Approximate entropy as acoustic emission feature parametric data for crack detection

References

• Piotrkowski , R. , Castro , E. and Gallego , A. 2009 . Wavelet power, entropy and bispectrum applied to AE signals for damage identification and evaluation of corroded galvanized steel . Mech. Syst. Signal Process. , 23 ( 2 ) : 432 – 445 .
   Google Scholar
• Yan , R. and Gao , R.X. 2007 . Approximate entropy as a diagnostic tool for machine health monitoring . Mech. Syst. Signal Process. , 21 ( 2 ) : 824 – 839 .
   Web of Science ®Google Scholar
• Hensman , J. , Pullin , R. , Eaton , M. , Worden , K. , K.M. , Holford and Evans , S.L. 2009 . Detecting and identifying artificial acoustic emission signals in an industrial fatigue environment . Meas. Sci. Technol. , 20 ( 4 )
   Google Scholar
• Kappatos , A.V. and Dermatas , S.E. 2007 . Feature extraction for crack detection in rain conditions . J. Nondestruct. Eval. , 26 ( 2–4 ) : 57 – 70 .
   Google Scholar
• Kirikera , G.R. , Shinde , V. , Schulz , M.J. , A. , Ghoshal , M. , Sundaresan and Allemang , R. 2007 . Damage localisation in composite and metallic structures using a structural neural system and simulated acoustic emissions . Mech. Syst. Signal Process. , 21 ( 1 ) : 280 – 297 .
   Google Scholar
• Pincus , S.M. 1991 . “ Approximate entropy as a measure of system complexity ” . In Proc. Nat. Acad. Sci. USA Vol. 88 , 2297 – 2301 .
   Google Scholar
• Oh , G. , Kim , S. and Eom , C. 2007 . Market efficiency in foreign exchange markets . Physica A: Stat. Mech. Appl. , 382 ( 1 ) : 209 – 212 .
   Google Scholar
• Srinivasan , V. , Eswaran , C. and Sriraam , N. 2007 . Approximate entropy-based epileptic EEG detection using artificial neural networks . IEEE Trans. Inf. Technol. Biomed. , 11 ( 3 ) : 288 – 295 .
   PubMedGoogle Scholar
• Xu , Y. , Ma , X. and Ning , X. 2007 . Estimation of ApEn for short-term HFECG based on multi-resolution analysis . Physica A: Stat. Mech. Appl. , 376 ( 1–2 ) : 401 – 408 .
   Google Scholar
• Hornero , R. , Aboy , M. , Abasolo , D. , McNames , J. and Goldstein , B. 2005 . Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension . IEEE Trans. Biomed. Eng. , 52 ( 10 ) : 1671 – 1680 .
   PubMedGoogle Scholar
• Hu , X. , Miller , C. , Vespa , P. and Bergsneider , M. 2008 . Adaptive computation of approximate entropy and its application in integrative analysis of irregularity of heart rate variability and intracranial pressure signals . Med. Eng. Phys. , 30 ( 5 ) : 631 – 639 .
   PubMedGoogle Scholar
• Chesnokov , Y.V. 2008 . Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods . Artif. Intell. Med. , 43 ( 2 ) : 151 – 165 .
   PubMed Web of Science ®Google Scholar
• Goncalves , H. , Rocha , A.P. , Ayres-De-Campos , D. and Bernardes , J. 2006 . Linear and nonlinear fetal heart rate analysis of normal and acidemic fetuses in the minutes preceding delivery . Med. Biol. Eng. Comput. , 44 ( 10 ) : 847 – 855 .
   PubMedGoogle Scholar
• Ferrario , M. , Signorini , M.G. , Magenes , G. and Cerutti , S. 2006 . Comparison of entropy-based regularity estimators: application to the fetal heart rate signal for the identification of fetal distress . IEEE Trans. Biomed. Eng. , 53 ( 1 ) : 119 – 125 .
   PubMedGoogle Scholar
• Abasolo , D. , Escudero , J. , Hornero , R. , Gomez , C. and Espino , P. 2008 . Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer's disease patients . Med. Biol. Eng. Comput. , 46 ( 10 ) : 1019 – 1028 .
   PubMedGoogle Scholar
• Mitrakovic , D. , Grabec , I. and Sedmak , S. 1985 . Simulation of AE signals and signal analysis systems . Ultrasonics , 23 ( 5 ) : 227 – 232 .
   Google Scholar