On the need for bump event correction in vibration test profiles representing road excitations in automobiles

This paper presents an approach to the synthesis of compressed vibration test profiles representing much longer time histories obtained in road testing of ground vehicles. Vibration test profiles are defined as those related directly to operational testing on specific road surfaces and which summarise the input to the vehicle in the given conditions. The method extends classical Fourier transform technique by means of bump event correction in the background Fourier signal where the bump event term implies a high-amplitude transient event of the shock type. The orthogonal wavelet decomposition was used as a specific filtering tool facilitating bump event identification. Examples of seat guide vertical acceleration have been considered. Calculated probability density functions suggest the ability of the bump correction method to improve the statistical accuracy of the final vibration test profile with respect to the original road data. Test profiles obtained by means of Fourier transform synthesis with subsequent reinsertion of bump events from separated frequency bands were more accurate than those obtained by Fourier synthesis alone. Further developments led to advanced bump reinsertion with synchronisation of events occurring in different frequency bands at the same moment of time. Test profiles generated in this way have provided better accuracy compared to the non-synchronised algorithm

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

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

Review Paper on Road Vehicle Vibration Simulation for Packaging Testing Purposes

Inefficient packaging constitutes a global problem that costs hundreds of billions of dollars, not to mention the additional environmental impacts. An insufficient level of packaging increases the occurrence of product damage, while an excessive level increases the packages' weight and volume, thereby increasing distribution cost. This problem is well known, and for many years, engineers have tried to optimize packaging to protect products from transport hazards for minimum cost. Road vehicle shocks and vibrations, which is one of the primary causes of damage, need to be accurately simulated to achieve optimized product protection. Over the past 50 years, road vehicle vibration physical simulation has progressed significantly from simple mechanical machines to sophisticated computer-driven shaking tables. There now exists a broad variety of different methods used for transport simulation. Each of them addresses different particularities of the road vehicle vibration. Because of the nature of the road and vehicles, different sources and processes are present in the vibration affecting freight. Those processes can be simplified as the vibration generated by the general road surface unevenness, road surface aberrations (cracks, bumps, potholes, etc.) and the vehicle drivetrain system (wheels, drivetrain, engine, etc.). A review of the transport vibration simulation methods is required to identify and critically evaluate the recent developments. This review begins with an overview of the standardized methods followed by the more advanced developments that focus on the different random processes of vehicle vibration by simulating non-Gaussian, non-stationary, transient and harmonic signals. As no ideal method exists yet, the review presented in this paper is a guide for further research and development on the topic

Non-Gaussian PDF modeling of turbulent boundary layer fluctuating pressure excitation

The purpose of the study is to investigate properties of the probability density function (PDF) of turbulent boundary layer fluctuating pressures measured on the exterior of a supersonic transport aircraft. It is shown that fluctuating pressure PDFs differ from the Gaussian distribution even for surface conditions having no significant discontinuities. The PDF tails are wider and longer than those of the Gaussian model. For pressure fluctuations upstream of forward-facing step discontinuities and downstream of aft-facing step discontinuities, deviations from the Gaussian model are more significant and the PDFs become asymmetrical. Various analytical PDF distributions are used and further developed to model this behavior