On the Use of Machine Learning to Detect Shocks in Road Vehicle Vibration Signals

Abstract

The characterization of transportation hazards is paramount for protective packaging validation. It is used to estimate and simulate the loads and stresses occurring during transport that are essential to optimize packaging and ensure that products will resist the transportation environment with the minimum amount of protective material. Characterizing road transportation vibrations is rather complex because of the nature of the dynamic motion produced by vehicles. For instance, different levels of vibration are induced to freight depending on the vehicle speed and the road surface; which often results in non-stationary random vibration. Road aberrations (such as cracks, potholes and speed bumps) also produce transient vibrations (shocks) that can damage products. Because shocks and random vibrations cannot be analysed with the same statistical tools, the shocks have to be separated from the underlying vibrations. Both of these dynamic loads have to be characterized separately because they have different damaging effects. This task is challenging because both types of vibration are recorded on a vehicle within the same vibration signal.This paper proposes to use machine learning to identify shocks present in acceleration signals measured on road vehicles. In this paper, a machine learning algorithm is trained to identify shocks buried within road vehicle vibration signals. These signals are artificially generated using non-stationary random vibration and shock impulses that reproduce typical vehicle dynamic behaviour. The results show that the machine learning algorithm is considerably more accurate and reliable in identifying shocks than the more common approaches based on the crest factor. TPR, true positive rate; FPR, false positive rate; AUC, area under the ROC curve. USING MACHINE LEARNING TO DETECT SHOCKS IN VIBRATION SIGNAL

Measurement and evaluation of the effect of vibration on fruits in transit—Review

“…Griffiths, Hicks, Keogh, and when compared with the damage produced in field experiments. 86,89,90 In another recent study, 91 the capability of machine learning to detect shocks in road vibration signals was investigated and reported to also have achieved successful correlation. Further research on these methods to characterize damage on different fruits may permit the development of more accurate test profiles to be used in simulation compared with the conventional-averaged PSD spectrabased simulation approach.…”

A New Approach for Road‐Vehicle Vibration Simulation

“…It is used to calculate the moving RMS of a signal, and when the window length is set to be 32 s, there will be a more accurate detection of shocks by the moving crest factor. 12 Threshold. Those shocks, whose MCF are greater than or equal to the threshold will be extracted from vibration.…”

Measuring the mechanical and climatic conditions encountered by palletized products in handling and transport

“…They use to primarily consider aspects related to the track conditions (as the used of paved [16,17] or unpaved roads [18]). Firstly, measures reveal that the amplitude of vibration does not represent the only aspects to be considered for evaluating the damage effect on packages, but also the frequency is noteworthy.…”