Structural Health Monitoring with Acoustic Emission
A special issue of Materials (ISSN 1996-1944).
Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 335
Special Issue Editors
Interests: formalisms for uncertainty representation; structural health monitoring with acoustic emission; prognostics and health management; image and video analysis
Interests: acoustic emission, structural health monitoring, fibre reinforced composite materials, distributed optical fibres, embedded sensing, guided waves, piezoelectric sensors, machine learning, clustering
Special Issue Information
Dear Colleagues,
The present Special Issue focuses on the use of acoustic emission (AE) as a structural health monitoring (SHM) technique for damage classification using data-driven algorithms. The use of such algorithms is well documented in the literature, but the exploitation of the potential of machine learning methods is limited in regards to AE data analysis. One of the inherent challenges with AE time-series data is related to the non-uniform time spacing of the signals obtained from a material.
Many data-driven models have been presented based on various different algorithms. They are able to discriminate between different failure modes in composites, for example, matrix cracking and fibre breakage. However, the number of studies that demonstrate the applicability of these models for on-line SHM—where new data arrive gradually, and are of interest in order to track the initiation and growth of the damage—are limited.
We welcome research papers that aim to address the challenge of on-line AE-based SHM that makes use of machine learning algorithms for data analysis. Contributions can focus on any aspect of the AE data processing chain, from sensors to decision-making, including novel data analysis and interpretation methodologies for SHM. We particularly welcome studies conducted on composite materials, but studies concerning other material systems will also be considered.
The following is a non-exhaustive list of the exemplary subject themes for this Special Issue:
- New sensors, using a combination or comparison of AE sensors such as PZT, NEMS/MEMS, optical fibres, flexible electronics, and wireless sensing.
- Data pre-processing, covering topics such as filtering, feature extraction, feature selection, localisation, sensor fault detection, compensation techniques for managing environmental effects and operational modes, and anomaly detection.
- Pattern recognition and machine learning, covering supervised, unsupervised, and partially supervised classification, as well as prediction and prognostics, and evaluation methods.
- Challenging application cases with AE data obtained from machinery, materials testing, and process monitoring. Examples include quasi-static and fatigue testing, multi-material and multi-sensor approaches, and application in extreme environments.
Papers focusing on the batch analysis of data without demonstrating applicability for online processing will be considered as less relevant for the Issue. Appropriate submissions should explicitly state the contributions and properly describe related works.
Data and code sharing using supporting websites such as NASA, Harvard, Mendeley, or Github, will be valuable for this Special Issue.
Dr. Emmanuel RamassoMs. Neha Chandarana
Guest Editors
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Keywords
- acoustic emission
- structural health monitoring
- damage classification
- algorithms
- machine learning methods
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