A Digital Twin concept for the prescriptive maintenance of protective coating systems on wind turbine structures

The application of protective coating systems is the major measure against the corrosion of steel for tower sections of wind turbines. The inspection, condition monitoring and maintenance of protective coating system is a demanding and time-consuming procedure and requires high human effort. The paper introduces for the first time a Digital Twin concept for the condition monitoring and prescriptive maintenance planning for surface protection systems on wind turbine towers. The initial point of the concept is an in-situ Virtual Twin for the generation of reference areas for condition monitoring. The paper describes the integration of an online image annotation and processing tool, a maintenance model, corrosive resistance parameters, structural load parameters, and sensor data into the Digital Twin concept. The prospects of the concept and its practical relevance are shown for tower structures of large onshore wind turbines.

A parametric texture model based on deep convolutional features closely matches texture appearance for humans

Our visual environment is full of texture—“stuff” like cloth, bark or gravel as distinct from “things” like dresses, trees or paths—and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parameteric model of texture appearance (CNN model) that uses the features encoded by a deep convolutional neural network (VGG-19) with two other models: the venerable Portilla and Simoncelli model (PS) and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery (“parafoveal”) and when observers were able to make eye movements to all three patches (“inspection”). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler PS model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the PS model (9 compared to 4 textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesise textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions.

Comparative analysis of sewer physical condition grading protocols for the City of Edmonton

Condition assessment is a key component of successful asset management, and many diverse condition assessment protocols exist for sewer mains. These protocols are used to generate condition grades for sewer mains after inspection. Condition assessment protocols are important because renewal actions for sewers are prioritized based on condition grades. If condition grades assigned to sewers do not reflect actual pipe conditions close enough, the resulting allocation of resources will not be the best. This paper presents a comparison of three possible sewer condition assessment protocols for the City of Edmonton: the Pipeline Assessment and Certification Program quick grading method, the Manual of Sewer Condition Classification fourth edition, and the City of Edmonton’s Sewer Physical Condition Classification Manual. The results show that structural sewer condition grades depend significantly on the protocol used; therefore, protocol selection has significant financial consequences for asset management programs in the city.

INSPECTION OF IMPACT DAMAGE IN HONEYCOMB COMPOSITE BY ESPI, THERMOGRAPHY AND ULTRASONIC TESTING

Honeycomb composites are now fairly widely used in civilian and military aircraft structures. Common defects found in these materials are delaminations by impact damage and their presence will lead to structural weaknesses which could lead failure of the airframe structures. It is important to develop effective non-destructive testing procedures to identify these defects and increase the safety of aircraft travel. This paper describes the detection technique of impact damage defect using thermography and ESPI. The results obtained with the two techniques are compared with ultrasonic C-scan testing. The investigation shows that both imaging NDT methods are able to identify the presence of artificial defect and impact damage. The adoption of the thermography allowed significant advantages in inspection condition, and gives smaller error in quantitative estimation of defects.

A Web-Based Intelligent System for Used-Oil Analysis

The analysis of oil in an operating machine is considered as a very useful means to assess the condition of the machine. However, classical techniques of oil analysis are strongly dependent on the analyst’s expertise to perform wear particle inspection, condition classification, colligation of the test results by ferrography, AES. and physical or chemical detection and interpretation of the possible existing faults in a machine. To solve these problems and realize the intelligence of oil analysis, a Web-based intelligence system for oil analysis has been devised. This system is composed of an automatic ferroscope controlled by a computer to obtain improved wear debris images, a platform to process the images and to connect the field analyst with the experts in machine diagnosis through internet and an intelligent software platform to evaluate the tribological conditions and diagnose the faults. Furthermore, some intelligent diagnosis methods used in the system are introduced.