A literature review of next-generation smart sensing technology in structural health monitoring

Vibration-based damage detection in wind turbine blades using Phase-based Motion Estimation and motion magnification

Operations and maintenance of offshore wind turbines (OWTs) play an important role in the development of offshore wind farms. Compared with operations, maintenance is a critical element in the levelized cost of energy, given the practical constraints imposed by offshore operations and the relatively high costs. The effects of maintenance on the life cycle of an offshore wind farm are highly complex and uncertain. The selection of maintenance strategies influences the overall efficiency, profit margin, safety, and sustainability of offshore wind farms. For an offshore wind project, after a maintenance strategy is selected, schedule planning will be considered, which is an optimization problem. Onsite maintenance will involve complex marine operations whose efficiency and safety depend on practical factors. Moreover, negative environmental impacts due to offshore maintenance deserve attention. To address these issues, this paper reviews the state-of-the-art research on OWT maintenance, covering strategy selection, schedule optimization, onsite operations, repair, assessment criteria, recycling, and environmental concerns. Many methods are summarized and compared. Limitations in the research and shortcomings in industrial development of OWT operations and maintenance are described. Finally, promising areas are identified with regard to future studies of maintenance strategies.

Offshore wind turbine operations and maintenance: A state-of-the-art review

Foreword Chapter 1 Introduction to SHM (Daniel L Balageas) Chapter 2 Vibration-based techniques for SHM (Claus-Peter Fritzen) Chapter 3 Fiber-optics sensors (Alfredo Guemes and Jose M Menendez) Chapter 4 SHM with piezoelectric sensors (Philippe Guy and Thomas Monnier) Chapter 5 SHM using electrical resistance (Michelle Salvia and Jean-Christophe Abry) Chapter 6 Low frequency electromagnetic techniques (Michel B Lemistre) Chapter 7 Capacitive methods for SHM in civil engineering (Xavier Derobert and Jean Iaquinta) Short Bibliographies of the Contributors Index

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Structural health monitoring

This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners. In particular, a stack of autoencoders and a convolutional neural network are used. The idea is to leverage the reflection and reverberation patterns of AE waveforms as well as their dispersive and multimodal characteristics to localize their sources with only one sensor. Specifically, this paper divides the structure into multiple zones and finds the zone in which each source occurs. To train, validate, and test the deep learning networks, fatigue cracks were experimentally simulated by Hsu–Nielsen pencil lead break tests. The pencil lead breaks were carried out on the surface and at the edges of the plate. The results show that both deep learning networks can learn to map AE signals to their sources. These results demonstrate that the reverberation patterns of AE sources contain pertinent information to the location of their sources.

https://www.mdpi.com/2226-4310/5/2/50/pdf

Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning

Feasibility of extracting operating shapes using phase-based motion magnification technique and stereo-photogrammetry

As a specific modern non-contact sensing technology, optical/video information is getting more and more attention employed to interpret structural responses and system status awareness. By means of processing the acquired video, a full-field system information is available which may be applied later to Experimental Modal Analysis (EMA), Structural Health Monitoring (SHM), System Identification (SI), etc., while at the same time, there is no influence to the structural testing such as mass loading and stiffness change. There are numerous technologies to extract the dynamic response of structures from acquired videos. In this paper, several point tracking algorithms are particularly compared, including Lucas-Kanade tracker, Hungarian registration algorithm and particle filter. These computer vision algorithms are implemented to extract the natural frequencies of a lab-scale structure, and the efficiency of each method is investigated regarding the consistency in estimating the natural frequencies and computational time. The recorded video contains external noise caused by lighting change during the experiment, as well as the intrinsic uncertainty on the photosensitive devices. Therefore, the natural frequencies estimated via different algorithms will have different values. An overall comparison between several computer vision algorithms are made in this paper in terms of precision, and computational load.

A Comparison of Computer-Vision-Based Structural Dynamics Characterizations

In recent years, image processing techniques are being applied more often for structural dynamics identification, characterization, and structural health monitoring. Although as a non-contact and full-field measurement method, image processing still has a long way to go to outperform other conventional sensing instruments (i.e. accelerometers, strain gauges, laser vibrometers, etc.,). However, the technologies associated with image processing are developing rapidly and gaining more attention in a variety of engineering applications including structural dynamics identification and modal analysis. Among numerous motion estimation and image-processing methods, phase-based video motion estimation is considered as one of the most efficient methods regarding computation consumption and noise robustness. In this paper, phase-based video motion estimation is adopted for structural dynamics characterization on a 2.3-meter long Skystream wind turbine blade, and the modal parameters (natural frequencies, operating deflection shapes) are extracted. Phase-based video processing adopted in this paper provides reliable full-field 2-D motion information, which is beneficial for manufacturing certification and model updating at the design stage. The phase-based video motion estimation approach is demonstrated through processing data on a full-scale commercial structure (i.e. a wind turbine blade) with complex geometry and properties, and the results obtained have a good correlation with the modal parameters extracted from accelerometer measurements, especially for the first four bending modes, which have significant importance in blade characterization.

Aral Sarrafi

Papers

Vibration-based damage detection in wind turbine blades using Phase-based Motion Estimation and motion magnification

Feasibility of extracting operating shapes using phase-based motion magnification technique and stereo-photogrammetry

A Comparison of Computer-Vision-Based Structural Dynamics Characterizations

Mode extraction on wind turbine blades via phase-based video motion estimation

Multicamera measurement system to evaluate the dynamic response of utility-scale wind turbine blades