Data acquisition is a crucial stage in the execution of condition monitoring (CM) of rotating machinery, by means of vibration analysis. However, the major challenge in the execution of this technique lies in the features of the recording equipment (accuracy, resolution, sampling frequency and number of channels) and the cost they represent. The present work proposes a low-cost data acquisition system, based on Raspberry-Pi, with a high sampling frequency capacity in the recording of up to three channels. To demonstrate the effectiveness of the proposed data acquisition system, a case study is presented in which the vibrations registered in a bearing are analyzed for four degrees of failure.

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Increasing industrial competitiveness has led to an increased global interest in condition monitoring. In this sector, rotating machinery plays an important role, where the bearing is one of the most critical components. Many vibration-based signal treatments are already being used to identify features associated with bearing faults. The information embedded in such features are employed in the construction of health indicators, which allow for evaluation of the current operating status of the machine. In this work, the use of contour maps to represent the diagnosis map of a bearing, used as a health map, is presented for the first time. The results show that the proposed method is promising, allowing for the satisfactory detection and evaluation of the severity of bearing damage. In this initial stage of the research, our results suggest that this method can improve the classification of bearing faults and, therefore, optimise maintenance processes.

Means of transport should be able to fulfil their main function safely and comfortably for travellers and drivers. The effects of vibrations on ride comfort are in the frequency range of 0.5 to 80 Hz and can be analysed using the UNE-2631 standard. This type of analysis has been conducted for several means of transport (bicycles, motorcycles, cars, trucks, etc.), but the literature on e-scooter comfort is very scarce. Existing research describes methodologies, simulation models, and a few measurements related to e-scooter comfort. This paper presents, for the first time, a comfort analysis using an Arduino-based data acquisition system at a sampling frequency of 200 Hz (higher than that in previous studies). Acceleration and speed measurements were obtained by sensorising an e-scooter with inflated wheels without any additional damping systems, which is one of the commonly used e-scooter types. In this study, the comfort for two different speeds (20 and 28 km/h), two types of pavements (pavers and asphalt), and two drivers with different weights was investigated. The results indicate the lowest comfort values for higher velocities and paver pavement. Furthermore, the comfort values were extremely low for all scenarios. In addition, the results demonstrate the necessity of using a sampling rate of at least 80 Hz for this e-scooter model.

Background Micro-mobility provides a solution for last mile problem and e-scooter sharing systems are one of the most heavily adopted micro-mobility services. The increasing usages of e-scooters make it necessary to analyse the possible effects of the vibrations transmitted to the drivers.Purpose This research has studied for the first time the e-scooter vibrations effects on drivers comfort and health for the actual range of circulation speeds, that can exceed 25km/h. Methods Based on experimental measured stiffness of two different e-scooter wheels and Multibody dynamic simulations, several statistical models have been obtained following the standard UNE2631. ResultsThe results show that for a common e-scooter and a road profile with a very good-good roughness level, a velocity of 16 km/h starts to be uncomfortable and for 23km/h could be harmful for health, for short trip durations. Derived from the statistical models, a new way of measuring the roughness has been proposed and that will be one of the future works to adjust and validate it.Conclusion E-scooter suspension systems (front suspension and wheels) must be improved under human comfort and health point of view. Furthermore, results suggest the necessity of study the vibrations effects on real e-scooters due to the maximum speed they can reach is greater than 25km/h.