Vibration-based damage detection techniques are widely applied in the structural health monitoring of offshore platforms. This study employs a parallel multi-scale convolutional neural network (PMSCNN) to analyze the acceleration response signals collected from offshore platforms, achieving the localization of fatigue cracks. The effectiveness of the proposed method is validated through numerical simulations of jacket-type offshore platforms subjected to random wave excitations from different directions. The study focuses on identifying fatigue crack damage in platform components, addressing both single and multiple damage scenarios involving small cracks. To assess the robustness of the method against noise, Gaussian white noise of varying intensities was added to the collected signals. The results demonstrate that the proposed approach effectively identifies and locates fatigue cracks in jacket-type offshore platforms, exhibiting strong noise resistance.
XingxianBTongxuanFChenS, et al. One-dimensional convolutional neural network for damage detection of jacket-type offshore platforms. Ocean Eng2020 (prepublish): 108293.
2.
AghaeidoostVAfsharNZiaie TajaddodB, et al. Damage detection in jacket-type offshore platforms via generalized flexibility matrix and optimal genetic algorithm (GFM-OGA).Ocean Eng2023; 281: 114841.
3.
WangMIncecikATianZ, et al. Structural health monitoring on offshore jacket platforms using a novel ensemble deep learning model. Ocean Eng2024; 301: 117510.
4.
MengmengWAtillaIShizheF, et al. Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement. Reliab Eng Sys Saf2023; 237: 109336.
5.
ChengJLYongJMHengZF, et al. Modal Parameter identification method of jacket platform structure based on AFDD and optimized FBFFT. China Ocean Eng2023; 37(3): 393–407.
6.
LiWHuangY. A combined method of cross-correlation and PCA-based outlier algorithm for detecting structural damages on a jacket oil platform under random wave excitations. Appl Ocean Res2020; 102: 102301.
7.
BartlomiejBYonghuiABillieF, et al. Axial strain accelerations approach for damage localization in statically determinate truss structures. Comp Aided Civ Infrastruct Eng2017; 32(4): 304–318.
8.
JiaxuanLAtillaIMengmengW, et al. Damage detection of offshore jacket structures using structural vibration measurements: application of a new hybrid machine learning method. Ocean Eng2023; 288(P2): 116078.
9.
HuangQGardoniPHurlebausS. A probabilistic damage detection approach using vibration-based nondestructive testing. Struct Safety2012; 38: 11–21.
10.
EbrahimianHAstrozaRContePJ, et al. Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation. Mech Sys Signal Proc2017; 84: 194–222.
11.
FathiAEsfandiariAFadavieM, et al. Damage detection in an offshore platform using incomplete noisy FRF data by a novel Bayesian model updating method. Ocean Eng2020; 217: 108023.
12.
GuJFGulMWuXG. Damage detection under varying temperature using artificial neural networks. Struct Cont Health Monit2017; 24(11): e1998.
13.
EftekharARagehALinzellD. Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition (Article). Struct Cont Health Monit2019; 26(2): e2288.
14.
TamTDinh-CongDJaehongL, et al. An effective deep feedforward neural networks (DFNN) method for damage identification of truss structures using noisy incomplete modal data. J Build Eng2020; 30: 101244.
15.
MehdiKMojtabaSMohammadrezaM, et al. Damage detection in power transmission towers using machine learning algorithms. Structures2023; 56: 104980.
16.
KhanAMKimYChooJ. Intelligent fault detection using raw vibration signals via dilated convolutional neural networks. J Supercomp2018; 76(10): 1–15.
17.
SeventekidisPGiagopoulosD. A combined finite element and hierarchical Deep learning approach for structural health monitoring: test on a pin-joint composite truss structure. Mech Sys Sig Proc2021; 157: 107735.
18.
SonglingXYidanSTengS, et al. Deep reference autoencoder convolutional neural network for damage identification in parallel steel wire cables. Structures2023; 57: 105316.
19.
SaraZArminAHosseinR, et al. Damage identification in steel girders of highway bridges utilizing vibration based methods and convolution neural network in the presence of noise. J Nondest Eval2024; 43(2): 39.
20.
AlirezaGKazemiMMChing-TaiN, et al. Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network. Eng Struct2022; 264: 2–19.
21.
PuruncajasBVidalYTutivénC. Vibration-response-only structural health monitoring for offshore wind turbine jacket foundations via convolutional neural networks. Sensors2020; 20(12): 3429.
22.
BilottaAMorassiATurcoE. Damage identification for steel-concrete composite beams through convolutional neural networks. J Vib Cont2024; 30(3-4): 876–889.
23.
MaziarJMamdouhE. Structural damage severity classification from time-frequency acceleration data using convolutional neural networks. Structures2023; 54: 54236–253.
24.
HuangTLiangTChenL. A comparative analysis of damage identification methods based on multi-channel data fusion and convolutional neural network for frame structures. Structures2024; 61: 106076.
25.
ZifeiXChunLYangY. Fault diagnosis of rolling bearings using an improved multi-scale convolutional neural network with feature attention mechanism. ISA Transact2021; 110: 379–393.
26.
ChengjinQYanruiJZhinanZ, et al. Anti-noise diesel engine misfire diagnosis using a multi-scale CNN-LSTM neural network with denoising module. CAAI Transact Intell Technol2023; 8(3): 963–986.
27.
YanRuiJChengJinQZhiNanZ, et al. A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions. Sci China Technol Sci2022; 65(11): 2551–2563.
28.
HaopengLJieCXiaoqiangZ. Multi-branch and multi-scale dynamic convolutional network for small sample fault diagnosis of rotating machinery. IEEE Sen J2023; 23(8): 1.
29.
XiaoqiangZYazhouZ. An intelligent diagnosis method of rolling bearing based on multi-scale residual shrinkage convolutional neural network. Meas Sci Technol2022; 33(8): 085103.
30.
YongboLXiaoqiangDXianzhiW, et al. Industrial gearbox fault diagnosis based on multi-scale convolutional neural networks and thermal imaging. ISA Transact2022; 129(Part B): 309–320.
31.
WeiZJunxiaLShuaiH, et al. Application of multi-scale convolutional neural networks and extreme learning machines in mechanical fault diagnosis. Machines2023; 11(515): 515.
RouhuiWYizhuRMengyingT, et al. Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network. Build Simul2024; 17(3): 371–386.
34.
YeXCaoYLiuA, et al. Parallel convolutional neural network toward high efficiency and robust structural damage identification. Struct Health Monit2023; 22(6): 3805–3826.
