In this article, we propose a method for the optimal dimensional synthesis of planar parallel manipulators covering a specified workspace. The proposed method aims to reduce the dimensions of the links, while maximizing the dexterity index over the workspace and it improves the rate of forces transmission and the manipulator stiffness. We study the stiffness of the manipulator through the analysis of deformations based on the finite elements method. The optimization is performed using genetic algorithms, particle swarm optimization and a hybrid method based on the combination of both methods. As study cases, we apply the propose method to the dimensional synthesis of two planar parallel manipulators. Compare to the results obtained analyzing a manipulator with links length within the design space, yet not optimal, the obtained manipulators through the proposed method meet the requirements of workspace while also showing an improved performance in terms of dexterity, forces transmission and stiffness.

Vibration-condition monitoring aims to detect bearing damages of rotating machinery’s incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior.

An estimation method of the combustion chamber pressure, in an internal combustion engine, based on the processing of the vibration (acceleration) signal of the cylinder head, at constant speed and no load conditions is presented in this paper. The model is created based on the comparison of the vibration and pressure signals around the peaks of highest vibration, after a preprocessing and filtering of the signal using the most similar frequency bands between the sources. A polynomial regression is used between the selected data points to generate the resulting model relating pressure and vibration (and average rotational speed per cycle, calculated based on the vibration peaks). The model is tested with measurements from two spark ignited engine test benches: a single cylinder engine and a four-cylinder engine. The resulting model has very low computational cost and can provide a very accurate estimation of general shape and magnitude of the pressure trace, but does not reflect strongly cycle by cycle variations. Testing the Normalized Root Mean Square Error (NRMSE), where the best value is 100 % the single cylinder engine scores were 63.52 % and 20.02 % for the points before and after the vibration peak. For the four-cylinder engine those values were: 82.47 % and 28.27 % respectively.

Pressure is one of the essential variables to give information about engine condition and monitoring. Direct recording of this signal is complex and invasive, while angular velocity can be measured. Nonetheless, the challenge is to predict the cylinder pressure using the shaft kinematics accurately. In this paper, a time-delay neural network (TDNN), interpreted as a finite pulse response (FIR) filter, is proposed to estimate the in-cylinder pressure of a single-cylinder internal combustion engine (ICE) from fluctuations in shaft angular velocity. The experiments are conducted over data obtained from an ICE operating in 12 different states by changing the angular velocity and load. The TDNN’s delay is adjusted to get the highest possible correlation-based score. Our methodology can predict pressure with an R2 $>0.9$, avoiding complicated pre-processing steps.

This study introduces a novel methodology integrating computer vision, visual servo control, and the Kalman Filter to precisely estimate object locations for a 3-RRR planar type parallel manipulator. Through kinematic analysis and the development of a vision system using color indicators, the research enhances the ability of the manipulator to track object trajectories, especially in cases of occlusion. Employing Eye-to-Hand visual servo control, the research further refines the visual orientation of the sensor for optimal end effector and object identification. The incorporation of the Kalman Filter as a robust estimator for occluded objects underscores the predictive accuracy of the system. Results demonstrate the effectiveness of the methodology in trajectory generation and object tracking, with potential implications for improving robotic manipulators in dynamic environments. This comprehensive approach not only advances the fields of kinematic control and visual servoing but also opens new avenues for future research in complex spatial manipulations.