Login / Signup

Application of MEMS Sensors for Evaluation of the Dynamics for Cargo Securing on Road Vehicles.

Jozef GnapJuraj JagelčákPeter MarienkaMarcel FrančákMariusz Kostrzewski
Published in: Sensors (Basel, Switzerland) (2021)
Safety is one of the key aspects of the successful transport of cargo. In the case of road transport, the dynamics of a vehicle during normal events such as braking, steering, and evasive maneuver are variable in different places in the vehicle. Several manufacturers provide different dataloggers with acceleration sensors, but the results are not comparable due to different sensor parameters, measurement ranges, sampling frequencies, data filtration, and evaluation of different periods of acceleration. The position of the sensor in the loading area is also important. The accelerations are not the same at all points in the vehicle. The article deals with the measurement of these dynamic events with MEMS sensors on selected points of a vehicle loaded with cargo and with changes in dynamics after certain events that could occur during regular road transport of cargo to analyze the possibilities for monitoring accelerations and the related forces acting on the cargo during transport. The article uses evaluation times of 80, 300, and 1000 ms for accelerations. With the measured values, it is possible to determine the places with a higher risk of cargo damage and not only to adjust the packaging and securing of the cargo, but also to modify the transport routes. Concerning the purposes of securing the cargo in relation to EN 12195-1 and the minimum values of forces for securing the cargo, we focused primarily on the places where the acceleration of 0.5 g was exceeded when analyzing the monitored route. There were 32 of these points in total, all of which were measured by a sensor located at the rear of the semi-trailer. In 31 cases, the limit of 0.5 g was exceeded for an 80-ms evaluation time, and in one case, the value of 0.51 g was reached in the transverse direction for a 300-ms evaluation time.
Keyphrases
  • mass spectrometry
  • ms ms
  • oxidative stress
  • low cost
  • deep learning
  • data analysis
  • wound healing