The paper presents the vibration spectra of a twin-screw compressor driven at various functioning regimes. The tests are performed to assess the potential of this rotary bladed machine as energy harvesting source. It is sought to determine the optimum spots on the compressor skid where piezoelectric harvesters can be placed, meeting the operational conditions. Like most of the industrial machinery, compressors exhibit inherent vibrations and heating during operation. The pulsatory effects of the compressed air trapped between the compressor’s male and female rotors inside the compressor also give birth to harmonics. It is important to evaluate the temperatures as well since piezoelectric harvesters should preferably operate close to room temperature so as not to deteriorate the material and avoid thermal hysteresis, which can ultimately lead to the loss of piezoelectric properties. The study herein involves acquiring vibrational data and representing it graphically as vibration spectra. An analytical model is also presented for calculating the main frequencies of the compressor, validated by the experimental data measurements. We also present laboratory tests with the piezoelectric harvester tuned near the male rotor’s frequency for achieving resonance conditions. The male rotor frequency was the most stable, with the highest amplitudes and of convenient value (~83 Hz) for the tip mass required for decreasing the piezoelectric cantilever’s resonant frequency from ~210 Hz.

The precision needed in optometric measurements for the correct customization of progressive lenses usually falls short of what is required for accurate prescriptions. This usually stems from the fact that most measurements are obtained using outdated methods, employing either rulers or protractors. While there is equipment available for precise measurements, the cost of purchase and ownership is usually prohibitive. In this context, due to constant progress in high-resolution cameras along with the processing power of handheld devices, another solution has presented itself in different iterations in the past decade, as put forward by different manufacturers of optical lenses. Such a system comprises a mobile computing device with image capture and processing capabilities (tablet or smartphone), along with a marker support system to be mounted on the user’s glasses frames. Aside from cost, the ease of implementation and usage, the advantage of such a system is that the parameters, as measured, allow for better customization, since the eyewear is already in the position in which it will be used. It allows the optometrist to measure parameters such as interpupillary distance, pantoscopic angle and the curvature of the eyewear in relation to the user’s own specific shape and size. This paper proposes a model of a marker support system that is easy to use, precise, low in cost and has minimal impact on the measurements obtained by the optometrist. As such, this paper examines the steps for determining the shape needed for supports in relation to the measurements that need to be taken; a finite element analysis of the support was proposed, along with various tests and modifications that were made to the device until a specific shape and material combination was found that satisfied all of the parameters required. An experimental model of the system was produced and tested on a wide variety of glasses frames with good results, as presented in the following work.

This study evaluates the potential of a twin-screw compressor as a heat source to harness thermal energy. Thermoelectric generators are a feasible solution for microenergy harvesting from waste heat based on the Seebeck effect. Thermographic infrared images of the compressor were used to assess potential installation spots. The physical mounting of the thermoelectric modules must consider certain hindering aspects. At first, the compressor skid is subject to standards and authorizations for its components, leaving only a couple of spots for screw-mounted module installations. Another inconvenience is the bonds in any thermoelectric material causing them not to withstand lateral mechanical stress in other directions except the c-axis perpendicular to the layers. Therefore, vibration measurements have to be performed beforehand. Numerical simulations were conducted, relying on the acquired thermoelectric modules as well as on the temperature and vibration data measured on the compressor. The thermoelectric generators studied are part of a multisource piezoelectric and thermoelectric energy harvesting system under research and development.

This paper establishes an integrated framework combining self-induced vibration measurements with deep learning for vibration-based remaining useful life (RUL) prediction of mechanical frame structures in mobile robots. The main innovations comprise (1) a self-induced vibration excitation system that utilizes the robot’s drive wheels to generate controlled mechanical oscillations, using a five-sensor micro-electro-mechanical system (MEMS) accelerometer array to capture non-uniform vibration mode shapes across the robot’s structure, and (2) a processing pipeline for RUL prediction using accelerometer data and early feature fusion in two machine-learning models (long short-term memory (LSTM) and a convolutional neural network (CNN)). Our research methodology includes (i) modal analysis to identify the robot’s natural frequencies, (ii) verification platform evaluation, comparing low-cost MEMS accelerometers against a reference integrated electronic piezoelectric (IEPE) accelerometer, demonstrating industrial-grade measurement quality (coherence > 98%, uncertainty 4.79–7.21%), and (iii) data-driven validation using real data from the mechanical frame, showing that the LSTM model outperforms the CNN with a 2.61× root-mean-square error (RMSE) improvement (R² = 0.99). Our solution demonstrates that early feature fusion provides sufficient information to model degradation and detect faults early at a lower cost, offering a feasible alternative to classical maintenance procedures through combined hardware validation and lightweight software suitable for Industrial Internet-of-Things (IIoT) deployment.

This paper presents an opto-mechatronic system that can be used in the field of optometry to facilitate and improve the measurements performed to determine the parameters needed to build custom progressive lenses (PAL) and position the optical centers of the lens in the patient correct area of view. Using such device improves the anthropometric parameters measurement accuracy, depending on the activity performed by the patient (close or wide range). The system will have the function of placing in space a tablet with specially designed software that capture an image, measures and calculates the individual optometric parameters of the patient.