Effects of Thermal Gradients in High-Temperature Ultrasonic Non-Destructive Tests.

Ultrasonic inspection techniques and non-destructive tests are widely applied in evaluating products and equipment in the oil, petrochemical, steel, naval, and energy industries. These methods are well established and efficient for inspection procedures at room temperature. However, errors can be observed in the positioning and sizing of the flaws when such techniques are used during inspection procedures under high working temperatures. In such situations, the temperature gradients generate acoustic anisotropy and consequently distortion of the ultrasonic beams. Failure to consider such distortions in ultrasonic signals can result, in extreme situations, in mistaken decision making by inspectors and professionals responsible for guaranteeing product quality or the integrity of the evaluated equipment. In this scenario, this work presents a mathematical tool capable of mitigating positioning errors through the correction of focal laws. For the development of the tool, ray tracing concepts are used, as well as a model of heat propagation in solids and an experimentally defined linear approximation of dependence between sound speed and temperature. Using the focal law correction tool, the relative firing delays of the active elements are calculated considering the temperature gradients along the sonic path, and the results demonstrate a reduction of more than 68% in the error of flaw positioning.

Surface Estimation via Analysis Method: A Constrained Inverse Problem Approach.

Ultrasonic imaging is a common technique in nondestructive evaluation, as it presents advantages such as low cost and safety of operation. In many industries, the interior inspection of objects with complex geometry has become a necessity. This kind of inspection requires the transducer to be coupled to the object with the use of some technique, such as immersing the object in water. When doing so, the geometry of the object surface must be known a priori or estimated. Recent methods for surface estimation start with an image of the interface between water and the specimen. Then, the surface is estimated by processing the image using different strategies. In this article, the strategy to extract the surface profile is based on an analysis-based inverse problem, hence named surface estimation via analysis method (SEAM). The problem formulation aims to reduce the noise in the estimate and also, by including priors, reach more accurate estimates. By using a second-order total variation regularization, which favors piecewise linear functions, the proposed method can describe a great range of surface profiles. Experiments were performed to evaluate the proposed method on surface profile estimation and results show good agreement with references and lower errors than methods in the literature. In addition, the estimated profiles enhance the imaging of the interior of objects, allowing better visualization of internal defects.

Sliding Window Mapping for Omnidirectional RGB-D Sensors.

This paper presents an omnidirectional RGB-D (RGB + Distance fusion) sensor prototype using an actuated LIDAR (Light Detection and Ranging) and an RGB camera. Besides the sensor, a novel mapping strategy is developed considering sensor scanning characteristics. The sensor can gather RGB and 3D data from any direction by toppling in 90 degrees a laser scan sensor and rotating it about its central axis. The mapping strategy is based on two environment maps, a local map for instantaneous perception, and a global map for perception memory. The 2D local map represents the surface in front of the robot and may contain RGB data, allowing environment reconstruction and human detection, similar to a sliding window that moves with a robot and stores surface data.

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring.

This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure.

A sparse reconstruction algorithm for ultrasonic images in nondestructive testing.

Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, ω-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan-about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR).