Vehicle Vibration Signals Research Articles

Highway vehicle detection based on distributed acoustic sensing.

Distributed acoustic sensing systems can obtain the road vibration information caused by vehicle driving vibration on highways. By characterizing the vehicle driving vibration data, a distributed acoustic sensing system based highway vehicle driving vibration detection scheme is proposed. Firstly, the out-of-bag error criterion of random forest is used to select multiple features of the vibration signals to obtain the most suitable feature quantity to characterize the road vibration signals, then the root mean square value of the signal matched filtering is calculated and compared, and the threshold is used to distinguish between the vehicle vibration signals and the noise. Finally, the improved trajectory start algorithm is used to accurately detect the vehicles travelling on the highway to obtain the real-time information of the vehicles. The experiment verified that the method can effectively detect highway vehicles.

Intelligent identification of differential subgrade settlement of ballastless track system based on vehicle dynamic responses and 1D-CNN approach

The millimeter-scale deformation control standards of high-speed railways make the monitoring and identification of subgrade settlement a key technology, especially for ballastless track systems. Addressing the uncoordinated deformation between track and subgrade caused by high track stiffness, this paper proposes an intelligent identification method for subgrade settlement based on vehicle vibration signals and deep-learning technology. Firstly, a high-speed vehicle-ballastless track-subgrade coupled dynamics model considering the track-subgrade nonlinear contact state is utilized, to obtain various vibration responses of the vehicle subsystem due to differential subgrade settlement. After data preprocessing, a multi-channel one-dimensional convolutional neural network (1D-CNN) is established to extract features from the vehicle vertical acceleration dataset and output subgrade settlement status automatically. Finally, the robustness of the model is verified using Gaussian white noise. The research indicates that the vertical accelerations of the car body and bogie frame contain dynamic characteristics caused by medium-long wavelength settlements, while the vertical acceleration of the wheelset is more sensitive to short-wavelength settlements. It is feasible to identify the degree of subgrade settlement using car body, bogie frame and wheelset vertical accelerations as parameterized multi-source input of the 1D-CNN network. The three-channel input model significantly improves the identification accuracy and demonstrates the robustness against noise interference compared to the single-input and dual-input models. This method can effectively enhance the identification capability of ‘concealed’ subgrade settlements in high-speed railways, offering a scientific methodology for status maintenance of ballastless track lines.

Real-time detection of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals

This paper aims to propose a real-time detection method of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals. Monorail rubber tires exhibit strong nonlinearity and three-dimensional elasticity, resulting in highly coupled vertical and lateral vibrations. As a result, traditional methods applied in railway system such as transfer function analysis are not suitable for back-calculation of road irregularities for this nonlinear system. Iterative simulation with numerous parameters is time-consuming, thus this paper proposes a parallel simulation approach fused with the genetic algorithm to shorten the calculation time and facilitate big data processing. The multi-rigid body model’s simulation result can closely match the test data by intelligently modifying the vehicle parameters. This method overcomes the transfer function’s limitations in nonlinear systems and the significant errors introduced by the simplified mathematical derivation method. It also overcomes the shortcoming of significant errors in the mathematical derivation method and disperses errors caused by simplifying the multi-rigid body dynamics and ensures calculation accuracy. Additionally, this paper highlights the application of artificial intelligence techniques in intelligent wheel eccentricity detection. It improves the traditional algorithm to shorten the calculation time and benefit big data processing.

Identification of asphalt pavement transverse cracking based on 2D reconstruction of vehicle vibration signal and edge detection algorithm

Transverse cracking is related to asphalt pavement structural performance. However, the method for transverse cracks identification by vehicle vibration signal has hardly been applied because of an extremely low recognition accuracy. The study aims to precisely identify the pavement transverse cracks by vehicle vibration response. A two-dimensional reconstruction method that transformed the vehicle vibration signal into an image was proposed. Five edge detection operators were used to generate the features of vibration reconstruction images. Besides, this study combined two basic characteristics, Euler number, and total target area. Eight classification models based on the above two features were trained by the RUSBoost integrated classifier. The results showed that the classification model based on the Prewitt operator eight connected domain Euler number and the total target area performed excellently. The testing accuracy can reach 93.5%. The classification model presented in this study has an excellent effect on pavement transverse crack identification and feasibility.

Asphalt pavement surface repair area detection based on smartphone sensors

Asphalt pavement repair areas affect pavement performance and service levels. It is necessary to distinguish the repair areas from normal sections. Based on vehicle vibration signals, this study identified ten pavement repair areas and divided them into four cases by factors including length and form in conjunction with the driving approach. Additionally, time domain analysis, frequency analysis, and probability distribution analysis were used to form the characteristics of the repair cases as well as the normal sections. It was found that the maximum value, extreme deviation, standard deviation in the time domain, maximum amplitude in the frequency domain, and peak of the probability density curve would serve as judgment indexes. A framework for identifying the repair areas was also established based on the five indexes. By validation, the overall accuracy can reach 95.0%, demonstrating a strong generalization capability.

Traffic Vibration Signal Analysis of DAS Fiber Optic Cables with Different Coupling Based on an Improved Wavelet Thresholding Method.

Distributed Acoustic Sensing (DAS) is a novel technology that uses fiber optics to sense and monitor vibrations. It has demonstrated immense potential for various applications, including seismology research, traffic vibration detection, structural health inspection, and lifeline engineering. DAS technology transforms long sections of fiber optic cables into a high-density array of vibration sensors, providing exceptional spatial and temporal resolution for real-time monitoring of vibrations. Obtaining high-quality vibration data using DAS requires a robust coupling between the fiber optic cable and the ground layer. The study utilized the DAS system to detect vibration signals generated by vehicles operating on the campus road of Beijing Jiaotong University. Three distinct deployment methods were employed: the uncoupled fiber on the road, the underground communication fiber optic cable ducts, and the cement-bonded fixed fiber optic cable on the road shoulder, and compared for their outcomes. Vehicle vibration signals under the three deployment methods were analyzed using an improved wavelet threshold algorithm, which was verified to be effective. The results indicate that for practical applications, the most effective deployment method is the cement-bonded fixed fiber optic cable on the road shoulder, followed by the uncoupled fiber on the road, and the underground communication fiber optic cable ducts are the least effective. This has important implications for the future development of DAS as a tool for various fields.

A Novel Method for Pavement Transverse Crack Detection Based on 2D Reconstruction of Vehicle Vibration Signal

Pavement transverse cracks are associated with pavement performance. However, due to low recognition accuracy, the transverse cracking detection through vehicle's vibration response has hardly been considered. This paper proposes a novel method for pavement transverse cracking detection using reconstructed vehicle vibration signal. An accelerometer installed in the vehicle collects the vibration response between the vehicle and the pavement transverse cracks. A brand-new approach to data processing is suggested that transforms a one-dimensional vibration signal into a two-dimensional vibration image. The two-dimensional vibration image generated can obtain different features effectively from the time domain. Based on Gray Level Concurrence Matrix (GLCM) algorithm, a characteristic index established from the vibration image's contrast can distinguish transverse cracks from uncracked sections. Then its feasibility was investigated by 410 pavement sections, the accuracy of the index is 93.84%. The transverse crack detection method proposed in this paper seems promising for precisely identifying pavement transverse cracks.

Vehicle identification using deep learning for expressway monitoring based on ultra-weak FBG arrays.

A deep learning with knowledge distillation scheme for lateral lane-level vehicle identification based on ultra-weak fiber Bragg grating (UWFBG) arrays is proposed. Firstly, the UWFBG arrays are laid underground in each expressway lane to obtain the vibration signals of vehicles. Then, three types of vehicle vibration signals (the vibration signal of a single vehicle, the accompanying vibration signal, and the vibration signal of laterally adjacent vehicles) are separately extracted by density-based spatial clustering of applications with noise (DBSCAN) to produce a sample library. Finally, a teacher model is designed with a residual neural network (ResNet) connected to a long short-term memory (LSTM), and a student model consisting of only one LSTM layer is trained by knowledge distillation (KD) to satisfy the real-time monitoring with high accuracy. Experimental demonstration verifies that the average identification rate of the student model with KD is 95% with good real-time capability. By comparison tests with other models, the proposed scheme shows a solid performance in the integrated evaluation for vehicle identification.

An Architecture for On board Frequency Domain Analysis of Launch Vehicle Vibration Signals

The dynamic properties of the airborne structures plays a crucial role in the stability of the vehicle during
 flight. Modal and spectral behaviour of the structures are simulated and analysed. Ground tests are carried out with environmental conditions close to the flight conditions, with some assumptions. Subsequently, based on the flight telemetered data, the on-board mission algorithm and the auto-pilot filter coefficients are fine tuned. An attempt is made in this paper to design a novel architecture for analysing the modal and spectral random vibration signals on-board the flight vehicle and to identify the dominant frequencies. Based on the analysed results, the mission mode algorithm and the filter coefficients can be fine tuned on-board for better effectiveness in control and providing more stability. Three types of windows viz. Hann, Hamming and Blackman-Harris are configured with a generalised equation using FIR filter structure. The overlapping of the input signal data for better inclusiveness of the real-time data is implemented with BRAM. The domain conversion of the data from time domain to frequency domain is carried out with FFT using Radix-2 BF architecture. The FFT output data are processed for calculating the power spectral density. The dominant frequency is identified using the array search method and Goldschmidt algorithm is utilised for the averaging of the PSDs for better precision. The proposed architecture is synthesised, implemented and tested with both Synthetic and doppler signal of 300 Hz spot frequency padded with Gaussian white noise. The results are highly satisfactory in identifying the spot frequency and generating the PSD array.

Pavement Roughness Grade Recognition Based on One-dimensional Residual Convolutional Neural Network.

A pavement's roughness seriously affects its service life and driving comfort. Considering the complexity and low accuracy of the current recognition algorithms for the roughness grade of pavements, this paper proposes a real-time pavement roughness recognition method with a lightweight residual convolutional network and time-series acceleration. Firstly, a random input pavement model is established by the white noise method, and the pavement roughness of a 1/4 vehicle vibration model is simulated to obtain the vehicle vibration response data. Then, the residual convolutional network is used to learn the deep-level information of the sample signal. The residual convolutional neural network recognizes the pavement roughness grade quickly and accurately. The experimental results show that the residual convolutional neural network has a robust feature-capturing ability for vehicle vibration signals, and the classification features can be obtained quickly. The accuracy of pavement roughness classification is as high as 98.7%, which significantly improves the accuracy and reduces the computational effort of the recognition algorithm, and is suitable for pavement roughness grade classification.

Identification method for subgrade settlement of ballastless track based on vehicle vibration signals and machine learning

High-frequency train loads and complex environmental factors over a long period will inevitably cause subgrade settlement, an issue that adversely affects line smoothness. The existing monitoring methods are inefficient, manpower consuming and small in scope. This paper takes the subgrade settlement disease of the double-block ballastless track as the research object and proposes a subgrade settlement identification method based on vehicle vibration signals. In addition, it establishes a vehicle-track-subgrade vertical coupling model that combines feature extraction with algorithms such as support vector machines (SVMs) and convolutional neural networks (CNNs) to accurately identify subgrade settlement diseases on ballastless tracks. The results show: I) the subgrade settlement is far more impactful than fastener failure and debonding of the supporting layer on the vehicle vibration, with the main frequency band concentrated in 0 to 10 Hz, and the vertical vibration acceleration of the vehicle body and bogie can be used as a sensitive feature to identify ballastless track subgrade settlement, II) both the particle swarm optimization (PSO)-SVM and CNN-SVM algorithms can effectively determine the existence of subgrade settlement and the values of the settlement wavelength and amplitude. Both algorithms run a 100 % identification of subgrade settlement, with the lowest identification rates of settlement wavelength being 97.56 % and 100 %, respectively and the lowest identification rates of amplitude being 84.78 % and 97.56 %, respectively. In general, the overall accuracy of the CNN-SVM always outperforms PSO-SVM, and III) changes to the vehicle passing speed will create a more obvious interference effect on the identification of the settlement amplitude. For the PSO-SVM algorithm, the identification rate for the amplitude has a difference of up to 8.70 %, while this is 2.44 % for the CNN-SVM algorithm, indicating the CNN-SVM algorithm is more robust than the PSO-SVM algorithm.

Road Recognition Based on Vehicle Vibration Signal and Comfortable Speed Strategy Formulation Using ISA Algorithm.

When a vehicle is being driven, it is excited by the road roughness and generates its own vibration. In order to improve the vehicle’s riding comfort and the physical–mental health of passengers in the vehicle, this paper proposes a formulation method for a comfortable speed strategy and the technical route of its application. According to international standard ISO 2631-1, the relationship between the weighted root-mean-square acceleration value and comfortable vehicle speed is analyzed. The simulation test platform of the road roughness signal and vehicle vibration signal is built by using the filtering white noise method and the second Lagrange equation through Matlab/Simulink. Combined with the simulation platform, this paper extracts seven characteristics with statistical properties from the time-domain signal and obtains 500 sample data. Random forest (RF), extreme learning machine (ELM), and radial basis function neural network (RBF-NN) are applied to identify roads. Two comfortable speed strategy formulation methods based on the improved simulated annealing (ISA) algorithm are proposed and compared according to the solution effect of each grade of comfortable speed. The results show that the simulated signals of each grade road roughness are accurate. Road recognition can be effectively carried out using the statistical characteristics of vehicle vibration acceleration signals. ELM has high recognition accuracy and fast execution speed. The ISA-II algorithm has a low solution error of comfortable speed and a low computation time. The comfortable speed of the research vehicle on different road grades showed a great difference.

A Characterization Method for Pavement Structural Condition Assessment Based on the Distribution Parameter of the Vehicle Vibration Signal

A pavement structural survey plays a vital role in road maintenance and management. This study was intended to explore the feasibility of a non-stop pavement structure assessment method by analyzing the vibration data from a vehicle sensor. In this study, three falling weight deflectometer (FWD) tests and four vehicle vibration tests were conducted on five pavement structures. The FWD test results show that the continuously reinforced composite pavement has a higher structural stiffness than the semi-rigid base asphalt pavement. According to the statistical distribution of vehicle acceleration, a distribution parameter, the peak probability density (PPD), was proposed. The correlation coefficient (−0.722) of the center deflection (D1) and PPD indicates a strong correlation between the two variables. Therefore, PPD is strongly correlated with pavement structural stiffness. This study proposed a novel characterization method for pavement structural conditions based on the distribution parameter of the vehicle vibration signal.

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

Synthesis of severely non-stationary vehicle vibration signals based on Higher-order moments and Hilbert spectrum

Vehicle vibration signals can be severely non-stationary under extreme conditions, and the synthesis of such kind of signal is acrucial researchtopic but requires some level of sophistication. This paper reveals how time-varying amplitudes and phase functions affect statistical characteristics of a random signal, and then proposes a novel four-step method to synthesize vehicle vibrations with arbitrary prescribed duration based on Hilbert spectrum and higher-order moments. Numerical simulations have verified that this method can mostly maintain the characteristics of sample data in both time domain and frequency domain, for instance, the probability density function, skewness, kurtosis, marginal Hilbert spectrum, and degree of stationarity. This method can not only provide accurate inputs for Monte Carlo Simulation, but also provide an experimental platform for Laboratory Testing. Further, it could be a reference approach to the simulation of other severely non-stationary scenarios.

Driving collision detection method based on VMD-KICA

With the rapid development of China’s transportation, higher requirements are put forward for the handling capacity of traffic accidents. Therefore, it is of great practical significance to study the effective monitoring methods of high-speed traffic accidents for timely guiding the traffic and saving the lives of the injured. The VMD is used to decompose the vehicle vibration signal, and the cross-correlation coefficient is used as the index to screen the components, and the KICA algorithm is used to extract the components of the screened component matrix, so as to further eliminate the environmental noise. The signal is recombined by the denoised components to highlight the time-domain statistical characteristics of the signal. The feature vector based on time-domain statistical parameters is constructed, and the recognition model is built by using the support vector mechanism optimized by genetic algorithm. The experimental results show that the accuracy of the method for vehicle driving state can be 95.5%.

Vehicle identification based on Variational Mode Decomposition in phase sensitive optical time-domain reflectometer

In this paper, aiming at the problem of vibration event classification based on phase sensitive optical time-domain reflectometer (Φ-OTDR), we propose an efficient multi class event recognition scheme based on Variational Mode Decomposition (VMD). The signals collected by optical fiber sensors are preprocessed by the VMD algorithm, and then the features of the signals are extracted by Mel Frequency Cepstral Coefficients (MFCC). Finally, the extracted features are classified and identified by using machine learning algorithm. In order to improve the reliability of identification, the VMD algorithm can be used to decompose the signal into different modes. We extract and identify the features of each mode signal. Finally, the result with the highest number of occurrences is taken as the identification result. Six different vehicle vibration signals are classified and identified, and the recognition accuracy is 97.7%.

Identification of asphalt pavement transverse cracking based on vehicle vibration signal analysis

Transverse cracking is a common distress of semi-rigid asphalt pavement in China. Crack detection offers essential information for pavement performance evaluation and maintenance. A vibration-based method is proposed for transverse cracking identification of asphalt pavement. Vibration signals of the running vehicle at the transverse-cracked sections and the adjacent uncracked sections are measured and preliminarily analysed in time domain. Symlet3 wavelet is used for signal denoising. Frequency domain analysis of signals by fast Fourier transform (FFT) and discrete wavelet transform (DWT) shows that the occurrence of the transverse crack leads to a dramatic increase of energy in the sensitive frequency band (10∼20 Hz). Considering the computation efficiency, the dual relative energy (DRE) process by FFT and DWT is proposed for crack detection. In addition, the continuous wavelet time-frequency analysis is utilised to locate the transverse cracking precisely. This approach seems promising for the rapid identification of pavement cracks.

Vehicle Detection and Classification Using Distributed Fiber Optic Acoustic Sensing

This paper presents a vehicle detection and classification system using distributed fiber-optic acoustic sensing (DAS) technology and describes a comprehensive classification method including signal processing and feature extraction. This sensing device is based on Rayleigh scattering light and is used for real-time vehicle detection, classification, and speed estimation. Distributed acoustic signals from an arbitrary point can be detected and located through DAS technology which can provide fully distributed acoustic information along the entire fiber link. This technology utilizes sensing fiber in the form of distributed sensors to collect traffic vibration signals and then extracts several features from the signals to estimate the vehicle count and identify vehicle categories. According to the vehicle vibration signal characteristics, the wavelet-denoising algorithm and dual-threshold algorithm are improved. The improved algorithm is used to reconstruct the signal for feature extraction, and the vehicle count and speed are obtained. When all features have been extracted, the classification of vehicle types is implemented by a support vector machine classifier. The validation data (using a distributed fiber-optic acoustic sensor) demonstrate that the vehicle detection accuracy is higher than 80%, the speed estimation error is less than 5%, and the vehicle classification accuracy is higher than 70%.

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.