Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

The paper presents a study of the vibrations in the load cell response of automatic weight fillers for fluids, due to the dynamics of the system. The aim is to characterize vibratory phenomena through both experimental and model-based analysis, in order to identify the main causes and identify compensation strategies. Two test campaigns were conducted, on a test bench and on a sixteen stations machine, with the simultaneous acquisition of acceleration signals and load cell signals. A detailed sensitivity analysis based on experimental data, as many system parameters vary, has been developed. For the system modelling, a one Degree of Freedom (1 DoF) model, with lumped parameters and time-variant mass, including fluidic forces, was considered and numerically implemented. Genetic algorithms were used for the identification problems in the model-based analysis. The model allowed a deeper understanding of the phenomena that occur, showing promising results for the vibration prediction in a compensation process.

Motion planning in robotic systems, particularly in industrial contexts, must balance execution speed, precision, and safety. Excessive accelerations, especially centripetal ones in high, curvature regions, can cause vibrations, reduce tracking accuracy, and increase mechanical wear. This paper presents an off-line motion planning method that integrates curvature-based velocity modulation with jerk- and acceleration-limited S-curve profiles. The approach autonomously adjusts the speed along a predefined path according to local curvature by planning the motion at piecewise constant velocity and ensuring compliance with dynamic constraints on jerk, acceleration, and velocity. A non-linear filter tracks the velocity reference and smooths transitions while maintaining fluid motion, automatically adjusting velocity based on path curvature, ensuring smooth S-curve trajectories without requiring manual intervention. By jointly addressing geometric feasibility and dynamic smoothness, the proposed method reduces execution time while minimizing vibrations in applications involving abrupt curvature variations, as confirmed by its application to planar and spatial trajectories with varying curvature complexity. The method applies to smooth parametric trajectories and is not intended for paths with tangent discontinuities. The simulation results confirm full compliance with the imposed acceleration and jerk limits; nevertheless, future work will include experimental validation on realistic process trajectories and a quantitative performance assessment.

The present paper analyses the potential dynamic performance of a novel redundant SCARA robot, currently at the stage of a functional design proposed by a renowned robot manufacturer. The static and dynamic manipulability of the new concept is compared with the conventional model of the same manufacturer by means of computer simulation in typical pick and place tasks arising from industry. The introduction of a further revolute joint in the SCARA robot kinematics leads to some improvements in the kinematic and dynamic behaviour at the expense of a greater complexity. In this paper, the potential of a redundant SCARA architecture in cutting cycle-times is investigated for the first time in performing several tasks. It is shown that, in order to exploit the possible enhancements of the redundant structure, the whole manipulator, mechanics and control must be redesigned according to specific tasks aiming at the optimization of their cycle-time.

Cam follower mechanisms are widely used in automated manufacturing machinery to transform a rotary stationary motion into a more general required movement. Reverse engineering of cams has been studied, and some solutions based on different approaches have been identified in the literature. This article proposes an innovative method based on the use of an evolutionary algorithm for the identification of a law of motion that allows for approximating in the best way the motion or the sampled profile on the physical device. Starting from the acquired data, through a genetic algorithm, a representation of the movement (and therefore of the cam profile) is identified based on a type of motion law traditionally used for this purpose, i.e., the modified trapezoidal (better known as modified seven segments). With this method it is possible to estimate the coefficients of the parametric motion law, thus allowing the designer to further manipulate them according to the usual motion planning techniques. In a first phase, a study of the method based on simulations is carried out, considering sets of simulated experimental measures, obtained starting from different laws of motion, and verifying whether the developed genetic algorithm allows for identifying the original law or approximating one. For the computation of the objective function, the Euclidean norm and the Dynamic Time Warping (DTW) algorithm are compared. The performed analysis establishes in which situations each of them is more appropriate. Implementation of the method on experimental data validates its effectiveness.