Traditional Raman distributed optical sensor (DTS) exhibits a low-pass characteristic that causes sharp temperature changes to be over smoothed. This behavior can be modeled as the convolution of the real temperature profile with the DTS impulse response. This paper presents a deconvolution algorithm developed to improve the spatial resolution of a Raman DTS system. The algorithm is based on a linear DTS model and total variation regularization. The main advantage is the ability to correctly reconstruct hot regions with dimensions down to 15 cm, which represents a resolution gain of up to six times when compared with the DTS spatial resolution of 1 m. We present simulations and experimental results demonstrating the efficacy of the proposed method.

From wells to refining plants and distribution networks, operations in the oil and gas industry involve the transport of multiphase mixtures through long pipelines. One of the most representative patterns is the so-called slug flow, which is characterized by the intermittent passage of gaseous and liquid structures. The monitoring of slug flows allows for proper control and safety in many operations in the oil and gas industry. In recent years, Distributed Acoustic Sensing (DAS) has emerged as a promising technology for industrial use and is applied here for flow monitoring. Namely, this paper introduces the use of DAS for the measurement of slug flow. A number of flow configurations under controlled conditions are investigated. Measurements were carried out in a 9 m-horizontal pipeline section in a flow-loop laboratory. Fiber-optics were helically wrapped along the outer surface of the pipeline to promote higher sensitivity at a higher spatial resolution space. The velocity of the gas-phase given by the elongated bubbles velocity is estimated from DAS data through radial integration in k-f space and distributed cross-correlation methods. Using measurements from a commercial conductance-based sensor as a reference, the results showed RMSD% of 8.34% and 12.18%, respectively. The cross-correlation method has the clear advantage of being able to yield (spatial) distributed data.

Recently, specially crafted unidimensional optimization has been successfully used as line search to accelerate the overrelaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA) for computed tomography. In this paper, we extend the use of fast line search to the monotone fast iterative shrinkage-threshold algorithm (MFISTA) and some of its variants. Line search can accelerate the FISTA family considering typical synthesis priors, such as the ℓ ₁ -norm of wavelet coefficients, as well as analysis priors, such as anisotropic total variation. This paper describes these new MFISTA and OMFISTA with line search, and also shows through numerical results that line search improves their performance for tomographic high-resolution image reconstruction.

This paper presents a new method for thermal imaging of hydroelectric generators stators. The method is based on distributed temperature optical fiber sensing using Raman scattering. The thermal image is generated by combining the information of temperature and the spatial position of the sensor with the 3-D model of the structure. Regarding the use of conventional sensors, such as resistance temperature detector or PT100, the main advantage is the possibility to identify temperature variations over the entire stator surface. The results were obtained over a 22-h test with a 200-MW hydroelectric generator. The produced thermal images showed a great potential for monitoring the temperature distribution in the stator according to the generator load. The new method can contribute for identification of fault in the structure insulation, which when early identified can reduce damage caused by short circuit in the stator windings.

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, !-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).