This paper presents an approach developed to quantify the chaotic behavior for the characterization of electromechanical systems. A technique named Signal Analysis based on Chaos using Density of Maxima (SAC-DM) is presented. This technique uses a simple peak counting algorithm in the time domain of the current signal to detect faults. To demonstrate the potential of SAC-DM, an experiment is presented where a small BLDC motor at different speeds, with a regular and an unbalanced propeller. The results demonstrated that the SAC-DM was able to detect the speed of the BLDC motor in 99.16% of the cases, and to identify the unbalanced system in 99.79% of the cases, when the speed is at 50%.

We describe the formalism of statistical irreversible thermodynamics constructed based on Zubarev’s nonequilibrium statistical operator (NSO) method, which is a powerful and universal tool for...

The three-phase induction motor (TIM) is present in most industrial processes, being the main responsible for the electromechanical conversion in the sector. The total breakdown of these machines can compromise the production of entire sectors in a manufacturing environment, hence the need for early fault detection. In this context, broken bar failures are the most common on the TIM rotor. The present work analyzes the chaotic behavior of one stator current of a TIM, with different levels of broken rotor bar failures. The characterization of the failure is performed by analyzing the behavior of the chaotic variable called density of maxima. The proposed approach has advantages over classic Motor Current Signature Analysis techniques in that it does not require long acquisition windows (in this work it was possible to perform detection using a range of 0.35 s) and enables fault detection even for motors operating under variable load or with low slip. The technique also has a lower computational cost compared to classical detection methods. The obtained results indicate that the method is also able to identify different severity levels with the TIM under nominal load.

A novel technique named Signal Analysis based on Chaos using Density of Maxima (SAC-DM) is presented to analyze Brushless Direct Current (BLDC) motors behavior. These motors are vastly used in electric vehicles, especially in Drones. The proposed approach is compared with the traditional Fast-Fourier Transform (FFT) and the experiments analyzing a BLDC motor of a drone demonstrates similar results but computationally simpler than that. The main contribution of this technique is the possibility to analyze signals in time domain, instead of the frequency domain. It is possible to identify working and faulty behavior with less computational resources than the traditional approach.

We present analytical and numerical results that demonstrate the presence of the Braess paradox in chaotic quantum dots. The paradox that we identify, originally perceived in classical networks, shows that the addition of more capacity to the network can suppress the current flow in the universal regime. We investigate the weak localization term, showing that it presents the paradox encoded in a saturation minimum of the conductance, under the presence of hyperflow in the external leads. In addition, we demonstrate that the weak localization suffers a transition signal depending on the overcapacity lead and presents an echo on the magnetic crossover before going to zero due to the full time-reversal symmetry breaking. We also show that the quantum interference contribution can dominate the Ohm term in the presence of constrictions and that the corresponding Fano factor engenders an anomalous behavior.

We investigate the spin-to-charge conversion emerging from a mesoscopic device connected to multiple terminals. We obtain analytical expressions to the characteristic coefficient of spin-to-charge conversion, which are applied in two kinds of ballistic chaotic quantum dots at low temperature. We perform analytical diagrammatic calculations in the universal regime for two-dimensional electron gas and single-layer graphene with the strong spin-orbit interaction in the universal regime. Furthermore, our analytical results are confirmed by numerical simulations. Finally, we connect our analytical findings to recent experimental measures giving a conceptual explanation about the apparent discrepancies between them.