Electroencephalograms provide a non-invasive and effective method for studying emotion recognition and developing Artificial Intelligence (AI) models to understand human behavior and decision-making processes. This study involved testing several machine learning classification kernels to develop an accurate emotion recognition model capable of classifying emotions stimuli such as "Boring," "Calm," "Happy," and "Fear" during gameplay. An emotion classifier was assessed using the publicly available database for an emotion recognition system based on EEG signals and various computer games (GAMEEMO). The signal processing method, referred to as Regression EEG (REGEEG), involves an efficient electrode pairing selector developed for EEG signal processing using a regression algorithm, rotation matrices, director vectors, and robust statistical and polynomial feature extraction. REGEEG and feature extraction methods were evaluated with 28 machine learning kernels, resulting in five kernels with classification performance above 80%, with the K-Nearest Neighbors (k-NN) based model outperforming the rest (achieving over 95% accuracy, F1-Score, and kappa-score). REGEEG performance was further validated using 30 Cross-Validation (CV) folders and 28 in the Leave-one Subject-out (LoSo) technique without impacting the average classification performance. The classification highlights revealed low variance in the CV, while the LoSo approach helped identify outliers in the GAMEEMO dataset. Furthermore, the EEG pair channels selector demonstrates superior performance in classification, indicating a correlation between features and each processed pair of channels.

Gearboxes are important components in industrial applications and its condition monitoring is relevant in industries for reducing costs and minimizing maintenance downtime. Diagnosing wear in gearboxes saves time to prepare appropriate corrective actions, and to ensure that the system does not deteriorate critically. Nowadays, for condition monitoring in gearboxes, vibration analysis is commonly used due to its high reliability. In this work, a reliable methodology for diagnosing different levels of wear in a gearbox through vibration signals, and supported by a theoretical model, is proposed. The theoretical model is based on calculating the characteristic frequencies of the gearbox, with the aim for locating the spectral components of the faults in the vibration signal. Experimentation is done to a healthy gearbox and three wear levels. Results show the reliability of this method that makes it suitable to be used in diagnosing industrial machinery such as in automotive manufacturing applications.

Squirrel-cage induction motors (IM) are the most popular motors used in industry, consuming around 85% of the power in industrial plants. IM are critical components for most industries, and an early detection of induction motor faults has been a main subject of investigation during last years. Broken rotor bars in induction motors are among the major failures that are desirable to detect at early stages because this failure significantly increases power consumption and is responsible for further damage to the machinery. However, the detection of partially-broken rotor bars at an early stage is not so easily achieved. Therefore, it is necessary to use suitable condition monitoring accompanied with signal processing techniques to detect a partially-broken rotor bar. Thus, this paper focuses on a study and evaluation of a condition monitoring method based on the complete ensemble empirical mode decomposition CEEMD of the stator current; aiming to detect partially broken, one, and two broken rotor bars in a line fed EVI during the startup current transient. From the experimental results, the CEEMD is able to graphically show, in the time domain, the physical effect of the fault evolution during the startup current as an abrupt change in the envelope, and finally, these abrupt changes are quantified in a way that the level of fault can be identified correctly. Also, the CEEMD has the advantage of being an online diagnosis method, which does not require knowing a priori the motor current of the healthy condition.

Nowadays, power quality monitoring is a very important subject. The growing utilization of nonlinear loads such as rectifiers considerably distorts the shape of current and voltage signals affecting the power quality of the grid. Therefore, harmonic estimation and compensation are important tasks for improving power quality indexes in electric systems. In this work, the feasibility of using the discrete Fourier square-wave transform (DFSWT) as a fast processing engine, with low power consumption, for online harmonic component estimation is assessed. From obtained results, the current harmonic components demanded by the nonlinear loads were properly identified through DFSWT and compensated utilizing a shunt active power filter (S-APF) in all treated cases, obtaining a well-shaped sinusoidal current signal in the grid front-end.

Asymmetrical loads in power systems leads to unbalance voltage/current signals and consequently, adverse effects such as overheating, lifetime, torque, and speed reduction, among others. In order to compensate the level of unbalance and avoid the above mentioned effects, the instantaneous unbalance and symmetrical components estimations have to be carried out. In this work, a methodology for estimating the instantaneous unbalance and symmetrical components through the Hilbert transform (HT) in three-phase voltage and current systems is proposed. First, a bandpass filter is used to remove the harmonics/interharmonics components; afterwards, the HT is applied to obtain the instantaneous amplitude and phase of each voltage/current signal. Finally, the symmetrical components and unbalance percentage are computed. The proposed approach is validated and tested through synthetic signals and under real operating conditions. Besides, a field programmable gate array (FPGA)-based implementation is developed as a low-cost and portable System-on-Chip (SoC) solution.

Nowadays industry pays much attention to prevent failures that may interrupt production with severe consequences in cost, product quality, and safety. The most-analyzed parameters for monitoring dynamic characteristics and ensuring correct functioning of systems are electric current, voltage, and vibrations. System-on-chip (SoC) design is an approach to increase performance and overcome costs during equipment monitoring. This work presents the design and implementation of a low-cost SoC design that utilizes reconfigurable hardware and a customized embedded processor for time-frequency analysis on industrial equipment through short-time Fourier transform and discrete wavelet transform. Three study cases (electric current supply to an induction motor during startup transient, voltage supply to an induction motor through a variable speed drive, and vibration signals from industrial-robot links) show the suitability of the proposed monitoring system for time-frequency analysis of different signals in distinct industrial applications, and early diagnosis and prognosis of abnormalities in monitored systems.

Speed observation of rotating machines under different operational conditions is required in applications from fault detection to traction. Sensorless rotating speed observation has become popular in recent years. The MRAS rotor-flux-oriented and synchronous models are common schemes used for sensorless speed observation. These techniques are affected under low rotating frequencies and variable load conditions. This work presents an experimental analysis on the performance of both schemes during rotating speed estimation under different rotational frequencies and variable-load conditions. Obtained results show performance deficiencies on their estimations according to the rotational speed and load. A double scheme methodology that mixes the MRAS-based and the synchronous speed observers through a compensation function is proposed. Its performance analysis indicates an improvement on the estimated speed at different rotational frequencies with variable load. An FPGA-based implementation is developed as a low-cost, portable system-on-chip solution for real-time online estimation of rotating speed on induction motors.

Preventive maintenance is one of the major concerns in modern industry where failure detection on motors increases the useful life cycle on the machinery. Bar breakage is one of the most common failures on motors and their condition monitoring is a mandatory task for industries. Previous works for bar breakage detection are based on off-line current or vibration analysis through the spectrum, but their detectability is compromised under certain operating conditions. The novelty of this work is the proposal of a correlation algorithm that combines current and vibration spectra to enhance detectability where other works fail. Current and vibration monitoring are non-invasive methods and they are preferred for on-line analysis. The proposed correlation between current and vibration allows to detect the broken bar condition on induction motors when no load is applied to the motor, which is the condition where other reported works fail to detect. The contribution of this work is that the proposed algorithm, though complex in computational load, is implemented into a low-cost FPGA (Spartan-3 XC3S1000) that gives a special purpose SOC solution for on-line operation, thanks to the development of a special purpose hardware signal processing unit. The developed FPGA based algorithm was tested for different bar breakage conditions in the induction motors and with several loads, giving as a result a better detectability for the motor failure under conditions where other methodologies fail, with the additional advantage of being a low-cost SOC solution for on-line detection