High-speed train structures, operating under time-varying conditions, present a significant challenge in the field of Structural Health Monitoring (SHM) due to uncertainties introduced during the extraction of damage indexes from signals. In this paper, a novel method for quantifying fatigue crack based on improved energy-power entropy under dynamic temperature environments is introduced. Two new types of information entropy, namely energy singular spectral entropy (ES) and power singular spectral entropy (PS) are proposed. A baseline Gaussian Mixture Model (GMM) is constructed using the information entropy acquired under dynamic temperature when the structure is in a healthy state. Subsequently, an online GMM is constructed via an online update mechanism utilizing a moving feature sample set. The minimum matching Kullback–Leibler (KL) distance of the probability component is employed to quantitatively depict the cumulative migration trend of the GMM under damage conditions, thereby aiding in damage detection. The experimental results verify the reliable performance of the GMM-KL method in detecting fatigue cracks under dynamic temperature.
AltunEKhanN (2021) Modelling with the novel INAR(1)-PTE process. Methodology and Computing in Applied Probability24(3): 1735–1751.
2.
AndreiSMaliukAZahoorA, et al. (2021) Novel bearing fault diagnosis using Gaussian mixture model-based fault band selection. Sensors21(19): 6579.
3.
BanerjeeSQingXPBeardS, et al. (2010) Prediction of progressive damage state at the hot spots using statistical estimation. Journal of Intelligent Material Systems and Structures21: 595–605.
4.
BarboshMSinghPSadhuA (2020) Empirical mode decomposition and its variants: a review with applications in structural health monitoring. Smart Materials and Structures29(9): 093001.
5.
ChakrabortyDKovvaliNSuppappolaA, et al. (2015) An adaptive learning damage estimation method for structural health monitoring. Journal of Intelligent Material Systems and Structures26(2): 125–143.
6.
ChenHZhangGFanD, et al. (2020) Nonlinear lamb wave analysis for microdefect identification in mechanical structural health assessment. Measurement164: 108026.
7.
DarSLee (2005) Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence27(08): 27–32.
8.
HassanMHabibMRSeoudR, et al. (2018) Wavelet-based multiresolution bispectral analysis for detection and classification of helicopter drive-shaft problems. Journal of Dynamic Systems, Measurement, and Control140: 061009.
9.
LeJLiaoXZhangL, et al. (2021) Distributionally robust chance constrained planning model for energy storage plants based on Kullback–Leibler divergence. Energy Reports7(3): 5203–5213.
10.
LiuXYuYLiJ, et al. (2022) Leaky Lamb wave–based resin impregnation monitoring with noninvasive and integrated piezoelectric sensor network. Measurement189: 110480.
11.
LuSBZhouL (2017) Fatigue crack monitoring of aerospace structure based on lamb waves and binary tree support vector machines. Journal of Vibroengineering19(5): 3271–3282.
12.
MaoYMaLRenY, et al. (2021) Research on regional adjustment method of power grid operation and maintenance cost level based on information entropy considering multiple differences. IOP Conference Series: Earth and Environmental Science680(1): 012050.
13.
QiuLFangFYuanSF (2019) Improved density peak clustering-based adaptive Gaussian mixture model for damage monitoring in aircraft structures under time varying conditions. Mechanical Systems and Signal Processing126: 281–304.
14.
RezaeianjouybariBShangY (2020) Deep learning for prognostics and health management: state of the art, challenges, and opportunities. Measurement163: 107929.
15.
ShiGLLiXJinWD, et al. (2014) Fault analysis of high-speed train running gear based on permutation and combination entropy. Computer application research31(12): 3625–3627.
16.
ShiYFZhuZYChenP, et al. (2021) Self reduced working modal analysis of state space model based on EM algorithm and modal form. Engineering Mechanics38(09): 15–25.
17.
WangQMaSXYueD (2018) Identification of damage incomposite structures using Gaussian Mixture Model processed Lamb waves. Smart Materials and Structures27(4): 045007.
18.
WangJZShenYFRaoD, et al. (2021) Physical-virtual time reversing of nonlinear Lamb waves for fatigue crack detection and quantification. Mechanical Systems and Signal Processing160: 107921.
19.
WangBShiWZhaoB, et al. (2022) A modal decomposition imaging algorithm for ultrasonic detection of delamination defects in carbon fiber composite plates using air-coupled Lamb waves. Measurement195: 111165.
20.
XieFYJiangYQFengCY, et al. (2022) EEMD-KNN state recognition method for locomotive traction seat. Machine Tool & Hydraulics50(13): 32–56.
21.
YangQH (2021) An arrhythmia diagnosis method based on the combined feature of instantaneous frequency and power spectrum entropy. Computer Science and Application11(3): 495–503.
22.
YangYYuDJChengJ (2016) A roller bearing fault diagnosis method based on EMD energy entropy and ANN. Journal of Sound and Vibration294(1): 269–277.
23.
YangJSHuaMSWangTT, et al. (2024) An oversampling method based on Gaussian Mixture Model for multi-bolt looseness monitoring using Lamb waves. Journal of Vibration and Control1–15. doi: 10.1177/10775463241260443.
24.
YuJ (2012) Machine tool condition monitoring based on an adaptive Gaussian mixture model. Journal of Manufacturing Science and Engineering134(3): 031004.
25.
YuanSFChenJYangWB, et al. (2017) On-line crack prognosis in attachment lug using Lamb wave-deterministic resampling particle filter-based method. Smart Materials and Structures26: 085016.
26.
ZhangLGLiuWZhangSQ, et al. (2021) Chaotic singular spectrum analysis and application based on improved phase space reconstruction algorithm. Journal of Metrology42(10): 167–192.
27.
ZhouKZhengYBZhangJ, et al. (2019) A reconstruction-based mode separation method of Lamb wave for damage detection in plate structures. Smart Materials and Structures28(3): 035033.
28.
ZhouDTHuoLSChenD, et al. (2021) A feasibility study on monitoring of weld fatigue crack growth based on coda wave interferometry (CWI). Smart Materials and Structures30(9): 095013.
29.
ZhuYJNiYQJesusA, et al. (2018) Thermal strain extraction methodologies for bridge structural condition assessment. Smart Materials and Structures27(10): 105051.
