Ultrasonic Inspection for Welds with Irregular Curvature Geometry Using Flexible Phased Array Probes and Semi-Auto Scanners: A Feasibility Study

Pipes of various shapes constitute pipelines utilized in industrial sites. These pipes are coupled through welding, wherein complex curvatures such as a flange, an elbow, a reducer, and a branch pipe are often found. Using phased array ultrasonic testing (PAUT) to inspect weld zones with complex curvatures is faced with different challenges due to parts that are difficult to contact with probes, small-diameter pipes, spatial limitations due to adjacent pipes, nozzles, and sloped shapes. In this study, we developed a flexible PAUT probe (FPAPr) and a semi-automatic scanner that was improved to enable stable FPAPr scanning for securing its inspection data consistency and reproducibility. A mock-up test specimen was created for a flange, an elbow, a reducer, and a branch pipe. Artificial flaws were inserted into the specimen through notch and hole processing, and simulations and verification experiments were performed to verify the performance and field applicability of the FPAPr and semi-automatic scanner.

Reliability Updating of Offshore Structures Subjected to Marine Growth

Marine growth is a known problem for oceanic infrastructure and has been shown to negatively impact the reliability of bottom-fixed or floating offshore structures submitted to fatigue or extreme loading. Among other effects, it has been shown to change drag forces by increasing member diameters and modifying the roughness. Bio-colonization being highly random, the objective of this paper is to show how one-site inspection data increases reliability by decreasing uncertainties. This can be introduced in a reliability-based inspection framework for optimizing inspection and maintenance (here, cleaning). The modeling and computation are illustrated through the reliability analysis of a monopile in the European Atlantic area subjected to marine growth and according to the plastic collapse limit state. Based on surveys of structures in the North Sea, long-term stochastic modeling (space and time) of the marine growth thickness is first suggested. A Dynamic Bayesian Network is then developed for reliability updating from the inspection data. Finally, several realistic (10–20 measurements) inspection strategies are compared in terms of reliability improvement and the accuracy of reliability assessment.

Wetland loss in the Ñeembucú Wetlands Complex, Paraguay, using remote sensing

South American wetlands are of global importance, yet limited delineation and monitoring restricts informed decision-making around the drivers of wetland loss. A growing human population and increasing demand for agricultural products has driven wetland loss and degradation in the Neotropics. Understanding of wetland dynamics and land use change can be gained through wetland monitoring. The Ñeembucú Wetlands Complex is the largest wetland in Paraguay, lying within the Paraguay-Paraná-La Plata River system. This study aims to use remotely sensed data to map land cover between 2006 and 2021, quantify wetland change over the 15-year study period and thus identify land cover types vulnerable to change in the Ñeembucú Wetlands Complex. Forest, dryland vegetation, vegetated wetland and open water were identified using Random Forest supervised classifications trained on visual inspection data and field data. Annual change of -0.34, 4.95, -1.65, 0.40 was observed for forest, dryland, vegetated wetland and open water, respectively. Wetland and forest conversion is attributed to agricultural and urban expansion. With ongoing pressures on wetlands, monitoring will be a key tool for addressing change and advising decision-making around development and conservation of valuable ecosystem goods and services in the Ñeembucú Wetlands Complex.

Association analysis in food sampling inspection data

At present, China exists a problem that the cost of food sampling inspection is too high. This paper attempts to reduce the number of sampling inspection items in the same food category, reduce the cost of food sampling inspection, and improve the work efficiency through the association analysis of national sampling inspection data. And this paper applies Apriori algorithm to analyse the association rules, which is based on the unqualified pastry sampling inspection data in the 2019 national food sampling inspection database. Finally, we obtain 10 strong association rules through experiments. The results show that this association analysis can reduce the workload of food sampling inspection effectively.

Bridge maintenance management based on routine inspection data: a quantitative approach

The United States National Bridge Inventory (NBI) records element-level condition ratings on a scale of 0 to 9, representing failed to excellent conditions. Current bridge management systems apply Markov decision processes to find optimal repair schemes given the condition ratings. The deterioration models used in these approaches fail to consider the effect of structural age. In this study, a condition-based bridge maintenance framework is proposed where the state of a bridge component is defined using a three-dimensional random variable that depicts the working age, condition rating, and initial age. The proportional hazard model with a Weibull baseline hazard function translates the three-dimensional random variable into a single hazard indicator for decision-making. To demonstrate the proposed method, concrete bridge decks were taken as the element of interest. Two optimal hazard criteria help select the repair scheme (essential repair, general repair, or no action) that leads to minimum annual expected life-cycle costs.

A New Risk Assessment Model to Check Safety Threats to Long-Distance Pipelines

With the number of long-distance pipelines increasing in China, risk management has become important for controlling pipeline leakage. However, all the current assessment technologies are semi-quantitative and do not include inspection data. To address this problem, a new quantitative risk assessment model is proposed to guide decision-making on excavation inspection and maintenance. Based on previous failure cases, the model includes data about the surrounding soils as well as about the pipeline's protective layer, cathodic protection and thickness readings. Testing of the proposed model on previous failure cases shows that the new model can correctly assess the real leakage risk of a long-distance pipeline and support the quantitative integrity management of a long-distance pipeline during its whole service life.

Automated Painting Survey, Degree of Rusting Classification, and Mapping with Machine Learning

Objective Continuous fabric maintenance (FM) is crucial for uninterrupted operations on offshore oil and gas platforms. A primary FM goal is managing the onset of coating degradation across the surfaces of offshore platforms. Physical field inspection programs are required to target timely detection and grading of coating conditions. These processes are costly, time-consuming, labour-intensive, and must be conducted on-site. Moreover, the inspection findings are subjective and provide incomplete asset coverage, leading to increased risk of unplanned shutdowns. Risk reduction and increased FM efficiency is achieved using machine learning and computer vision algorithms to analyze full-facility imagery for coating degradation and subsequent ‘degree-of-rusting’ classification of equipment to industry inspection standards. Methods, Procedures, Process Inspection data is collected for the entirety of an offshore facility using a terrestrial scanner. Coating degradation is detected across the facility using machine learning and computer vision algorithms. Additionally, the inspection data is tagged with unique piping line numbers per design, fixed equipment tags, or unique asset identification numbers. Computer vision algorithms and the detected coating degradation are subsequently used as input to determine the ‘degree-of-rusting’ throughout the facility, and coating condition status is tagged to specific piping or equipment. The degree-of-rusting condition rating follows common industry standards used by inspection engineers (e.g., ISO 4628-3, ASTM D610-01, or European Rust Scale). Results, Observations, Conclusions Atmospheric corrosion is the number one asset integrity threat to offshore platforms. Utilizing this automatic coating condition technology, a comprehensive and objective analysis of a facility's health is provided. Coating condition results are overlaid on inspection imagery for rapid visualisation. Coating condition is associated with individual instances of equipment. This allows for rapid filtering of equipment by coating condition severity, process type, equipment type, etc. Fabric maintenance efficiencies are realized by targeting decks, blocks, or areas with the highest aggregate coating degradation (on process equipment or structurally, as selected by the user) and concentrating remediation efforts on at-risk equipment. With the automated classification of degree-of-rusting, mitigation strategies that extend the life of the asset can be optimised, resulting in efficiency gains and cost savings for the facility. Conventional manual inspections and reporting of coating conditions has low objectivity and increased risk and cost when compared to the proposed method. Novel/Additive Information Drawing on machine learning and computer vision techniques, this work proposes a novel workflow for automatically identifying the degree-of-rusting on assets using industry inspection standards. This contributes directly to greater risk awareness, targeted remediation strategies, improving the overall efficiency of the asset management process, and reducing the down-time of offshore facilities.