TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publication References
  4. Nonlinear sensor fault diagnosis in wireless sensor networks using structural response data
 
Options

Nonlinear sensor fault diagnosis in wireless sensor networks using structural response data

Publikationstyp
Conference Paper
Date Issued
2016-06
Sprache
English
Author(s)
Dragos, Kosmas  
Smarsly, Kay 
Jahr, Katrin  
TORE-URI
http://hdl.handle.net/11420/10018
Citation
International Workshop of the European Group for Intelligent Computing in Engineering (EG-ICE 2016)
Contribution to Conference
23rd International Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2016  
Scopus ID
2-s2.0-84987732924
This paper introduces a novel approach towards sensor fault diagnosis in wireless structural health monitoring systems. As compared to traditional fault diagnosis approaches, a number of innovations are reported in this paper. First, by embedding fault models and algorithms directly into wireless sensor nodes, sensor faults can be self-detected by the nodes in a distributedcooperative fashion. Second, no redundant sensor installations are required for fault diagnosis, because the wireless sensor nodes exploit the redundant information of correlated sensors already installed in the monitored structure ("analytical redundancy"). Third, instead of using raw time series of sensor data for analytical redundancy, the sensor data is first transformed from the time domain into the frequency domain on-board the sensor nodes, entailing significantly reduced data traffic. Fourth, nonlinearities in the data sets (e.g. due to measurement factors) are handled by implementing the analytical redundancy approach in terms of feedforward backpropagation neural networks embedded into the wireless sensor nodes. Fifth, due to the adaptation abilities of the embedded neural networks, sensor fault diagnosis remains efficient and accurate even if the monitored structure is subject to structural changes (or damage) as reflected in the sensor data. Sixth, no a priori knowledge about the structure or about the sensor instrumentation is required because the neural networks, representing a purely data-driven approach, take previously collected sensor data as the sole basis for fault diagnosis. This paper presents the fault diagnosis methodology, followed by the implementation into a fault-tolerant wireless structural health monitoring system prototype. Validation experiments on a laboratory test structure demonstrate the efficiency and accuracy of the proposed approach.
TUHH
Weiterführende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback