Intelligent automatic operational modal analysis

Rosso, Marco Martino; Aloisio, Angelo; Parol, Jafarali; Marano, Giuseppe Carlo; Quaranta, Giuseppe

doi:10.1016/j.ymssp.2023.110669

Cited by 22 publications

(2 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [ 45 ] were also successfully inspired by the SSI COV to build an automatic operational modal analysis system. It was described as an ML technique (random forest) to control the parameters of SSI COV to execute modal estimates.…”

Section: The Proposed Ar-based Shm Methodologymentioning

confidence: 99%

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification

Carter,

Sakr,

Sadhu

2024

Sensors

View full text Add to dashboard Cite

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork.

show abstract

Section: The Proposed Ar-based Shm Methodologymentioning

confidence: 99%

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification

Carter,

Sakr,

Sadhu

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Finally, a majority voting strategy was utilized to obtain the final size identification result. This can provide a better generalization than a single DT, and therefore minimizes the risk of overfitting [58]. A dataset description for training/testing is presented in Section 3.2.…”

Section: Proposed Methodsmentioning

confidence: 99%

Leak State Detection and Size Identification for Fluid Pipelines with a Novel Acoustic Emission Intensity Index and Random Forest

Nguyen,

Ahmad,

Kim

2023

Sensors

View full text Add to dashboard Cite

In this paper, an approach to perform leak state detection and size identification for industrial fluid pipelines with an acoustic emission (AE) activity intensity index curve (AIIC), using b-value and a random forest (RF), is proposed. Initially, the b-value was calculated from pre-processed AE data, which was then utilized to construct AIICs. The AIIC presents a robust description of AE intensity, especially for detecting the leaking state, even with the complication of the multi-source problem of AE events (AEEs), in which there are other sources, rather than just leaking, contributing to the AE activity. In addition, it shows the capability to not just discriminate between normal and leaking states, but also to distinguish different leak sizes. To calculate the probability of a state change from normal condition to leakage, a changepoint detection method, using a Bayesian ensemble, was utilized. After the leak is detected, size identification is performed by feeding the AIIC to the RF. The experimental results were compared with two cutting-edge methods under different scenarios with various pressure levels and leak sizes, and the proposed method outperformed both the earlier algorithms in terms of accuracy.

show abstract