The data contained in pages 1-81 of this application have been submitted in confidence and contain trade secrets or proprietary information, and such data shall be used or disclosed only for evaluation purposes, provided that if this applicant receives an award as a result of or in connection with the submission of this application, DOE shall have the right to use or disclose the data herein to the extent provided in the award. This restriction does not limit the Government's right to use or disclose data obtained without restriction from any source, including the applicant. Fiber-Optic Defect and Damage Locator System for Wind Turbine Blades Intelligent Fiber Optic Systems Corporation (IFOS)
A Lamb wave-based damage identification method called damage imaging method for composite shells is presented. A damage index (DI) is generated from the delay matrix of the Lamb wave response signals, and it is used to indicate the location and approximate area of the damage. A piezoelectric actuator is employed to generate the Lamb waves that are subsequently captured by a fiber Bragg grating (FBG) sensor element array multiplexed in a single fiber connected to a high-speed fiber-optic sensor system. The high-speed sensing is enabled by an innovative parallel-architecture optical interrogation system. The viability of this method is demonstrated by analyzing the numerical and experimental Lamb wave response signals from laminated composite shells. The technique only requires the response signals from the plate after damage, and it is capable of performing near real-time damage identification. This study sheds some light on the application of a Lamb wave-based damage detection algorithm for curved plate/shell-type structures by using the relatively low frequency (around 100 kHz) Lamb wave response and the high-speed FBG sensor system.
Early detection of rail defects can avoid derailments and costly damage to the train and railway infrastructure. Small breaks, cracks or corrugations on the rail can quickly propagate after only a few train cars have passed over it, creating a potential derailment. The current technology makes use of a dedicated instrumented car or a separate railway monitoring vehicle to detect large breaks. These cars are usually equipped with accelerometers mounted on the axle or side frame. The simple detection algorithms use acceleration thresholds which are set at high values to eliminate false positives. As a result, rail surface defects that produce low amplitude acceleration signatures may not be detected, and special track components that produce high amplitude acceleration signatures may be flagged as defects.
This paper presents the results of a feasibility study conducted to develop new and more advanced sensory systems as well as signal processing algorithms capable of detecting various rail surface irregularities. A dynamic wheel-rail interaction model was used to simulate train dynamics as a result of rail defects and to assess the potential of this new technology on rail defect detection. In a future paper, we will present experimental data in support of the proposed model and simulations.
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