Integrated ARMA model method for damage detection of subsea pipeline system

An integrated ARMA model algorithm is developed in this study for the structural health monitoring (SHM) of subsea pipeline system. In which, the partition and normalization procedure is firstly employed in signal pre-processing to remove the influence of various loading conditions, the auto-correlation function of the normalized signal is utilized as a substitute of analysis input to overcome noise effect and avoid the bias in Autoregressive Moving Average (ARMA) model fitting caused by noise disturbance as well. Then, Partial Auto-correlation Function (PAF) method is employed in building optimal ARMA model. With which Autoregressive (AR) parameters serving as damage feature vector, a damage indicator (DI) based on the Mahalanobis distance between ARMA models is defined for damage detection and localization. Dynamic vibration of subsea pipeline system under ambient excitations is numerically simulated in ANSYS software and the acceleration responses of pipeline in various damage cases are analyzed utilizing the proposed integrated method. In numerical study, the finite element (FE) model of a subsea soil-pipeline-fluid system is developed and the undersea hydrodynamic force acting on the pipeline is derived based on Spectral Analysis Method. Finally, the proposed method is proved to be robust and very sensitive to damage. It provides accurate identification of damage existence and damage locations with high time efficiency. Therefore, it can be used for the online SHM of subsea pipeline structures and other civil structures.

[1]  M. S. Kirkgöz,et al.  PARTICLE VELOCITY PREDICTION AT THE TRANSFORMATION POINT OF PLUNGING BREAKERS , 1986 .

[2]  James M. W. Brownjohn,et al.  ARMA modelled time-series classification for structural health monitoring of civil infrastructure , 2008 .

[3]  Tomonori Nagayama,et al.  Decentralized damage identification using wavelet signal analysis embedded on wireless smart sensors , 2011 .

[4]  K. Krishnan Nair,et al.  Time Series Based Structural Damage Detection Algorithm Using Gaussian Mixtures , 2007 .

[5]  E. V. Seymour,et al.  Design And Installation Of The North Rankin Trunkline And Slugcatcher , 1984 .

[6]  L. Auersch.  Dynamic interaction of various beams with the underlying soil – finite and infinite, half-space and Winkler models , 2008 .

[7]  M. M. Reda Taha,et al.  Damage identification for structural health monitoring using fuzzy pattern recognition , 2005 .

[8]  L. Borgman,et al.  Ocean wave simulation for engineering design , 1967 .

[9]  J. R. Scott,et al.  A Sea Spectrum for Model Tests and Long-Term Ship Prediction , 1965 .

[10]  R. Bhattacharyya.  Dynamics of Marine Vehicles , 1978 .

[11]  Gaëtan Kerschen,et al.  Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis , 2005 .

[12]  Xinqun Zhu,et al.  Time-varying system identification using a newly improved HHT algorithm , 2009 .

[13]  Hani G. Melhem,et al.  Damage detection of structures by wavelet analysis , 2004 .

[14]  Robert G. Dean,et al.  Intercomparison of near-bottom kinematics by several wave theories and field and laboratory data , 1986 .

[15]  Wei-Xin Ren,et al.  Structural damage identification by using wavelet entropy , 2008 .

[16]  Keith Worden,et al.  DAMAGE DETECTION USING OUTLIER ANALYSIS , 2000 .

[17]  Zengrong Wang,et al.  Structural damage detection using autoregressive-model-incorporating multivariate exponentially weighted moving average control chart , 2009 .

[18]  Mark Randolph,et al.  MODELING OF SHALLOWLY EMBEDDED OFFSHORE PIPELINES IN CALCAREOUS SAND , 2002 .

[19]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[20]  S. Fassois,et al.  Recursive identification of vibrating structures from noise-corrupted observations , 1991 .

[21]  O. S. Salawu.  Detection of structural damage through changes in frequency: a review , 1997 .

[22]  Wei-Xin Ren,et al.  Wavelet packet based damage identification of beam structures , 2005 .

[23]  Chih-Chen Chang,et al.  Structural Damage Assessment Based on Wavelet Packet Transform , 2002 .

[24]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[25]  Klaus Hasselmann,et al.  A parametric wave prediction model , 1976 .

[26]  Xinqun Zhu,et al.  Dynamic assessment of underwater pipeline systems using statistical model updating , 2008 .

[27]  V. L. Hill,et al.  Assuring the integrity of natural gas transmission pipelines. Topical report, November 1992 , 1992 .

[28]  T. Barnett,et al.  Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP) , 1973 .

[29]  Nicholas A J Lieven,et al.  An Introduction to Damage Prognosis , 2005 .

[30]  Norris Stubbs,et al.  Damage identification in beam-type structures: frequency-based method vs mode-shape-based method , 2003 .

[31]  Mustafa Gul,et al.  Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications , 2009 .