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To avoid structural failures it is of critical importance to detect, locate and quantify impact damage as soon as it occurs. This can be achieved by impact identification methodologies, which continuously monitor the structure, detecting, locating, and quantifying impacts as they occur. This article presents an improved impact identification algorithm that uses principal component analysis (PCA) to extract features from the monitored signals and an algorithm based on linear approximation with maximum entropy to estimate the impacts. The proposed methodology is validated with two experimental applications, which include an aluminum plate and an aluminum sandwich panel. The results are compared with those of other impact identification algorithms available in literature, demonstrating that the proposed method outperforms these algorithms. [ABSTRACT FROM AUTHOR]

In this study, GMAW and CMT welding technologies were evaluated in terms of their technological lifecycles based on their patent datasets together with the S-curve concept, and the joints were evaluated in terms of their welding characteristics. To predict the future trends for both technologies, different models based on the time-series and growth-curve methods were tested. From a process point of view, the results showed better performance and stability for the CMT process based on the heat input to the base material and the frequency of the short circuits. The temperature distribution in the sample revealed that the GMAW process delivers higher values and, consequently, greater heat transfer. Regarding the technological lifecycle, the analyses revealed that the CMT welding process, despite being recent, is already in its mature phase. Moreover, the GMAW welding process is positioned in the growth phase on the S-curve, indicating a possibility of advancement. The main findings indicated that through mathematical modelling, it is possible to predict, in a precise way, the inflection points and the maturity phases of each technology and chart their trends with expert opinions. The new perspectives for analysing maturity levels and welding characteristics presented herein will be essential for a broaden decision-making market process. [ABSTRACT FROM AUTHOR]

The aim of the article is presentation of the testing methodology and results of examination the probabilistic model of the measurement process. The case study concerns the determination of the risk of an incorrect decision in the assessment of the compliance of products by measurement. Measurand is characterized by the generalized Rayleigh distribution. The model of the measurement process was tested in parallel mode by six risk metrics. An undesirable effect in the reconstruction building block of the model was detected, consisting in the distortion of probability distribution at the edges of the measuring range. The paper gives guidelines on how to use the model, to obtain the analytical risk assessment consistent with the results of the Monte Carlo method. The study can be useful in product quality control, test design, and fault diagnosis. [ABSTRACT FROM AUTHOR]

The Generalized Renewal Process (GRP) is a probabilistic model for repairable systems that can represent the usual states of a system after a repair: as new, as old, or in a condition between new and old. It is often coupled with the Weibull distribution, widely used in the reliability context. In this paper, we develop novel GRP models based on probability distributions that stem from the Tsallis’ non-extensive entropy, namely the q-Exponential and the q-Weibull distributions. The q-Exponential and Weibull distributions can model decreasing, constant or increasing failure intensity functions. However, the power law behavior of the q-Exponential probability density function for specific parameter values is an advantage over the Weibull distribution when adjusting data containing extreme values. The q-Weibull probability distribution, in turn, can also fit data with bathtub-shaped or unimodal failure intensities in addition to the behaviors already mentioned. Therefore, the q-Exponential-GRP is an alternative for the Weibull-GRP model and the q-Weibull-GRP generalizes both. The method of maximum likelihood is used for their parameters’ estimation by means of a particle swarm optimization algorithm, and Monte Carlo simulations are performed for the sake of validation. The proposed models and algorithms are applied to examples involving reliability-related data of complex systems and the obtained results suggest GRP plus q-distributions are promising techniques for the analyses of repairable systems. [ABSTRACT FROM AUTHOR]