Vehicle Vibration Signals Research Articles

Evaluation of machine learning algorithms for detection of road induced shocks buried in vehicle vibration signals

Road surface imperfections and aberrations generate shocks causing vehicles to sustain structural fatigue and functional defects, driver and passenger discomfort, injuries, and damage to freight. The harmful effect of shocks can be mitigated at different levels, for example, by improving road surfaces, vehicle suspension and protective packaging of freight. The efficiency of these methods partly depends on the identification and characterisation of the shocks. An assessment of four machine learning algorithms (Classifiers) that can be used to identify shocks produced on different roads and test tracks is presented in this paper. The algorithms were trained using synthetic signals. These were created from a model made from acceleration measurements on a test vehicle. The trained Classifiers were assessed on different measurement signals made on the same vehicle. The results show that the Support Vector Machine detection algorithm used in conjunction with a Gaussian Kernel Transform can accurately detect shocks generated on the test track with an area under the curve (AUC) of 0.89 and a Pseudo Energy Ratio Fall-Out (PERFO) of 8%.

Vehicle Vibration Signal Processing for Road Surface Monitoring

In India, road transportation dominates all other means of transportation mechanisms. Well maintained roadways make the travel smooth and comfortable for the passengers. Most of the time road irregularities, such as potholes and humps create disturbances to cozy travel and cause major damages to vehicles. Detecting road potholes and road roughness levels is a key to road condition monitoring, which impacts on transport safety and driving comfort. This paper proposes a method that aims to monitor road surfaces, detect road potholes and humps, and predict their severity by analyzing the vertical vibration signals produced by the vehicle while it moves. The proposed system uses the smartphone accelerometer to capture the vehicle vibrations in which $z$ -axis reading corresponds to the vehicle vertical vibrations. Gaussian model-based mining algorithm is proposed for the abnormal event detection, $x$ - $z$ ratio filtering is applied for event classification as pothole or hump. Severity estimation algorithm is proposed, which makes use of the relation between vertical acceleration and relative vertical displacement of the vehicle.

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.

Modeling of non‐stationary vibration signals based on the modified Kronecker sequences

AbstractThe paper establishes a representation model for non‐stationary random vibration signals based on the modified Kronecker sequences. The modified Kronecker sequence constructed via generalizing golden ratio is one of the special types of low discrepancy sequences which have better dimensional projections [1]. The actual modeling and simulation of non‐stationary random data is more suitable for seismological signals and not for the vehicle vibrations [2, 3]. Under these circumstances, this paper presents a new algorithm for finding the modified Kronecker sequences in order to generate non‐stationary vehicle vibration signals which mostly withhold the amplitude‐frequency‐time distribution of the sample signal. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Modeling and simulation of non-stationary vehicle vibration signals based on Hilbert spectrum

Vehicle vibrations are random and highly non-stationary in many occasions, however, so far there has been no suitable model to characterize and simulate these non-stationary random vibrations. Under the circumstances, some primary research was made in this paper on modeling and simulating non-stationary random vibration signals. After reviewed various tools available to analyze non-stationary data and compared their relative merits, this paper has established a new model to represent non-stationary random data based on the Hilbert spectrum, and has proposed a novel five-step method to simulate non-stationary vehicle vibration signals, which can mostly retain statistical characteristics of the sample signal. This model and this method have been verified the effectiveness and feasibility by numerical simulations and practical experiments based on various records of random vibration signals collected from transport vehicles, further they could be applied in many fields such as vehicle dynamic analysis, and vibration environmental testing.

A New Method of Eliminating Outliers in Data Processing of Tracked Vehicle Vibration Signal

The existence of outliers can produce false harmonic frequency components in data processing for vibration signal of tracked vehicle.The paper uses the Wright standards to judge details coefficients of outliers and calculates new details coefficients to replace the ones of the outliers by weighted coefficient method.The signal can be reconstructed with new details coefficients .The results show that the new method can retain the maximum information of singal after eliminating the outliers , meet the data distribution and reduce variance.

Research on Metro Vehicle Vibration with Regard to Route Conditions

With the development of underground rail, the problem that vehicle vibration affects the vehicle’s life-span and harms the health of the passenger has been concerned. In this paper, a vibration test system has been formed with data collecting instrument and acceleration sensors to collect the signals of the metro vehicle floor when it running at the ATO (Auto Train Operation) mode in the tunnel, Analyzing the regulation of vehicle vibration signal change that come with the change of the route condition, such as curve, slope, and so on. Obtain the relationship between the road condition and vehicle vibration. The analysis result shows that, the vehicle vibration not only be affected by the structure of the vehicle itself and the geological condition on which the track laid, but also has close relationship with the route condition.