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A Multiscale Approach to the Smart Deployment of Micro-Sensors over Lightweight Structures.

ABSTRACT: A topology optimization approach has been recently proposed to maximize the sensitivity to damage of measurements, collected through a network of sensors to be deployed over thin plates for structural health monitoring purposes. Within such a frame, damage is meant as a change in the structural health characterized by a reduction of relevant stiffness and load-carrying properties. The sensitivity to a damage of unknown amplitude and location is computed by comparing the response to the external actions of the healthy structure and of a set of auxiliary damaged structures, each one featuring reduced mechanical properties in a small region only. The topology optimization scheme has been devised to properly account for the information coming from all of the sensors to be placed on the structure and for damage depending on its location. In this work, we extend the approach within a multiscale frame to account for three different length scales: a macroscopic one, linked to the dimensions of the whole structure to be monitored; a mesoscopic one, linked to the characteristic size of the damaged region; a microscopic one, linked to the size of inertial microelectromechanical systems (MEMS) to be used within a marginally-invasive health monitoring system. Results are provided for a square plate and for a section of fuselage with stiffeners, to show how the micro-sensors have to be deployed to maximize the capability to detect a damage, to assess the sensitivity of the results to the measurement noise and to also discuss the speedup in designing the network topology against a standard single-scale approach.

SUBMITTER: Capellari G

PROVIDER: S-EPMC5539583 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:Inertial measurement units (IMU) are proven as efficient tools for swimming analysis by overcoming the limits of video-based systems application in aquatic environments. However, coaches still believe in the lack of a reliable and easy-to-use analysis system for swimming. To provide a broad view of swimmers' performance, this paper describes a new macro-micro analysis approach, comprehensive enough to cover a full training session, regardless of the swimming technique. Seventeen national level swimmers (5 females, 12 males, 19.6 ± 2.1 yrs) were equipped with six IMUs and asked to swim 4 × 50 m trials in each swimming technique (i.e., frontcrawl, breaststroke, butterfly, and backstroke) in a 25 m pool, in front of five 2-D cameras (four under water and one over water) for validation. The proposed approach detects swimming bouts, laps, and swimming technique in macro level and swimming phases in micro level on all sensor locations for comparison. Swimming phases are the phases swimmers pass from wall to wall (wall push-off, glide, strokes preparation, swimming, and turn) and micro analysis detects the beginning of each phase. For macro analysis, an overall accuracy range of 0.83-0.98, 0.80-1.00, and 0.83-0.99 were achieved, respectively, for swimming bouts detection, laps detection and swimming technique identification on selected sensor locations, the highest being achieved with sacrum. For micro analysis, we obtained the lowest error mean and standard deviation on sacrum for the beginning of wall-push off, glide and turn (-20 ± 89 ms, 4 ± 100 ms, 23 ± 97 ms, respectively), on shank for the beginning of strokes preparation (0 ± 88 ms) and on wrist for the beginning of swimming (-42 ± 72 ms). Comparing the swimming techniques, sacrum sensor achieves the smallest range of error mean and standard deviation during micro analysis. By using the same macro-micro approach across different swimming techniques, this study shows its efficiency to detect the main events and phases of a training session. Moreover, comparing the results of both macro and micro analyses, sacrum has achieved relatively higher amounts of accuracy and lower mean and standard deviation of error in all swimming techniques.

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A Multiscale Approach to the Smart Deployment of Micro-Sensors over Lightweight Structures.

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