Road Vehicle Vibration Research Articles

Spectral Non-Stationarity in Road Vehicle Vibrations

Road-induced vibrations are in the scope of various environmental testing protocols, e.g., for packaging vibration testing (PVT) purposes. This field matures with well-understood methods for analyzing amplitude-type non-stationarity (NS) in road vehicle vibrations (RVV). Albeit frequency-type NS is well known, only suggestions are provided for processing the phenomenon in PVT. Both types of NS can be jointly investigated in the time-frequency domain; thus, the current study initiates the investigation of spectral non-stationarities (SNS) in RVV. Three vibration series were recorded from 118 km traveled distance supplying an empirical insight.

Spectral moment segmentation and spectrogram synthesis for simulation of road vehicle vibrations

Vibration testing procedures relying solely on a Fourier profile can introduce only stationary random vibrations. It is in contrast with the non-stationary and non-Gaussian nature of the Road vehicle vibrations (RVV). The following procedure segments the first four spectral moments of the time–frequency domain of RVV, constructs probability density arrays per frequency bins, and perform simulations according to random segment lengths and -root mean squares, yielding more realistic representations of RVV. The distribution of time- and frequency domain moments and normalized spectral entropies are confronted. The Probability-based spectrogram synthesis (PBSS) offers a data-driven stochastic modelling framework for simulating non-stationary RVV.

Segmentation of a Road Vehicle Vibration Signal Using Multiple Comparison Procedures between Paired Samples

Segmentation of a Road Vehicle Vibration Signal Using Multiple Comparison Procedures between Paired Samples

On the parameters that influence road vehicles vibration levels

It has long been recognised that the level of road vehicle vibrations are mainly a function of vehicle characteristics, road roughness and vehicle speed. With the introduction of easy‐to‐use vibration data recorders, significant amounts of data have been recorded, and numerous studies on the rms levels of truck vibrations have been published. However, the results available to date are typically from specific scenarios and do not provide comprehensive comparisons with similar published work. In addition, most of the publications only report the mean rms level with no indication of how the rms varies throughout the journey nor statistical information on the likelihood of particular rms levels being exceeded. This paper brings together the available information on road transport vehicle vibration levels for analysis. It does so by first collating published mean vibration rms values for a broad range of scenarios and supplements them with additional mean rms values recorded by the authors. The collated results were analysed statistically to reveal the influence of important parameters, namely, suspension type, road type, payload and vehicle type. Results from the statistical analysis are used to quantify the influence of each parameter and to allow for the prediction of expected rms levels based on the transport scenario. This introduces a risk‐based approach to laboratory testing which allows the analyst to set the test rms levels based the road transport scenario and the accepted level of risk.

Using the Weibull distribution to characterise road transport vibration levels

This paper reviews the various ways that have been proposed to characterise road transport vehicle vibrations and recommends a new approach to characterise the vibrations levels during a transport journey. Some 47 road vehicle vibration records, obtained from a broad range of conditions, were analysed, and results show that the root‐mean‐square (rms) distribution of the vibrations can be accurately modelled with a reduced version of the three‐parameter Weibull distribution (shape parameter set to 2). This statistical approach to characterising road vehicle vibrations takes into account the random fluctuations in rms levels that occur naturally during a road journey and can be used to classify the severity of RVV. This offers significant improvement on the simplistic mean rms value that has, so far, been the sole parameter to describe vibration levels during transport. The Weibull location parameter represents the low threshold of the rms level in the record (except when xo is less than zero, in which case the low rms threshold is zero), whereas the Weibull range parameter is proportional to the range of rms level. Results also reveal a strong relationship between the rms mean and the sum of the location and scale parameters. In addition, this enables generation of rms distributions from the mean power density spectrum (PDS) alone. The modified (fixed‐shape) Weibull distribution can be used to faithfully describe the entire statistical distribution of the rms level of a journey or transport mode with just two parameters. This new approach can be used in a practical way for quantifying and comparing transport vibration rms levels for design and testing purposes.

Comparison study on simulation effect of improved simulation methods for packaging random vibration test

In general, existing testing methods for packaging design employ Gaussian distributions to represent vibration induced by road profiles. However, the actual road vehicle vibration is non‐Gaussian. The key limitation is that the simulated Gaussian vibration cannot reconstruct the shock events buried in the vibration, which will result in inaccurate reliability evaluation of cushion packaging. A new simulation technique called shock extraction has been proposed and validated in our earlier study. This article is a further study of our previous research. The shock extraction method has been compared with other three representative simulation methods including the single‐level PSD, three‐way split spectral, and wavelet decomposition in terms of simulation effect. Signal simulated by the shock extraction method possesses the same vibration intensity, duration, approximate statistical characteristics, and PSD plots with the original signal. The results show that the simulation effect of the shock extraction method is the best, and the worst is the single‐level PSD. The wavelet decomposition and three‐way split spectral are somewhere in the middle.

Evaluation of Shock Detection Algorithm for Road Vehicle Vibration Analysis

The ability to characterize shocks which occur during road transport is a vital prerequisite for the design of optimized protective packaging, which can assist in reducing cost and waste related to products and good transport. Many methods have been developed to detect shocks buried in road vehicle vibration signals, but none has yet considered the nonstationary nature of vehicle vibration and how, individually, they fail to accurately detect shocks. Using machine learning, several shock detection methods can be combined, and the reliability and accuracy of shock detection can also be improved. This paper presents how these methods can be integrated into four different machine learning algorithms (Decision Tree, k-Nearest Neighbors, Bagged Ensemble, and Support Vector Machine). The Pseudo-Energy Ratio/Fall-Out (PERFO) curve, a novel classification assessment tool, is also introduced to calibrate the algorithms and compare their detection performance. In the context of shock detection, the PERFO curve has an advantage over classical assessment tools, such as the Receiver Operating Characteristic (ROC) curve, as it gives more importance to high-amplitude shocks.

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. Copyright © 2016 John Wiley & Sons, Ltd.

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. Copyright © 2015 John Wiley & Sons, Ltd.

Quantifying the Non-stationarity of Vehicle Vibrations with the Run Test

This paper investigates the applicability of the run test as a tool to quantify the statistical non-stationarity of road vehicle vibrations. The run test was applied to the moving root-mean-square (RMS) time history of a number of vibration records measured from a variety of vehicle types, routes and vehicle speeds. The paper discusses the limitations associated with calculating the moving RMS of random signals especially with respect to the window width. When applied to the set of vibration records (with segments of inactivity removed), the run test indicates that every record is non-stationary. A run ratio parameter was introduced to quantify the level of non-stationarity, which shows that the result is sometimes dependent on the RMS window width. Further analysis correlating the run test results with the statistical distribution or the RMS indicates that the run ratio parameter has some merit in quantifying the level of non-stationarity in road vehicle vibrations. Copyright © 2013 John Wiley & Sons, Ltd.

On the Statistical Distribution of Road Vehicle Vibrations

SUMMARYThis paper presents an alternative method for characterizing the random vibrations produced by transport vehicles. The paper discusses the significance and limitations of the average power spectral density and explains why it is not always adequate as the sole descriptor of road vehicle vibrations as the process generally tends to be non‐stationary and non‐Gaussian. The paper adopts an alternative analysis method, based on the statistical distribution of the moving root‐mean‐square (rms) vibrations, as a supplementary indicator of overall ride quality. A variety of sample vibration records, collected from various vehicle types and routes in Spain and Australia, were used to investigate the suitability of various mathematical models, based on the Weibull distribution. It shows that the model can also effectively describe the statistical parameters of the process, namely the mean, median, standard deviation, skewness and kurtosis. The paper proposes a single mathematical model that can accurately describe the statistical character of the non‐stationary random vibrations generated by road vehicles in general. The proposed generic distribution model, based on the Weibull distribution, was developed to afford additional control over various aspects of the shape of the distribution function. The model was found to be general enough to be able to produce a range of well‐known distributions. Curve‐fitting results using the sum‐of‐squared error (least squares) optimization were found to produce non‐convergent results, which required inclusion of the mean, median, standard deviation, skewness and kurtosis in the optimization algorithm. The paper also shows how the model is capable of accurately describing the statistical parameters of the process, namely the mean, median, standard deviation, skewness and kurtosis. This result is relevant not only for the characterization of ride quality but also for the accurate synthesis of road vehicle vibrations in the laboratory. The results can be used to assist in developing a novel method for simulating non‐stationary (modulated) vibration in the laboratory. The rms distribution function can be used to create an rms level schedule that will enable the synthesis of random vibrations with varying rms levels to better represent the road transport vibration process. Copyright © 2011 John Wiley & Sons, Ltd.

Statistical modelling of predicted non‐stationary vehicle vibrations

AbstractThis paper describes a method to predict the vertical vibrations of road vehicles from measured pavement profiles. It discusses the limitations of current methods used for analysing and simulating vehicle vibrations and shows that more accurate characterization and simulation of the transport environment must take into account the non‐stationary nature of road vehicle vibrations. Vertical vibrations for typical transport vehicles under various operating conditions and pavement profiles are predicted using a computer model of the vehicle characteristics and analysed to produce the spectral and statistical characteristics. The paper also presents an improved method to compute the vibration intensity by using a dynamic segmentation data reduction technique. The effectiveness of the procedure to characterize the non‐stationarity of random vehicle vibrations is demonstrated. Finally, the paper deals with the statistical distribution of the vibration intensity and demonstrates how it can be adapted to a technique for the simulation the non‐stationary nature of random vehicle vibrations. Copyright © 2002 John Wiley & Sons, Ltd.