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

The characterisation of transportation hazards is paramount for protective packaging validation. It is used to estimate and simulate the loads and stresses occurring during transport which are essential to optimise packaging and ensure that products will resist the transportation environment with the minimum amount of protective material. Characterising road transportation vibrations is rather complex due to 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 nonstationary random vibration. Road aberrations (such as cracks, potholes, 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 characterised separately because they have different damaging effects. This task is a challenging because both types of vibration are recorded on a vehicle within the same vibration signal.