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Corrosion negatively impacts the functionality of civil structures. This paper introduces a new methodology that combines the fractality of vibration signals with a data processing stage utilizing autoencoders to detect corrosion damage in a truss-type bridge. Firstly, the acquired vibration signals are analyzed using six fractal dimension (FD) algorithms (Katz, Higuchi, Petrosian, Sevcik, Castiglioni, and Box dimension). The obtained FD values are then used to generate a gray-scale image. Then, autoencoders analyze these images to generate a damage indicator based on the reconstruction error between input and output images. These indicators estimate the damage probability in specific locations within the structure. The methodology was tested on a truss-type bridge model placed at the Vibrations Laboratory from the Autonomous University of Queretaro, Mexico, where three damage corrosion levels were evaluated, namely incipient, moderate, and severe, as well as healthy conditions. The results demonstrate that the proposal is a reliable tool to evaluate the condition of truss-type bridges, achieving an accuracy of 99.8% in detecting various levels of corrosion, including incipient stages, within the elements of truss-type structures regardless of their location. [ABSTRACT FROM AUTHOR]

The use of computer numerical control machine tools allows the industries to reduce costs and operating time while increasing the quality of the manufactured products. However, any malfunction in the equipment can partially affect the manufacturing process or even provoke the complete production interruption. For this reason, studies about phenomena that could affect the machinery are of interest for the manufacturing industry. In this sense, any disturbance in the electric system could either damage or affect the correct machine operation. The contribution of this work is to estimate the repercussion of voltage sags in conventional turning processes. This study is centered in the parameters of spindle motor torque and workpiece rugosity. A methodology and an analysis procedure are proposed to achieve the mentioned task. The methodology consists of estimating the values of power and torque during the electrical disturbance and establishing their relationship with the surface roughness index, Ra. The input parameters are the voltage, current and spindle speed that are monitored in a computer numerical control lathe affected by different levels of sags; also, the measurement of the workpiece surface roughness is used in the methodology. The analysis consists of calculating torque and rugosity variations due to induced voltage sags in order to find the effects of the sags in the machine performance. Results show that there exists a relationship between the voltage sags, the variations in torque and the rugosity. [ABSTRACT FROM PUBLISHER]

Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works. [ABSTRACT FROM AUTHOR]