Performance Evaluation of an Entropy-Based Structural Health Monitoring System Utilizing Composite Multiscale Cross-Sample Entropy

Abstract

The aim of this study was to develop an entropy-based structural health monitoring system for solving the problem of unstable entropy values observed when multiscale cross-sample entropy (MSCE) is employed to assess damage in real structures. Composite MSCE was utilized to enhance the reliability of entropy values on every scale. Additionally, the first mode of a structure was extracted using ensemble empirical mode decomposition to conduct entropy analysis and evaluate the accuracy of damage assessment. A seven-story model was created to validate the efficiency of the proposed method and the damage index. Subsequently, an experiment was conducted on a seven-story steel benchmark structure including 15 damaged cases to compare the numerical and experimental models. A confusion matrix was applied to classify the results and evaluate the performance over three indices: accuracy, precision, and recall. The results revealed the feasibility of the modified structural health monitoring system and demonstrated its potential in the field of long-term monitoring.

Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring

“…However, Cross-SampEn is not applicable to all problems for a more accurate value. Hence, RCMCSE was proposed to solve some problems such as the statistical reliability of Cross-SampEn was reduced as time scale increased [12]. RCMCSE, which is based on Cross-SampEn and MSE, is applicable to more problems compared with Cross-SampEn because of modifications to its calculation procedures.…”

“…According to previous research [12] discussing detection accuracy under different parameter combinations, RCMCSE parameters such as the template length m, threshold r, and signal length N were optimized as 4, 0.08 × SD of the time series, and 20,000 points, respectively. Gow et al [14] recommended a data length of between 14 m and 23 m points in the MSE analysis.…”

“…According to previous research [12], RCMCSE parameters such as the template length m, threshold r, signal length N, and scale factor were optimized as 4, 0.08 × SD of the time series, 20,000 points, and 10, respectively. RCMCSE was applied to calculate entropy values using numerical data, and all entropy values converted into statistics.…”

Voronoi Decomposition of Cardiovascular Dependency Structures in Different Ambient Conditions: An Entropy Study

“…Multiscale entropy ( MSE ) [ 8 , 9 ], based on SampEn , investigates the changes in complexity caused by a change of the time scale. Composite MSE ( CMSE ) performs an additional averaging, thus solving the problem of decreased reliability induced by temporal scaling [ 10 , 11 ]. A comprehensive study of fixed and variable thresholds at different scales also presents an excellent review of the MSE improvements [ 12 ].…”

“…The benefits offered by entropy are explored in cardiovascular data analysis. Entropy was implemented to determine the cardiac variability [ 13 ], the complexity changes in cardiovascular disease [ 14 ], a level of deterministic chaos of heart rate variability ( HRV ) [ 9 ], HRV complexity in diabetes patients [ 15 ], in heart failure [ 16 ], in stress, [ 17 , 18 ] or in different aging and gender groups [ 19 , 20 ], while multiscale cross-entropy was applied for health monitoring systems [ 11 ].…”