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The present study aims to introduce an innovative entropy-based denoising technique to enhance the accuracy of TDoA (Time Difference of Arrival) in source localization techniques based on acoustic emissions (AE). The approach focuses on the challenging scenario of industrial pipelines in energy and transportation systems characterized by high level of noise. Conventional methods for estimating the TDoA of AE sources, are generally hindered by external interferences in near-industrial scenarios, leading to distorted AE signals. The proposed approach encompasses a comprehensive analysis of signal waveforms, integrating local entropy to effectively compensate for the presents of noise, and employing the Akaike Information Criterion (AIC) to estimate the TDoA. To evaluate the performance of the proposed method, a two-stage experimental campaign was conducted on a pressurized hydraulic circuit. The first stage involved verifying the statistical reliability of the proposed algorithm by employing the Hsu-Nielsen test (pencil lead break) at multiple points along a series of pipes. In the second stage, an ad-hoc system was devised to induce accelerated corrosion on the same piping while capturing raw acoustic emission waveforms. Various levels of Gaussian noise, reflecting distinct signal-to-noise ratios, were added to the recorded raw waveforms to simulate diverse industrial interference scenarios. The findings illustrate significant enhancement in the accuracy and repeatability of AE source localization. The proposed entropy-based denoising technique showcases substantial potential for advancing damage detection and localization within the Process & Power Industry.

This paper explores the potential of a low-cost, advanced video-based technique for the assessment of structural damage to buildings caused by seismic loading. A low-cost, high-speed video camera was utilized for the motion magnification processing of footage of a two-story reinforced-concrete frame building subjected to shaking table tests. The damage after seismic loading was estimated by analyzing the dynamic behavior (i.e., modal parameters) and the structural deformations of the building in magnified videos. The results using the motion magnification procedure were compared for validation of the method of the damage assessment obtained through analyses of conventional accelerometric sensors and high-precision optical markers tracked using a passive 3D motion capture system. In addition, 3D laser scanning to obtain an accurate survey of the building geometry before and after the seismic tests was carried out. In particular, accelerometric recordings were also processed and analyzed using several stationary and nonstationary signal processing techniques with the aim of analyzing the linear behavior of the undamaged structure and the nonlinear structural behavior during damaging shaking table tests. The proposed procedure based on the analysis of magnified videos provided an accurate estimate of the main modal frequency and the damage location through the analysis of the modal shapes, which were confirmed using advanced analyses of the accelerometric data. Consequently, the main novelty of the study was the highlighting of a simple procedure with high potential for the extraction and analysis of modal parameters, with a special focus on the analysis of the modal shape’s curvature, which provides accurate information on the location of the damage in a structure, while using a noncontact and low-cost method.

Equipment ageing containing hazardous substances is a criticality for the safe as leakages can give rise to serious accidental scenarios such as fires, explosions, and dispersions of chemicals into the environment. The attention towards this issue grows over the time and becomes significant when the equipment is reaching the end of its lifetime. Ageing management usually includes targeted and in-depth integrity controls performed at regular intervals. Atmospheric storage tanks of hydrocarbons are particularly critical as the control of the bottom integrity requires not only the service interruption, but also the need for the inspectors to stay long time in hazardous environments during the time required for the thickness measurements. One of the main causes of perforation is corrosion. The whole bottom is in any case subjected to significant stress, therefore, the thickness of the bottom plates must not be below a threshold value to avoid leakages. The aim of this work is to investigate the entire bottom of an atmospheric storage tank to identify the most susceptible locations with respect to the deterioration due to localised corrosion. The assessment of the probability of perforation of the bottom indicated the annular ring as the most critical. To achieve this objective, the latest inspection data have been used that allowed identifying the probabilities of leakage for the various homogeneous areas identified in the bottom. An estimate of the time to reach the critical thickness has also been given based on a recent approach that combines the extreme value theory of with the Bayes theorem.

The present study aims to introduce an innovative entropy-based denoising technique to enhance the accuracy of TDoA (Time Difference of Arrival) in source localization techniques based on acoustic emissions (AE). The approach focuses on the challenging scenario of industrial pipelines in energy and transportation systems characterized by high level of noise. Conventional methods for estimating the TDoA of AE sources, are generally hindered by external interferences in near-industrial scenarios, leading to distorted AE signals. The proposed approach encompasses a comprehensive analysis of signal waveforms, integrating local entropy to effectively compensate for the presents of noise, and employing the Akaike Information Criterion (AIC) to estimate the TDoA. To evaluate the performance of the proposed method, a two-stage experimental campaign was conducted on a pressurized hydraulic circuit. The first stage involved verifying the statistical reliability of the proposed algorithm by employing the Hsu-Nielsen test (pencil lead break) at multiple points along a series of pipes. In the second stage, an ad-hoc system was devised to induce accelerated corrosion on the same piping while capturing raw acoustic emission waveforms. Various levels of Gaussian noise, reflecting distinct signal-to-noise ratios, were added to the recorded raw waveforms to simulate diverse industrial interference scenarios. The findings illustrate significant enhancement in the accuracy and repeatability of AE source localization. The proposed entropy-based denoising technique showcases substantial potential for advancing damage detection and localization within the Process & Power Industry. [ABSTRACT FROM AUTHOR]

The assessment of the integrity of the atmospheric storage tanks is needed to prevent releases of hazardous materials. In particular, the control of the localised losses of thickness, due to pitting or other phenomena, is essential for the bottom of the storage tanks, because these could lead to the perforation of the plates and leakages of materials with severe environmental consequences. Different techniques are available for the integrity measurement, but they always require the tanks is out of service, therefore, it has to be empty and reclaimed. Measurements can be repeated only after some years of further service. Nevertheless, the estimation of the perforation probability is important to assess the risk of major accidents as well as the environmental risk, associated with possible leakages. Unfortunately, discrete thickness measurements cannot determine with certainty the maximum corrosion depth of the bottom, where the materials usually exhibit localised corrosion in the form of pits. For this reason, to assess the perforation probability of the storage tank, a probabilistic approach based on the extreme value theory must be used. This work describes a model predicting the maximum expected damage with respect to the deterioration mechanisms affecting the bottom of the atmospheric storage tanks containing naphtha based solvents. The model is based on an analysis of the bottom damage, obtained by collecting thickness measurements. The application to a real case-study is presented.

Background:Lymphedema-cholestasis syndrome (LCS; Aagenaes syndrome) is a rare autosomal recessive disorder, characterized by 1) neonatal intrahepatic cholestasis, often lessening and becoming intermittent with age, and 2) severe chronic lymphedema, mainly

In Europe, after the Directive 2012/18/UE entered into force, the operators of establishments at major hazard accident are required to evaluate and manage equipment ageing through a detailed planning with the aim to control the integrity and prevent unwanted losses of hazardous materials as well as occupational risk. It is essential to estimate the equipment health and predict its remaining useful lifetime. Reliable estimates make possible to achieve a safe and efficient conduction of normal operations. To contribute to the achievement of these aims, a system is being developed, which is based on prognostic modelling and augmented reality for the prediction of the equipment degradation and the remaining useful lifetime. The system consists of hardware and software, in which the forecasting models draw information from a network of sensors installed on the equipment and from a database containing the history of inspections. The model integrates also information about deterioration mechanisms. This paper presents a preliminary study carried out for the derivation of the degradation model with respect to the mechanisms affecting atmospheric storage tanks, namely the internal corrosion of metal structures exposed hydrocarbons and the backside corrosion of the bottom floors. A comparison between the proposed method and the EEMUA standard is also given.

In the last years there has been a considerable development of wireless sensors based on technologies that allow machine-to-machine communication, thus to talk about the "Internet of Things" IoT and "smart systems". Many industries, such as manufacturing and construction, are adopting smart technologies for improving all the production activities, including safety. The research laboratories have developed many Smart Safety System SSS. Many Commercial provider are already selling SSS even though the actual benefits are not yet so clear. The paper focuses on the establishments that handle hazardous chemical materials and fall under the Directive 2012/18/EU Seveso III, which requires a quantitative assessment of the risk. The goal of the paper is to assess how the use of SSS applied to critical equipment can improve the safety, reducing the likelihood of incidents occurrence or mitigating the consequences related to the loss of containment of hazardous substances. The paper proposes a method, based on some primary criteria, useful for the stakeholders to address the choice of the SSS and to assess the benefits for risk reduction. Several examples, described in the paper, show solutions of smart systems able to monitor critical equipment on more frequent mechanisms of damage, including corrosion, erosion, thinning, structural defects, and vibration anomalies. The SSS provide the operators with a huge amount of data, which may be integrated with adequate information and sound knowledge, so that to make possible a dynamic management of the risk.