Underwater inspection/testing/ monitoring of offshore structures

Busby, Roswell F.

doi:10.1016/0029-8018(79)90006-4

Cited by 17 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This puts an emphasis on adopting expeditious data collection practices that harness the full potential of available tools. A comprehensive summary of the challenges facing underwater inspections is provided by Busby (1979) and Ramos (1992), which still remain relevant despite being published decades ago.…”

Section: Introductionmentioning

confidence: 99%

An underwater lighting and turbidity image repository for analysing the performance of image-based non-destructive techniques

O’Byrne

Schoefs

Pakrashi

et al. 2017

Structure and Infrastructure Engineering

View full text Add to dashboard Cite

Image processing based methods, capable of detecting and quantifying cracks, surface defects or recovering 3D shape information are increasingly being recognized as a valuable tool for inspecting underwater structures. It is of great practical importance for inspectors to know the effectiveness of such techniques when applied in conditions. This paper considers an underwater environment characterised by poor visibility chiefly governed by the lighting and turbidity levels, along with a range of geometry and damage conditions of calibrated specimens. The paper addresses the relationship between underwater visibility and the performance of image based methods through the development and calibration of a first open-source Underwater Lighting and Turbidity Image Repository (ULTIR). ULTIR contains a large collection of images of submerged specimens that have been photographed under controlled lighting and turbidity levels featuring various forms of geometry and damage. ULTIR aims to facilitate inspectors when rationalising the use of image processing methods as part of an underwater inspection campaign and to enable researchers to efficiently evaluate the performance of image based methods under realistic operating conditions. Stakeholders in underwater infrastructure can benefit through this first large, standardized, well-annotated, and freely-available database of images and associated metadata.

show abstract

Section: Introductionmentioning

confidence: 99%

An underwater lighting and turbidity image repository for analysing the performance of image-based non-destructive techniques

O’Byrne

Schoefs

Pakrashi

et al. 2017

Structure and Infrastructure Engineering

View full text Add to dashboard Cite

show abstract

“…The use of offshore equipment by the oil and gas industry creates the need for a periodic assessment of their condition. However, since such equipment remain in deep water during their entire lifespan, performing such assessment can present several challenges due to their remote location and the oceanic environment conditions [1]. Nonetheless, companies must commit to tackling this issue, as faulty equipment present a higher risk of environmentally harmful disasters [2].…”

Section: Introductionmentioning

confidence: 99%

Semi and Self-supervised Learning for Multi-label Classification for an Underwater Inspection Imagery Application

Pereira

Kohler

Castro

et al. 2023

Preprint

View full text Add to dashboard Cite

Underwater inspections are crucial for the preventive maintenance of offshore equipment from the oil and gas industry. However, the entire inspection process is costly, subjective and time-consuming. Traditionally, specialists assess equipment conditions through image and sensor data collected by underwater vehicles that travel to the seabed. Since this data can present a wide range of events, an inspection can yield a considerable amount of data. Despite being a tedious task, this data is undoubtedly domain-specific, requiring a group of highly specialized professionals to correctly scrutinize it. In this scenario, we propose using image classification models to help specialists find event(s) of interest. However, there are challenges inherent to the underwater inspection image classification problem, such as: high cost and scarcity of balanced labeled data, presence of label noise, high intra-class variance and characteristics of underwater images such as turbidity and uneven lightning conditions. Therefore, traditional supervised models might not fulfill the task. We undertake this problem with the methods DINO (Self-DIstillation with NO labels, self-supervised) and a new proposed method mPAWS, a multi-label version of PAWS (Predicting View Assignments With Support Samples, semi-supervised). The results obtained show the benefits of using such models for the application, achieving an improvement of $2.7\%$ when compared to current state-of-the-art supervised models. Such models can perform real-time inference, which can help accelerate the tasks of the underwater vehicle, making the work of the specialists more time-efficient.

show abstract

“…Dynamic strain response of the structure is of great concern for health monitoring of the structures under operating loads [1][2][3]. The inspection/testing/monitoring of the structure is a concern for its safety and potential damage to the environment [4][5][6]. One approach of getting the response is by identifying the actual loads and structural conditions, as well as building an appropriate model.…”

Section: Introductionmentioning

confidence: 99%

Underwater Dynamic Response at Limited Points Expanded to Full-Field Strain Response

Chen

Joffre

Avitabile

2018

Journal of Vibration and Acoustics

111

View full text Add to dashboard Cite

Expansion of real-time operating data from limited measurements to obtain full-field displacement data has been performed for structures in air. This approach has shown great success, and its main advantage is that the applied forces do not need to be identified. However, there are applications where structures may be immersed in water and the full-field real-time response may be needed for design and structural health assessments. This paper presents the results of a structure submersed in water to identify full-field response using only a handful of measured data. The same approach is used to extract the full-field displacements, and the results are compared to the actual full-field measured response. The advantage of this approach is that the force does not need to be identified and, most importantly, the damping and fluid–structure interaction does not need to be identified in order to perform the expansion. The results show excellent agreement with the measured data.

show abstract

Underwater inspection/testing/ monitoring of offshore structures

Cited by 17 publications

References 15 publications

An underwater lighting and turbidity image repository for analysing the performance of image-based non-destructive techniques

An underwater lighting and turbidity image repository for analysing the performance of image-based non-destructive techniques

Semi and Self-supervised Learning for Multi-label Classification for an Underwater Inspection Imagery Application

Underwater Dynamic Response at Limited Points Expanded to Full-Field Strain Response

Contact Info

Product

Resources

About