Entropy Method for Structural Health Monitoring Based on Statistical Cause and Effect Analysis of Acoustic Emission and Vibration Signals

Abstract

Acoustic emission (AE) and vibration signal are significant criteria of damage identification in structural health monitoring (SHM) engineering. Multidisciplinary knowledge and synergistic parameter effects are technical challenges for damage assessment modelling. This study proposes a structural damage cause-and-effect analysis method based on parameter information entropy. Monitoring data is used to form a time-domain feature wave (TFW). The structural strength degradation factor (DF) would be used to define structural damage information entropy (SDIE) vector. The structural damage cause and effect model is developed in a probability sense. A fatigue index is adopted for damage assessment, and a causal strength index is proposed to locate the most likely damage cause. A sandstone-truss structure experiment was conducted to show that the proposed method is effective for damage evaluation and the experimental results provide strong support. This is a statistical damage identification method based on causal logic uncertainty, meaning a complicated mechanics calculation can be avoided. INDEX TERMS Structural health monitoring, acoustic emission, cause-and-effect analysis, parameter information entropy.

Structural Safety of the Steel Hall under Dynamic Excitation Using the Relative Probabilistic Entropy Concept

“…One of the powerful numerical methods necessary for the probability tails (and their logarithms) is undoubtedly Monte Carlo simulation, which in crude or interval versions [ 15 ] may enable even extremely advanced physical system simulations. Determination of the entropy (or uncertainty) propagation may be helpful in stochastic structural dynamics [ 16 ], structural health monitoring [ 17 ], and also for a prediction of the earthquake hazards [ 18 ]. It is also used in uncertainty propagation studies for computer simulation and homogenization of composite materials [ 19 , 20 ].…”

Uncertainty propagation, entropy, and relative entropy in the homogenization of some particulate composites

“…30 It has been most frequently used in engineering and in applied sciences in the context of the maximum entropy principle (MEP) [31][32][33] stating that for the given variance a maximum entropy is obtained for the Gaussian distribution. There are some scarce applications of the entropy determination itself in engineering, specifically in stochastic structural dynamics, 34 structural health monitoring 35 , and also for earthquake hazard investigation. 36 Some applications to the homogenization problems have been recently proposed with the use of Shannon 37 and also Tsallis 38 entropy models.…”

On Bhattacharyya relative entropy in a homogenization of composite materials

“…1,2 It may serve also as some uncertainty measure, alternatively to classical statistical estimators, not only in computer science but also in mechanical, civil, or aeronautical engineering. 3,4 Numerical analysis in this area includes also an application of the maximum entropy principle, which gained some attention in structural studies also, see References 5 and 6. Because the designing process is based upon the verification of some limit functions defined as the differences in-between structural capacity and actual structural effort, probabilistic distance (divergence) seems to be more applicable.…”

“…Probabilistic entropy has been invented to introduce some universal measure of disorder in the information theory to compare various variables, processes, and datasets with each other 1,2 . It may serve also as some uncertainty measure, alternatively to classical statistical estimators, not only in computer science but also in mechanical, civil, or aeronautical engineering 3,4 . Numerical analysis in this area includes also an application of the maximum entropy principle, which gained some attention in structural studies also, see References 5 and 6.…”