Improved method for automotive vibration level testing based on signal feature extraction

abstractData from the 1971 San Fernando, California, earthquake provided the opportunity to study the variation of ground motions on a local scale. The uncertainty in ground motion was analyzed by studying the residuals about a regression with distance and by utilizing the network of strong-motion instruments in three local geographic regions in the Los Angeles area. Our objectives were to compare the uncertainty in the peak ground acceleration (PGA) and root mean square acceleration (RMSa) about regressions on distance, and to isolate components of the variance. We find that the RMSa has only a slightly lower logarithmic standard deviation than the PGA and conclude that the RMSa does not provide a more stable measure of ground motion than does the PGA (as is commonly assumed). By conducting an analysis of the residuals, we have estimated contributions to the scatter in high-frequency ground motion due to phenomena local to the recording station, building effects defined by the depth of instrument embedment, and propagation-path effects. We observe a systematic decrease in both PGA and RMSa with increasing embedment depth. After removing this effect, we still find a significant variation (a standard deviation equivalent to a factor of up to 1.3) in the ground motions within small regions (circles of 0.5 km radius). We conclude that detailed studies which account for local site effects, including building effects, could reduce the uncertainty in ground motion predictions (as much as a factor of 1.3) attributable to these components. However, an irreducible component of the scatter in attenuation remains due to the randomness of stress release along faults during earthquakes. In a recent paper, Joyner and Boore (1981) estimate that the standard deviation associated with intra-earthquake variability corresponds to a factor of 1.35.

Stochastic shock response spectrum decomposition method based on probabilistic definitions of temporal peak acceleration, spectral energy, and phase lag distributions of mechanical impact pyrotechnic shock test data

The objective of this study was to collect and interpret three-axis acceleration, temperature, and relative humidity data from six locations within commercial transport trailers shipping market-weight pigs. Transport was observed in Kansas (n = 15) and North Carolina (n = 20). Prior to loading, three-axis accelerometers were affixed to six locations on the trailers: top fore (TF), top center (TC), top aft (TA), bottom fore (BF), bottom center (BC), and bottom aft (BA) compartments. Data were post-processed to calculate root-mean-square (RMS) accelerations and vibration dose values (VDV) in the vertical direction and the horizontal plane. These values were compared with exposure action values (EAV) and exposure limit values (ELV), vibration levels deemed uncomfortable and potentially dangerous to humans. Additionally, RMS and VDV were compared among the trailer compartments. The vertical RMS accelerations for all compartments exceeded the EAV for loads measured in Kansas, and for the majority of the compartments measured in North Carolina. Many compartments, specifically the BA compartment from all trips, exceeded the vertical ELV. Regardless of where the data were collected, fewer compartments exceeded the EAV in the horizontal orientation. Only BA compartments exceeded the ELV in the horizontal orientation. There were Area × Level interactions for vertical and horizontal RMS and VDV (P < 0.01). The BF compartment had a greater vertical RMS value than the TF, TC, and BC (P < 0.02) compartments, but did not differ (P = 0.06) from the TA compartment. The vertical RMS of the TA compartment did not differ from the TF, TC, and BC compartments (P > 0.13). The BF compartment had a greater (P = 0.02) vertical VDV value than the TC location, but did not differ from the other locations (P > 0.16). All other locations did not differ in vertical VDV (P > 0.12). The BF compartment had greater horizontal RMS than the TC and TA compartments (P < 0.01), but did not differ from TF and BC compartments (P > 0.12). All other compartments did not differ in horizontal RMS (P > 0.34). All compartments, aside from the BA compartment, did not differ in horizontal VDV (P > 0.19). Vibration analyses indicated the BA compartment had the greatest vertical and horizontal vibrations and a large percentage of the compartments exceed the EAV and ELV, which indicated pigs may have experienced uncomfortable trips that could cause discomfort or fatigue.

This paper presents a technique for the feature extraction of voltage signal for classification of open-circuited (O-C) faults in power electronic converters (PECs) connected to grid tied wind power system. The Empirical Mode Decomposition (EMD) was applied in the PEC output for the detection of the fault, as well as identification of the abnormal power switching device in the PEC. This method is applied on 12 types of signals including normal and faulted output voltage signals. Six features are computed from each Intrinsic Mode Functions (IMFs) obtained from EMD which are: Root Mean Square (RMS), Mean, Median, Variation, Standard Deviation and Entropy. The classification of the fault is achieved using Neural Networks (NN) and a comparison of classification precision between Probabilistic Neural Network (PNN) and Back-Propagation Multi-Layer perceptron (BPMLP) is also done. The overall classification accuracy concludes that EMD allows receiving better performance in the fault classification than other traditional methods like support vector machines, Wavelet.

A cab seat suspension with a magneto-rheological (MR) fluid damper is introduced in this paper. A unified-format model for the MR damper is proposed to describe the dynamic characteristics of the MR damper. Also, a simple force-inverse model and a viscous damping tracking model are used for the coil current solution. A digital integrator and an extended Kalman filter are respectively adopted to obtain the vibration velocity of the chair frame and the relative motion velocity of the MR damper piston. A new skyhook control base with viscous damping tracking is applied to the semi-active seat suspension. In the simulation, compared with passive seat suspension under different displacement excitation (2, 4, 6, 8 Hz-sine, and random), the acceleration root mean square of the seat suspension with the MR damper is reduced by 52.2%, 32.2%, 41.3%, 50.8%, and 34.6%, respectively. In the experiment, the acceleration root mean square is reduced by 11.2%, 41.2%, 45.8%, and 31.5%, respectively under different displacement excitation (2, 4, 6, and 8 Hz-sine).

The vibration-based pavement roughness measurement method has garnered significant attention in recent years due to the development of low-cost sensors. The vehicle calibration process is necessary before field measurement due to the effect of changeable vehicle dynamics, restricting the large-scale implementation of pavement roughness measurement. This paper proposes a novel calibration method for vibration-based pavement roughness measurement. It can quickly calibrate vehicle dynamic parameters through a few controlled impact tests. The model updating technique is employed to estimate vehicle dynamic parameters, resulting in the development of a full-car numerical model that accurately simulates the vibration of the test vehicle. A road-vehicle coupled simulation system is introduced, incorporating randomly generated road profiles and the calibrated full-car model. This system can simulate vibration responses under various conditions. A linear relationship between IRI (International Roughness Index) and vehicle vibration indices (e.g., RMS – Root Mean Square of vertical acceleration) is estimated based on simulated results. The efficiency and accuracy of the calibration method are validated on actual roads in Shanghai, China. The average IRI difference between the ground truth and test data reached 0.5 m/km in a 100 m road section, demonstrating the reliability and practicality of this fast calibration method.

The combination of artificial intelligence techniques and quantitative investment has given birth to various types of price prediction models based on machine learning algorithms. In this study, we verify the applicability of machine learning fused with statistical method models through the EMD-XGBoost model for stock price prediction. In the modeling process, specific solutions are proposed for overfitting problems that arise. The stock prediction model of machine learning fused with statistical learning was constructed from an empirical perspective, and an XGBoost algorithm model based on empirical modal decomposition was proposed. The data set selected for the experiment was the closing price of the CSI 300 index, and the model was judged by four indicators:mean absolute error, mean error, and root mean square error, etc. The method used for the experiment was the EMD-XGBoost network model, which had the following advantages: first, combining the empirical modal decomposition method with the XGBoost model is conducive to mining the time series data for Second, the decomposition of the CSI 300 index data by the empirical modal decomposition method is helpful to improve the accuracy of the XGBoost model for time series data prediction. The experiments show that the EMD-XGBoost model outperforms the single ARIMA or LSTM network model as well as the EMD-LSTM network model in terms of mean absolute error, mean error, and root mean square error.

Taking a type of tractor made in China as an example for no-suspension vehicle, 2DOF twin-shaft vehicle dynamic model and its natural frequencies were studied. Based on the analysis of vehicle with front axle suspension, 3DOF twin- shaft vehicle dynamic model of front axle suspension vehicle was build up. The two vehicle dynamic models were researched by MATLAB/SIMULINK, with the white-noise for random excitation. The relationship of vibration characteristics of the vehicle and parameter of front axle suspension was put forward, through adjusting the stiffness and damping during simulating. In this paper, vibration characteristics of vehicle without suspension and with front axle suspension were compared. The simulation results show that the root mean square of body vertical acceleration was reduced 5%, the root mean square of pitch vibration angle acceleration and angle displacement can be reduced 45% and 3.4% respectively, at the same time, the amplitude of parameter was reduced, which improved the driving performance of the vehicle and the driver's ride comfort.

While vision obviously plays an essential role in orienting and obstacle avoidance, its role in the regulation of dynamic balance is not yet fully understood. The objective of this study was to assess dynamic stability while blindfolded, under optimal conditions that minimized the fear of falling. The hypothesis was that visual deprivation could be compensated for by using other sensory strategies to stabilize gait. One hundred healthy adults (aged 20-69 years) participated in the study. They were previously accustomed to blindfolded treadmill walking wearing a safety harness. Their preferred walking speeds (PWS) were assessed with eyes open (PWSEO) and with eyes closed (blindfolded, PWSEC). Three five-minute tests were performed: (A) normal walking at PWSEO, (B) blindfolded walking at PWSEC, and (C) normal walking at PWSEC. Trunk acceleration was measured with a lightweight inertial sensor. Dynamic stability was assessed by using (1) acceleration root mean square (RMS), which estimates the variability of the signal, and hence, the smoothness of the trunk movement and (2) local dynamic stability (LDS), which reflects the efficiency of the motor control to stabilize the trunk. Although walking at PWSEC with eyes open (comparing conditions A and C) had a slight impact on gait stability (relative difference: RMS +4 %, LDS -5 %), no destabilizing effect of visual deprivation (B vs. C, RMS -4 %, LDS -1 %) was observed. Therefore, it is concluded that when reassuring conditions are offered to individuals while walking, they are able to adopt alternative sensory strategies to control dynamic equilibrium without the help of vision.

Vibration data provided by vehicle fleets can offer a continuous measurement system for pavement roughness, complementing profilometer surveys for network-level management. However, its heterogeneous nature—varying suspensions and speeds—poses significant application challenges. This study examines whether vertical accelerations measured above the suspension can predict the International Roughness Index (IRI) without requiring hard-to-obtain vehicle parameters or complex techniques, thus facilitating practical adoption by road agencies. Quarter-car simulations were performed on 36 measured pavement profiles for 18 representative vehicles spanning realistic parameter ranges and travel speeds (30–120 km/h). The root mean square (RMS) of vertical acceleration was computed for each run and related to IRI through six linear regression forms, including logarithmic variants accounting for speed. Monte Carlo analysis showed that aggregating RMS across nine vehicles reduced data dispersion and yielded the lowest prediction error. Achieving the best trade-off in accuracy, the logarithm model using separate RMS acceleration and speed terms yielded a root mean square error of 0.21 m/km and an adjusted R² of 0.84 on the test phase. This work also shows that a smoothed curve of mean RMS acceleration and box plot graphs can highlight rough segments when no IRI data is available, providing a rapid screening tool for maintenance prioritization. These findings indicate that collecting data from roughly 8 to 10 vehicles per segment and applying a simple logarithm calibration can provide stable and interpretable IRI estimates. The approach can potentially scale with connected-vehicle or smartphone fleets and guide agencies in continuous condition monitoring and targeted inspections.

In this study the ride values of passenger cars are investigated for Korean subjects based on the vibration of the human bodies. When three subjects are excited by driving a vehicle on the road, their responses of acceleration are measured at 12 points on their bodies according to Griffin's 12 axis system: 3 translational axes on a seat surface, 3 rotational axes on a seat surface, 3 translational axes at the seat back, and the 3 translational axes at the feet. Since one of the most important parameters for ride comfort is the level and duration of the root mean square acceleration experienced, the following ride values are evaluated for four different vehicles: the seat effective amplitude transmissibility (SEAT), the component ride value, and the overall ride value based on acceleration root mean square. For this purpose, frequency weighing functions and axis multiplying factors are used. The ride indices are also studied considering the seat dynamic characteristics with subjects. (A) For the covering abstract of the conference see IRRD 492369.

Watch your step: A pilot study of smartphone use effect on young females’ gait performance while walking up and down stairs and escalators

Purpose This paper aims to experimentally investigate the effect of surface texture on the stick-slip behavior of the guideway of machine tools, and to find significant pattern and optimal parameters to suppress the stick-slip. Design/methodology/approach A method using the root mean square (RMS) of force acceleration are introduced to evaluate the stick-slip with groove and dimple patterns, as well as different surface roughness and contact modes. Findings Smooth surfaces are more prone to stick-slip phenomena. Circular dimples show better anti-stick-slip performance compared to groove textures, while the area density of dimple textures is the key parameter. Practical implications Under low-speed conditions, the guideway of machine tools may exhibit a stick-slip phenomenon, which compromises the stability of micro-feeding, positioning and processing accuracy and may even reduce the service life of cutting tools and guideways. Originality/value RMS of force acceleration is suggested for quantitative assessment of stick-slip. The design frame of dimple pattern to suppress stick-slip is proposed.

Conventionally modeled as spring–damper system, the end-stops in vibration energy harvesters set a limit to the displacement of the proof mass at sufficiently high excitation levels. In some studies, it is seen that the end-stop parameters needed adjustment to fit the simulations to the measurements at particular operating condition. In this article, the discrepancy between the simulation and measurement results on varying the operating condition is investigated in detail. A check on sensitivity of an electrostatic in-plane gap closing energy harvester to the parameters end-stop stiffness, end-stop damping, and end-stop position at various biases and excitation levels is performed. The simulations at 3-V bias and root mean square (RMS) acceleration amplitude 0.6 g show a remarkable variation of 30 Hz in up-sweep jump-down frequency on varying end-stop position by 0.12 µm. The simulation results also show a significant increase in sensitivity of up-sweep jump-down frequency to end-stop damping on increasing excitation level at fixed bias. The article also discusses the sensitivity in jump frequencies to perturbations in the excitation signal due to the presence of noise, where the jump-down frequency becomes smaller as the noise level increases. The trajectories studied at 8-V bias and RMS acceleration amplitude 0.6 g with different end-stop parameters show a strong influence of the end-stop model parameters on the motion of the proof mass. A lumped model of the device is fitted to the measurements for a whole range of operating conditions with one fixed set of model parameter, where asymmetric end-stop positions and their effect on the device behavior are shown to be crucial. The results presented in this article show that in order to reproduce and analyze the measured behavior of the harvester over a range of operating conditions, very fine details in the model are significant.

Under dynamic conditions, the dynamic force between the suspended equipment and the car body is substantially increased. This increase not only affects the ride comfort but also substantially raises the likelihood of fatigue damage to the suspended equipment. A multiobjective analytical target cascading (ATC) optimization is proposed to improve the ride comfort of high-speed trains and reduce the vibration of the suspended equipment. A mathematical simulation model of the vehicle equipment system is established, and the acceleration frequency-response function expression of the car body and the suspension system is obtained. The comfort index of the car body and the acceleration root mean square (RMS) of the suspended equipment are calculated by combining the German vertical irregular track spectrum and the comfort filter function. Meanwhile, the ATC method is used to optimize the car body comfort index and the acceleration RMS of the underframe suspended equipment. Then, the availability of the optimization method is evaluated via numerical simulation. Compared with the original suspension scheme of the underframe equipment, when the running speed of the vehicle is 300 km/h, the RMS value of the vibrational acceleration of the underframe equipment after optimization decreases by 19.9 %, and the ride comfort indexes at the car body center and above the front and rear bogies are improved by 6.4 %, 0.1 %, and 1.1 %, respectively. Simulation results demonstrate that ATC optimization can improve the railway vehicle ride comfort and reduce the vibration of the suspended equipment. This paper provides a new approach to the suspension parameter design of equipment.