rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Structural Health Monitoring
    sub title: An Advanced Signal Processing Perspective
  name:
    name part: Yan, Ruqiang.
    role:
      role term: editor.
  name:
    name part: Chen, Xuefeng.
    role:
      role term: editor.
  name:
    name part: Mukhopadhyay, Subhas Chandra.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XI, 375 p. 284 illus., 175 illus. in color. online resource.
  abstract: This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on the utilization of advanced signal processing techniques to help monitor the health status of critical structures and machines encountered in our daily lives: wind turbines, gas turbines, machine tools, etc. As such, it offers a key reference guide for researchers, graduate students, and industry professionals who work in the field of SHM.
  table of contents: Advanced Signal Processing for Structural Health Monitoring -- Signal Post-Processing for Accurate Evaluation of the Natural Frequencies -- Holobalancing Method and its Improvement by Reselection of Balancing Object -- Wavelet Transform Based On Inner Product for Fault Diagnosis of Rotating Machinery -- Wavelet Based Spectral Kurtosis and Kurtogram: A Smart and Sparse Characterization of Impulsive Transient Vibration -- Time-Frequency Manifold for Machinery Fault Diagnosis -- Matching Demodulation Transform and its Application in Machine Fault Diagnosis -- Compressive Sensing: A New Insight to Condition Monitoring of Rotary Machinery -- Sparse Representation of the Transients in Mechanical Signals -- Fault Diagnosis of Rotating Machinery Based on Empirical Mode Decomposition -- Bivariate Empirical Mode Decomposition and Its Applications in Machine Condition Monitoring -- Time-Frequency Demodulation Analysis Based on LMD and Its Applications -- On The Use of Stochastic Resonance in Mechanical Fault Signal Detection.
  note: edited by Ruqiang Yan, Xuefeng Chen, Subhas Chandra Mukhopadhyay.
  subject:
    topic: Engineering
  subject:
    topic: Physical measurements
  subject:
    topic: Measurement
  subject:
    topic: Industrial engineering
  subject:
    topic: Production engineering
  subject:
    topic: Biomedical engineering
  subject:
    topic: Engineering
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Biomedical Engineering
  subject:
    topic: Industrial and Production Engineering
  subject:
    topic: Measurement Science and Instrumentation
  classification: TK5102.9
  classification: TA1637-1638
  classification: TK7882.S65
  classification: 621.382
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Smart Sensors, Measurement and Instrumentation, 26
  identifier: 9783319561264
  identifier: http://dx.doi.org/10.1007/978-3-319-56126-4
  record info:
    record creation date: 170429
    record change date: 20170429051603.0
    record identifier: 978-3-319-56126-4