonic structural health monitoring. Unlike static measurements, ultrasonic detection requires a higher sensitivity and broader bandwidth to ensure the fidelity of the ultrasonic Lamb wave that propagates in a plate-like structure for the subsequent waveform analysis. Thus, the FBG sensor head and its corresponding demodulation system need to be carefully designed, and other practical issues, such as the installation methods and data process methods, should also be properly addressed. In this review, the mature techniques of FBG-based ultrasonic sensors and their practical applications in ultrasonic structural health monitoring are discussed. In addition, state-of-the-art techniques are introduced to fully present the current developments.

Optical Fiber Sensors for Aircraft Structural Health Monitoring

Aircraft structures require periodic and scheduled inspection and maintenance operations due to their special operating conditions and the principles of design employed to develop them. Therefore, structural health monitoring has a great potential to reduce the costs related to these operations. Optical fiber sensors applied to the monitoring of aircraft structures provide some advantages over traditional sensors. Several practical applications for structures and engines we have been working on are reported in this article. Fiber Bragg gratings have been analyzed in detail, because they have proved to constitute the most promising technology in this field, and two different alternatives for strain measurements are also described. With regard to engine condition evaluation, we present some results obtained with a reflected intensity-modulated optical fiber sensor for tip clearance and tip timing measurements in a turbine assembled in a wind tunnel.