Monitoring saltwater corrosion of steel using ultrasonic coda wave interferometry with temperature control

Assessing corrosion is crucial in the petrochemical and marine industries. Usual ultrasonic methods based on pulse-echo and guided waves to detect corrosion lack of precision and struggle in structures with a complex shape. In this paper, a complementary and sensitive ultrasonic method based on coda wave interferometry is presented to detect and quantify thickness loss caused by saltwater corrosion of a steel sample. The method consists in exciting the sample and measuring periodically the scattered coda signal. Correlation of the coda signal with a reference taken for the sample initial state permits the monitoring of corrosion spread with a high accuracy. A laboratory experiment is conducted with two steel samples immersed in saltwater with coda and temperature measured simultaneously. One of the samples is protected from corrosion and is used as a control sample to determine the influence of temperature on the coda signals. It is shown that the coda signals on the corroded sample can be temperature-corrected using the temperature measurement only. A control sample is not needed. A good correlation is found between a parameter quantifying the stretching of the coda over time and the corrosion surface, which is monitored with a camera. Finally, a simple theoretical model of coda signal is proposed to quantify the real-time average corrosion rate during the experiment with a sub-micrometric precision. The estimated final average corrosion depth is validated by independent depth profile measurements. The uncertainties and sensitivity of the presented method are investigated.

Detection of overlapping ultrasonic echoes with deep neural networks

Ultrasonic Pulse-Echo techniques have a significant role in monitoring the integrity of layered structures and adhesive joints along their service life. However, when acoustically measuring thin layers, the resulting echoes from two successive interfaces overlap in time, limiting the resolution that can be resolved using conventional pulse-echo techniques. Deep convolutional networks have arisen as a promising framework, providing state-of-the-art performance for various signal processing tasks. In this paper, we explore the applicability of deep networks for detection of overlapping ultrasonic echoes. The network is shown to outperform traditional algorithms in simulations for a significant range of echo overlaps, echo pattern variance and noise levels. In addition, experiments on two physical phantoms are conducted, demonstrating superiority of the network over traditional methods for layer thickness estimation.

Towards using convolutional neural network to locate, identify and size defects in phased array ultrasonic testing

Machine learning algorithms are widely used in image recognition. In Phased Array Ultrasonic Testing (PAUT), images are typically formed through constructive and destructive superpositions of signals backscattered from flaws or geometric features. However, all PAUT data acquisition schemes require several emissions and the duration of the acquisition may be too slow in high-speed manufacturing. In this study, the Faster R-CNN was used to identify, locate and size flat bottom holes (FBH) and side-drilled holes (SDH) in an immersed test specimen using a single plane wave insonification. The training was performed on segmented and classified data generated using GPU-accelerated finite element simulations. SDH and FBH of different diameters, depths and lateral positions were included in the training set. The thickness of the test specimen was also variable. An ultrasonic phased array probe of 64 elements was simulated. All elements of the phased array probe were fired at the same time and the time traces from each element were recorded. The individual time traces were concatenated to form a matrix, which was then used in the training. This inspection scenario enables fast acquisition of data at the expense of poor lateral resolution in the resulting image. The trained neural network was initially tested using finite element simulations. Results were assessed in terms of the intersection of the union (IoU) between the ground truth geometry and the predicted geometry. With the simulated cases, the thickness of the test specimen was detected in all cases. When using a 40% IoU threshold, the detection rate of the FBH was 87% while only 20% for the SDH. The smallest detected FBH had a 0.56 wavelength depth and a lateral extent of 1.04 wavelength. Drawing a box using the -6dB drop method around the FBH always led to an IoU under 15%. On average, the lateral extent of the FBH using the -6dB method was three times larger than the diameter predicted by the proposed method. Then, the training was continued with a small augmented dataset of experiments (equivalent to 3% of the simulated dataset). In experiments, the results show that the test specimen was always correctly identified. When using a 40% IoU threshold the experimental detection rate of the FBH was 70%. The smallest detected defect in experiments had a depth of 2 wavelengths.

Time-frequency analysis to a particular type of scattering problems involving metallic-polymer tubing structures

In this paper, recent studies of backscattered acoustic signals in thinner steel-polymer tubing structures have been presented. Reassigned smoothed pseudo Wigner-Ville (rspWV) analysis has been adopted in order to diminish the cross-term effect, and achieve high resolution spectral. Vibration modes, which are associated to the resonances of circumferential waves, have been determined by using the modal isolation plan representation. At normalized frequencies below 140, an appreciable influence from the polymer coating thickness on the A₀⁺ and S₀ modes has been noticed. Furthermore, the trajectory of the A₀^- wave has been modified in the normalized frequency band 40-42. Group velocity curves of the A₀^- wave have, then, been graphically illustrated. The findings have shown a particular curvature change at reduced frequency 41 in the case of an immersed two-layer tube in water. Studies of acoustic backscattering involving steel-polymer tubing structures have confirmed the significant coupling of A₀⁺ and S₀ waves. Besides, the disappearance of the A₀⁺ resonance trajectory has been observed; which is a very important phenomenon to understand.

Development of a monitoring scheme for preventive maintenance of the cement machinery

It was underscored that the optimization of policies of preventive maintenance has become a subject of much research. This article proposes a new optimal policy of preventive maintenance for the roller shaft system. It is divided into two main sections. The first proposes a new design of the shaft where the effect of preventive maintenance is integrated, and the second is developing a new control technique adapted to the new design. In this regard, we are interested in the shaft of rollers of the rotary kilns of cement. We have noted the stresses imposed to the axis of the roller. These constraints that have stemmed from the contact between tire and its support rollers. Currently the ultrasonic inspection method of the solid shaft in this situation poses disadvantages, for example, the obligation to stop the rotary Kiln and the difficulty of detecting and sizing defects (fatigue cracks). We propose in this study another approach, we recommend opting for a hollow shaft instead of a solid shaft with a minimum diameter that allows the control system to enter hollow shaft to show for inspection purposes. This will allow preventive control, in operation by the non-destructive technique of ultrasound. The analysis shows the different results of comparison between the axis of full and hollow cylinders, having the same dimensions and the same material and ultimately the most interesting is the same operation.