Output-only entropy-based damage detection using transmissibility function

Soofi, Yasaman J.; Bitaraf, Maryam

doi:10.1007/s13349-021-00535-3

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…Therefore, a damaged system exhibits a different level of complexity compared to a baseline system. Over the past few decades, several entropy-based methods have been introduced in the field of SHM, such as sample entropy [ 27 , 28 ], fuzzy entropy [ 29 ], permutation entropy [ 30 ], and spectral entropy [ 31 ]. Information theory-based methods have emerged as a new approach to novelty detection [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

“…These characteristics make PE an attractive tool for handling a wide range of practical signal applications [ 33 ]. For instance, Soofi et al [ 30 ] employed PE and inverse transmissibility function to investigate a damage diagnosis method based solely on entropy metrics. Li et al [ 34 ] utilized multiscale PE to develop a novel method for detecting metal fatigue crack propagation.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Zhang,

Li,

2024

Sensors

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In structural health monitoring (SHM), most current methods and techniques are based on the assumption of linear models and linear damage. However, the damage in real engineering structures is more characterized by nonlinear behavior, including the appearance of cracks and the loosening of bolts. To solve the structural nonlinear damage diagnosis problem more effectively, this study combines the autoregressive (AR) model and amplitude-aware permutation entropy (AAPE) to propose a data-driven damage detection method. First, an AR model is built for the acceleration data from each structure sensor in the baseline state, including determining the model order using a modified iterative method based on the Bayesian information criterion (BIC) and calculating the model coefficients. Subsequently, in the testing phase, the residuals of the AR model are extracted as damage-sensitive features (DSFs), and the AAPE is calculated as a damage classifier to diagnose the nonlinear damage. Numerical simulation of a six-story building model and experimental data from a three-story frame structure at the Los Alamos Laboratory are utilized to illustrate the effectiveness of the proposed methodology. In addition, to demonstrate the advantages of the present method, we analyzed AAPE in comparison with other advanced univariate damage classifiers. The numerical and experimental results demonstrate the proposed method’s advantages in detecting and localizing minor damage. Moreover, this method is applicable to distributed sensor monitoring systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Zhang,

Li,

2024

Sensors

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“…The damage detection of structural partitions can accurately locate the local damage to the system and then complete the detection of large and complex structural damage. Because the structure is partitioned, each inspection only needs to be measured in a smaller area, so the detection of sizeable structural damage can be performed by a limited number of sensors (Li et al (2021); Soofi and Bitaraf (2022)). Theoretically, for multi-degree-of-freedom (MDOF) systems (Zhang et al (1999); Hughes et al (2004)), the TF is derived using the dynamical properties of the system structure, and the TF depends only on the system zero point.…”

Section: Introductionmentioning

confidence: 99%

Correlation transmissibility damage indicator for deterioration performance analysis of hydropower generator unit

Tong

Mao

et al. 2023

Journal of Vibration and Control

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The condition monitoring system of the hydropower generator unit (HGU) contains a vast amount of monitoring data related to equipment performance. It is challenging to reveal the HGU performance change law from these data. This paper constructed a correlation transmissibility damage indicator (CTDI) to analyze HGU degradation performance based on the transmissibility function (TF). Unlike traditional TF damage indicators, CTDI can use different types of monitoring data to obtain system TF to increase TF combination and improve detection sensitivity. The analysis is performed using actual monitoring data from a hydropower plant in Guangxi, China. Firstly, a new frequency concept is defined based on the characteristics of HGU obtaining data every 10 minutes. Then, the HGU is considered a system and divided into three subsystems according to the sensor installation locations of the monitoring system. The damage indicator is used to analyze the system and its subsystems at the time scales of day, week, and month to locate and quantify the damage of HGU. The final results of this analysis show that the method can accurately detect and locate faults, applies to different operating conditions, and reveals the HGU performance deterioration pattern, which is of great significance for HGU maintenance.

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Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach

Jalali,

Yeganeh Khaksar,

Mohammadzadeh S.

et al. 2024

Front. Struct. Civ. Eng.

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Output-only entropy-based damage detection using transmissibility function

Cited by 7 publications

References 43 publications

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Correlation transmissibility damage indicator for deterioration performance analysis of hydropower generator unit

Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach

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