Structural Health Monitoring Using Acoustic Emission Signals: The Role of Machine Learning
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 122
Special Issue Editor
Interests: acoustic emission; structure–property relationships for the development of engineering performance of materials and also for use as diagnostic indicators in medicine (tissue quality); development of new, quantitative approaches to the analysis of stress waves in solids for diagnostic engineering; applications of mechanics to miniature mechanical systems to achieve real engineering solutions in millimetre-sized envelopes
Special Issue Information
Dear Colleagues,
Acoustic emission (structure-borne, self-generated ultrasound) has been suggested for structural monitoring for several decades. However, for applications outside the laboratory, there is a need to separate the signal of interest (e.g., that from a growing fatigue crack) from the many other sources of AE (e.g., from adjacent operating machinery) in a typical service environment. This Special Issue will bring together contributions from leaders in the fields of manufacturing, transport, civil engineering, and biomedical engineering to form a comprehensive overview of the state of the art, including case studies.
Topics will include the following:
- Monitoring damage accumulation in structures made from composite materials;
- Assessments of structural integrity and structural degradation;
- Integrity in biomechanical applications;
- Machinery health monitoring;
- Signal processing and AI techniques;
- Monitoring arrays of structures and machines with high reliability
Prof. Dr. Robert L. Reuben
Guest Editor
Manuscript Submission Information
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Keywords
- acoustic emission
- structural integrity
- structural degradation
- machine learning
- artificial intelligence
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