Progressive Fault Identification and Prognosis of Railway Tracks Based on Intelligent Inference

Details

• Creators:
  Tang, Jiong ; Zhang, Yang ; Wang, Ting
• Corporate Creators:
  University of Connecticut ; University of Maine. Transportation Infrastructure Durability Center (TIDC)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Analysis Damage Location Damage Severity Data Analysis Electro-mechanical Coupling Energy Energy Harvesting Impedance Measurement Inverse Analysis Measurement Mechanical Impedance Mechatronics Multi-objective Optimization Optimization Performance Piezoelectric Transducers Sensors Statistical Inference Structural Analysis Structural Damage Identification

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• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  University of Maine. Transportation Infrastructure Durability Center (TIDC)
• Abstract:
  In this project we explore and develop a new damage detection and identification approach suitable for elastic structure which is built upon piezoelectric active interrogation and intelligent data analytics. Novel sensor designs have been accomplished to effectively extract the impedance information of the underlying structure, the change of which provides the input information for the subsequent damage identification analysis. A series of highly accurate and robust damage identification algorithms built upon multi-objective optimization and Bayesian inference is then formulated to identify the location and severity of damage. During the investigation, new energy harvesting scheme that can enhance the electromechanical coupling of the transducer is synthesized, which can lead to enlarged actuation/sensing range. Comprehensive experimental investigations are conducted to validate the sensor prototype design as well as the algorithmic advancements. Throughout the research, industrial insights are adopted to improve the system performance. This research leads to a new structural fault identification methodology that has potential to be applied to large scale infrastructure, including but not limited no railway tracks.
• Format:
  PDF
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:faf2ba35fa3c9447924fd1ab0b2b89f1a8e3aca783dc1f104998246d640988794e424fc646314b4426f6edded186fc9a6a19d1ecde2265fc00bfbd7b01d5e466
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88709/dot_88709_DS1.pdf
• File Type:
  [PDF - 1.63 MB ]