Empirical mode decomposition and its variants: A review with applications in structural health monitoring
Structural health monitoring (SHM) is one of the most emerging approaches for early
damage detection, which leads to improved safety and efficient maintenance of large-scale …
damage detection, which leads to improved safety and efficient maintenance of large-scale …
Unsupervised learning methods for data-driven vibration-based structural health monitoring: a review
Structural damage detection using unsupervised learning methods has been a trending
topic in the structural health monitoring (SHM) research community during the past decades …
topic in the structural health monitoring (SHM) research community during the past decades …
Structural health monitoring by a novel probabilistic machine learning method based on extreme value theory and mixture quantile modeling
This article proposes a novel probabilistic machine learning method based on unsupervised
novelty detection for health monitoring of civil structures. The core of this method is based on …
novelty detection for health monitoring of civil structures. The core of this method is based on …
Unsupervised learning-based damage assessment of full-scale civil structures under long-term and short-term monitoring
MH Daneshvar, H Sarmadi - Engineering Structures, 2022 - Elsevier
Abstract Machine learning has become an influential and useful tool for many civil
engineering applications, particularly structural health monitoring (SHM). For this reason …
engineering applications, particularly structural health monitoring (SHM). For this reason …
Big data analytics and structural health monitoring: a statistical pattern recognition-based approach
Recent advances in sensor technologies and data acquisition systems opened up the era of
big data in the field of structural health monitoring (SHM). Data-driven methods based on …
big data in the field of structural health monitoring (SHM). Data-driven methods based on …
A locally unsupervised hybrid learning method for removing environmental effects under different measurement periods
Environmental effects induce deceptive variability in unlabeled vibration data for structural
health monitoring (SHM). Although unsupervised learning is an effective solution to this …
health monitoring (SHM). Although unsupervised learning is an effective solution to this …
Non-parametric empirical machine learning for short-term and long-term structural health monitoring
A Entezami, H Shariatmadar… - Structural Health …, 2022 - journals.sagepub.com
Early damage detection is an initial step of structural health monitoring. Thanks to recent
advances in sensing technology, the application of data-driven methods based on the …
advances in sensing technology, the application of data-driven methods based on the …
Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection
Time series analysis and novelty detection are effective and promising methods for data-
driven structural health monitoring (SHM) based on the statistical pattern recognition …
driven structural health monitoring (SHM) based on the statistical pattern recognition …
Structural damage detection based on variational mode decomposition and kernel PCA-based support vector machine
This paper proposes a novel structural damage detection method by combining the
advantages of variational mode decomposition algorithm and kernel principal component …
advantages of variational mode decomposition algorithm and kernel principal component …
A novel feature extraction method based on weighted multi-scale fluctuation based dispersion entropy and its application to the condition monitoring of rotary …
Features describing the state of industrial gearboxes and their extraction from the mixed
noisy signal are always an issue of concern. Unfortunately, traditional feature extraction …
noisy signal are always an issue of concern. Unfortunately, traditional feature extraction …