The Vibration Monitoring Methods and Signal Processing Techniques for Structural Health Monitoring: A Review

2015 ◽  
Vol 23 (4) ◽  
pp. 585-594 ◽  
Author(s):  
D. Goyal ◽  
B. S. Pabla
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Vibration Monitoring ◽  
Monitoring Methods ◽  
Structural Health ◽  
Processing Techniques ◽  
Signal Processing Techniques

scholarly journals A Review of Recent Distributed Optical Fiber Sensors Applications for Civil Engineering Structural Health Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1818
Author(s):  
Mattia Francesco Bado ◽  
Joan R. Casas
Keyword(s):  
Structural Health Monitoring ◽  
Optical Fiber ◽  
Health Monitoring ◽  
Optical Fiber Sensors ◽  
Vibration Monitoring ◽  
Fiber Sensors ◽  
Structural Health ◽  
Comprehensive Collection ◽  
Monitoring Tools ◽  
Distributed Optical Fiber

The present work is a comprehensive collection of recently published research articles on Structural Health Monitoring (SHM) campaigns performed by means of Distributed Optical Fiber Sensors (DOFS). The latter are cutting-edge strain, temperature and vibration monitoring tools with a large potential pool, namely their minimal intrusiveness, accuracy, ease of deployment and more. Its most state-of-the-art feature, though, is the ability to perform measurements with very small spatial resolutions (as small as 0.63 mm). This review article intends to introduce, inform and advise the readers on various DOFS deployment methodologies for the assessment of the residual ability of a structure to continue serving its intended purpose. By collecting in a single place these recent efforts, advancements and findings, the authors intend to contribute to the goal of collective growth towards an efficient SHM. The current work is structured in a manner that allows for the single consultation of any specific DOFS application field, i.e., laboratory experimentation, the built environment (bridges, buildings, roads, etc.), geotechnical constructions, tunnels, pipelines and wind turbines. Beforehand, a brief section was constructed around the recent progress on the study of the strain transfer mechanisms occurring in the multi-layered sensing system inherent to any DOFS deployment (different kinds of fiber claddings, coatings and bonding adhesives). Finally, a section is also dedicated to ideas and concepts for those novel DOFS applications which may very well represent the future of SHM.

Time–frequency and time–scale analyses for structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 449-477 ◽  
Author(s):  
Wiesław J Staszewski ◽  
Amy N Robertson
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Time Scale ◽  
Time Frequency ◽  
Structural Health ◽  
Variant Analysis

Signal processing is one of the most important elements of structural health monitoring. This paper documents applications of time-variant analysis for damage detection. Two main approaches, the time–frequency and the time–scale analyses are discussed. The discussion is illustrated by application examples relevant to damage detection.

Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

2021 ◽  
pp. 136943322110384
Author(s):  
Xingyu Fan ◽  
Jun Li ◽  
Hong Hao
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Low Frequency ◽  
Data Driven ◽  
Mode Shapes ◽  
Monitoring Methods ◽  
Destructive Testing ◽  
Electromechanical Impedance ◽  
Structural Health

Vibration based structural health monitoring methods are usually dependent on the first several orders of modal information, such as natural frequencies, mode shapes and the related derived features. These information are usually in a low frequency range. These global vibration characteristics may not be sufficiently sensitive to minor structural damage. The alternative non-destructive testing method using piezoelectric transducers, called as electromechanical impedance (EMI) technique, has been developed for more than two decades. Numerous studies on the EMI based structural health monitoring have been carried out based on representing impedance signatures in frequency domain by statistical indicators, which can be used for damage detection. On the other hand, damage quantification and localization remain a great challenge for EMI based methods. Physics-based EMI methods have been developed for quantifying the structural damage, by using the impedance responses and an accurate numerical model. This article provides a comprehensive review of the exciting researches and sorts out these approaches into two categories: data-driven based and physics-based EMI techniques. The merits and limitations of these methods are discussed. In addition, practical issues and research gaps for EMI based structural health monitoring methods are summarized.

scholarly journals Application of Machine Learning with Impedance Based Techniques for Structural Health Monitoring of Civil Infrastructure

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1139-1148
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Soft Computing ◽  
Health Monitoring ◽  
Multiple Cracks ◽  
Civil Infrastructure ◽  
Structural Health ◽  
Soft Computing Techniques

Increased attentiveness on the environmental and effects of aging, deterioration and extreme events on civil infrastructure has created the need for more advanced damage detection tools and structural health monitoring (SHM). Today, these tasks are performed by signal processing, visual inspection techniques along with traditional well known impedance based health monitoring EMI technique. New research areas have been explored that improves damage detection at incipient stage and when the damage is substantial. Addressing these issues at early age prevents catastrophe situation for the safety of human lives. To improve the existing damage detection newly developed techniques in conjugation with EMI innovative new sensors, signal processing and soft computing techniques are discussed in details this paper. The advanced techniques (soft computing, signal processing, visual based, embedded IOT) are employed as a global method in prediction, to identify, locate, optimize, the damage area and deterioration. The amount and severity, multiple cracks on civil infrastructure like concrete and RC structures (beams and bridges) using above techniques along with EMI technique and use of PZT transducer. In addition to survey advanced innovative signal processing, machine learning techniques civil infrastructure connected to IOT that can make infrastructure smart and increases its efficiency that is aimed at socioeconomic, environmental and sustainable development.

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