In this work, we investigate non-Hermitian (NH) elastic waveguides with periodically applied proportional feedback efforts, implemented through piezoelectric sensors and actuators. Using one-dimensional spectral models for longitudinal motion, it is shown that dispersion diagrams of this family of structures exhibit non-reciprocal imaginary frequency components, manifesting as wave attenuation or amplification along opposite directions for all Bloch bands (BBs). The effects of positive and negative proportional feedback, as well as local and non-local actuation are investigated. Overall, switching the sign of the feedback effort inverts the amplification direction, while increasing the degree of non-locality produces splitting of the BBs into multiple bands with interchanging non-reciprocal behavior. Furthermore, skin modes localized at the boundaries of finite domains are investigated and successfully predicted by the winding number of the complex dispersion bands. These results contribute to recent efforts in designing metamaterials with novel properties associated with the physics of non-Hermitian systems, which may find fruitful technological applications relying on vibration and noise control, wave localization, filtering and multiplexing.

This article presents machinery fault diagnosis in electric motors through mechanical vibration monitoring. Using a commercial machinery fault simulator, two fiber Bragg grating (FBG) accelerometers are evaluated under one healthy condition and nine different fault conditions, including rotors with broken bars, faulted bearings, misalignment, and imbalance. The mechanical vibration spectrum for each fault condition is assessed by analyzing the Bragg wavelength shift modulated by the acceleration. Offline analysis is carried out to identify the mechanical vibration pattern characteristics. In this preliminary analysis, the high-order frequency characteristics of faulted-bearing conditions and the slip frequency characteristics of broken rotor bar conditions were well-identified by both accelerometers. Following this analysis, this article uses two concurrent approaches to indicate the feasibility of classifying and/or clustering the data from supervised or unsupervised approaches. To that extent, the sensor system is first analyzed using a supervised approach from the $k$ -nearest neighbors (kNN) approach for fault condition classification, where the proposed supervised algorithm resulted in an accuracy of 100% for the fault condition estimation. After the classification analysis, data clustering is also proposed. In the unsupervised approach, the highest score for seven clusters was observed. Besides in this work, nine fault conditions were studied, there are two fault conditions where the positions of the accelerometers were interchanged for a sensor’s acceleration sensitivity evaluation purpose (maintaining the same fault condition). Finally, the results obtained in this work have shown the feasibility of the use of FBG-based accelerometers in different fault conditions under supervised and unsupervised approaches.

Degenerative diseases and injuries that compromise hand movement reduce individual autonomy and tend to cause financial and psychological problems to their family nucleus. To mitigate these limitations, over the past decade, hand exoskeletons have been designed to rehabilitate or enhance impaired hand movements. Although promising, these devices still have limitations, such as weight and cost. Moreover, the movements performed are not kinematically compatible with the joints, thereby reducing the achievements of the rehabilitation process. This article presents the biomimetic design of a soft hand exoskeleton actuated using artificial tendons designed to achieve low weight, volume, and cost, and to improve kinematic compatibility with the joints, comfort, and the sensitivity of the hand by allowing direct contact between the hand palm and objects. We employed two twisted string actuators and Bowden cables to move the artificial tendons and perform the grasping and opening of the hand. With this configuration, the heavy part of the system was reallocated to a test bench, allowing for a lightweight set of just 232 g attached to the arm. The system was triggered by the myoelectric signals of the biceps captured from the user’s skin to encourage the active participation of the user in the process. The device was evaluated by five healthy subjects who were asked to simulate a paralyzed hand, and manipulate different types of objects and perform grip strength. The results showed that the system was able to identify the intention of movement of the user with an accuracy of 90%, and the orthosis was able to enhance the ability of handling objects with gripping force up to 1.86 kgf.

Machine learning methods used in Structural Health Monitoring applications still have generalization difficulties among structures, even when structures are nominally and topologically similar. The data sets present divergences between their probability distributions that do not allow the model’s generalization for damage detection. This issue is even more complex in situations where one wants to quantify damage levels through data sets collected from different structures. Transfer learning methods offer a solution to overcome those limitations, using relevant information from a labeled structure (source domain) to assist the analysis of another structure (target domain) under unknown conditions. Therefore, this paper proposes the use of transfer component analysis to mitigate divergences between the model/structure’s features, and the label consistency requirement is applied in combination with a Gaussian process regression model for damage quantification. The effectiveness of the estimated model improves when the labels consistency between domains is achieved, indicating the current damage level in the structure when the regression model achieves its best performance (lowest error). The proposed methodology is applied on the benchmark data of a three-story building structure from the Los Alamos National Laboratory using the knowledge from its numerical model under several conditions, where the complete information of its behavior is available. The results compare the analysis in the original space and after applying the proposed methodology, demonstrating an improvement of the performance in the damage detection and quantification steps.