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The article presents a method for diagnosing cycloidal gear damage on a laboratory stand. The damage was simulated by removing the sliding sleeves from two adjacent external pins of the cycloidal gearbox. Damage to the sliding sleeves may occur under operating conditions and can lead to the destruction of the gear unit. Hence, early detection is essential. Signals from torque sensors, rotational speed sensors and vibration acceleration sensors of input and output shafts for various rotational speeds and transmission loads were recorded. The frequency analysis of these signals was carried out. Due to the fluctuation of the rotational speed, the frequency spectrum gives an approximate picture and is not useful in detecting this type of damage. The statistical characteristics of the signal were determined. However, only statistical moments of higher orders, such as kurtosis, are sensitive to the tested damage. Therefore, the use of multifractal analysis of the vibration signal using the wavelet leader method (WLMF) was considered. Then log-cumulants of the multifractal spectrum were selected as the new signal features.

This proof-of-concept (PoC) study presents a pipeline made by two blocks: 1. the identification of the network that generates interictal epileptic activity; and 2. the study of the time course of the electrical activity that it generates, called neurodynamics, and the study of its functional connectivity to the other parts of the brain. Network identification is achieved with the Functional Source Separation (FSS) algorithm applied to electroencephalographic (EEG) recordings, the neurodynamics quantified through signal complexity with the Higuchi Fractal Dimension (HFD), and functional connectivity with the Directed Transfer Function (DTF). This PoC is enhanced by the data collected before and after neuromodulation via transcranial Direct Current Stimulation (tDCS, both Real and Sham) in a single drug-resistant epileptic person. We observed that the signal complexity of the epileptogenic network, reduced in the pre-Real, pre-Sham, and post-Sham, reached the level of the rest of the brain post-Real tDCS. DTF changes post-Real tDCS were maintained after one month. The proposed approach can represent a valuable tool to enhance understanding of the relationship between brain neurodynamics characteristics, the effects of non-invasive brain stimulation, and epileptic symptoms.

Almost one-third of the revenues of the Additive Manufacturing (AM) machines market is generated by desktop systems. A new category of such devices are Laser Sintering (LS) machines, in which we can find a few representatives of this group. A growing interest in the use of desktop AM solutions in research and industry is visible, and therefore an evaluation of its capabilities and limitations is desirable. The presented paper focuses on comparing desktop (Sintratec S1; Sinterit Lisa) and industrial (Formiga P110) LS systems. The properties of raw materials were characterized by Scanning Electron Microscopy (SEM) and Differential Scanning Calorimetry (DSC), as well as by determining the Particle Size Distribution (PSD) and static and dynamic flowability of powder. Laser Sintering commercial sets were characterized by their mechanical properties (tensile strength), surface quality (roughness), as well as by their accuracy and porosity (computed tomography). The conducted test showed significant differences, especially in the case of material properties and manufacturing repeatability. The found differences between desktop and industrial LS systems suggest that the use of a low-budget LS system, especially for the research and production of end-use parts, has to take into account its limitations.