Structural damage detection using deep learning and FE model updating techniques

Abstract The structural condition can be estimated by various methods. Damage detection, as one of those methods, deals with identifying changes in specific features within structural behavior based on numerical models. Since the method is based on simulation for various damage conditions, there are limitations in applicability due to inevitable discrepancies between the analytical model and the actual structure. Finite element model updating is a technique for establishing a finite element model that can reflect the current state of a target structure based on the measured responses. It is performed based on optimization for various structural parameters, but the final output can converge differently depending on the initial model and the characteristics of the algorithm. Although the updated model may not faithfully replicate the target structure as it is, it can be considered equivalent in terms of the relationship between the structural properties and behavioral characteristics of the target. This allows for the analysis of changes in the mechanical relationships established for the target structure. The change can be related to structural damage, and artificial intelligence technology can provide an alternative solution in such complex problems where analytical approaches are challenging. Taking practical aspects from the aforementioned methods, a novel structural damage detection methodology is presented in this study for identifying the location and extent of the damage. Model updating is used to establish a reference model that reflects the structural characteristics of the target. Training data for various damage conditions based on the reference model allows the artificial intelligence networks to identify damage to the target structure

The optical characteristics of organic light-emitting diodes with various microlens arrays

It is important to increase the light extraction efficiency (LEE) of devices in which micrometer-sized spheroidal, conical, pyramidal, and truncated microlens arrays (MLAs) are coupled to organic light-emitting diodes (OLEDs). MLAs may be hexagonal or square. To specify the shapes of individual lenses and their arrays in MLAs, the respective variables x (newly defined in this paper) and the fill factor (FF) can be used. MLAs are characterised by their LEE enhancements (LEEEs) and angular intensity distributions (AIDs), and these are the key optical characteristics of MLAs coupled to OLEDs. Here, these characteristics were obtained via computational simulation using a ray-tracing method. The LEEEs of the spheroidal MLAs were the same regardless of whether the arrays were hexagonal or square. The spheroidal MLAs are suitable for light emission applications in forward direction and, for spherical MLAs of FF = 74.55%, the LEEE was 88.3%. For conical hexagonal and pyramidal MLAs, the LEEEs gradually decreased and the difference widened as the extent of truncation increased. For display applications, the largest LEEE for light rays with emission angles < 10° was 56.5% for spheroidal MLA/OLD devices. Considering all emission angles, the largest LEEE was about 120% for pyramidal MLA/OLED devices. For regular pyramidal MLAs, the intensities of emitted light by the azimuth emission angle evidenced four sawtooth shapes precluding applications in displays

Detecting Deformation on Pantograph Contact Strip of Railway Vehicle on Image Processing and Deep Learning

An electric railway vehicle is supplied with electricity by an OCL (Overhead Contact Line) through the contact strip of its pantograph. This transmitted electricity is then used to power the electrical equipment of the railway vehicle. This contact strip wears out due to contact with the OCL. In particular, deformations due to chipping and material loss occur because of friction with the fittings on the OCL. These deformations on the contact strip affect its power transmission quality because of contact loss with the OCL. However, it is difficult to monitor the contact strip during operation and judge its condition in order to implement accident prevention measures. Thus, in this study, we developed a contact strip monitoring method based on image processing for inspection. The proposed method measures the deformation in the contact strip based on an algorithm that determines the wear on the deformed contact strip using deep learning and image processing. The image of the contact strip is acquired by installing a camera and laser to capture the pantograph as it passes the setup. The proposed algorithm is able to determine the wear size by extracting the edges of the laser line using deep learning and estimating the fitted line of the deformations based on the least squares method