Accelerometer Research Articles

Predicting Condition States, Based on Displacement Data, Generated by Acceleration Sensors on Industrial Linear Vibrating Screens Through Neural Networks

Predicting Condition States, Based on Displacement Data, Generated by Acceleration Sensors on Industrial Linear Vibrating Screens Through Neural Networks

Medium-High-Frequency and High Sensitivity Fiber Optic Acceleration Sensor Based on F–P Interference

Medium-High-Frequency and High Sensitivity Fiber Optic Acceleration Sensor Based on F–P Interference

Investigation on piezoresistive properties of reduced graphene oxide treated glass textiles based multifunctional sensors

AbstractGlass fabric‐reinforced composites (GFRC) and other revolutionary engineered materials find extensive application in the aerospace, construction, and automobile sectors. It is challenging to predict damage under real‐time stresses due to the anisotropic behavior of composite materials. This study presents the development of a glass textile‐based multifunctional composite sensor and demonstrates its application as a change in resistance or gauge factor in structural health monitoring (SHM) systems. This sensor is coated with reduced graphene oxide (rGO) nanomaterial. Its electrical resistance is evaluated by examining the concentration of nanoparticles and varying geometrical parameters. A three‐point bending method assessed the piezoresistive behavior of the integrated sensor in the GFRC. The impact of sensor width and relative placements in the material's thickness direction within the composite samples is evaluated. Cyclic flexural testing was also performed to demonstrate real‐world applications. The research concludes that it can be utilized as a damage assessment technique for GFRC. The developed sensor can be used to provide an electrically conductive path or as a resistor in the field of E‐textiles. The developed piezoresistive sensor has the flexibility to be used as a multifunctional sensor for multiple purposes, such as force, torque, weight, pressure, flow, acceleration sensors, etc.Highlights Glass textile‐based multifunctional materials are reported as strain‐monitoring sensors. Tailored sensor resistance could be achieved through rGO current conduction paths. Glass textile‐based sensors could potentially be used in the field of E‐textile.

Multilevel attention mechanism for motion fatigue recognition based on sEMG and ACC signal fusion.

This study aims to develop a cost-effective and reliable motion monitoring device capable of comprehensive fatigue analysis. It achieves this objective by integrating surface electromyography (sEMG) and accelerometer (ACC) signals through a feature fusion strategy. The study introduces a multi-level attention mechanism for classification, leveraging convolutional neural networks (CNNs). The preprocessing phase involves a local feature attention mechanism that enhances local waveform features using the amplitude envelope. A dual-scale attention mechanism, operating at both channel and neuron levels, is employed to enhance the model's learning from high-dimensional fused data, improving feature extraction and generalization. The local feature attention mechanism significantly improves the model's classification accuracy and convergence, as demonstrated in ablation experiments. The model, optimized with multi-level attention mechanisms, excels in accuracy and generalization, particularly in handling data with pseudo-artifacts. Computational analysis indicates that the proposed optimization algorithm has minimal impact on CNN's training and testing times. The study achieves recognition accuracies of 92.52%, 92.38%, and 92.30%, as well as F1-scores of 91.92%, 92.13%, and 92.29% for the three fatigue states, affirming its reliability. This research provides technical support for the development of affordable and dependable wearable motion monitoring devices.

Investigating Electromagnetic Interference Induced Risks for Autonomous Driving

Abstract. The results of immunity tests on components of self-driving automobiles to radiated high power electromagnetic (HPEM) pulses are presented in this work. It is of particular interest to investigate such automobiles' resilience towards deliberate attacks with electromagnetic interference (EMI). Different types of HPEM capabilities at the Bundeswehr Research Institute for Protective Technologies and CBRN Protection (WIS) in Munster are applied for this investigation. Two types of automotive components have been tested, a two-axis acceleration sensor and an electronic power steering unit. Statistics of errors from the devices under test (DUTs), as well as a characterization of the applied pulses are presented. A correlation can be drawn between the pulse repetition rate or the amplitude of the applied pulses and the severity of the effect on the DUTs.

Experimental determination of damping coefficient of a support with elastic ring under harmonic loading

The article describes the possibility of determining the damping coefficient of a support with an elastic ring based on dry friction forces using a special test bench. The forced excitation force is set by the oscillations of a compact speaker installed on top of the oscillating mass through a U-shaped crossbar. The object of the study is a damper with an elastic ring, that is a thin-walled ring with evenly distributed bulges inside and outside, arranged in a checkerboard pattern. The damping coefficient is estimated using a device for simulating a rotor support, an acceleration sensor, an exciter speaker, a controller and a processing station. The amount of damping is estimated according to the width of the peak at the resonant frequency. A comparison is given of the magnitude of the damping coefficient obtained from the analysis of the resonance peak and from the decay rate during the impact experiment.

Model test study on the dynamic failure process of tunnel surrounding rocks in jointed rock mass under explosive load

The presence of joints can significantly reduce the integrity and stability of an engineering rock mass. Under dynamic loads, such as from blasting excavation, the key blocks divided by joints may be destabilized and prone to sliding, potentially leading to engineering geological disasters like rockbursts. To study the dynamic instability process, similar materials for the rock mass and joints were developed based on the similarity theory, and a tunnel model in the jointed rock mass was constructed. Subsequently, a detonating fuse was used to generate a dynamic load, and the dynamic instability process of the tunnel surrounding rock in the jointed rock mass under explosive load was studied using the geotechnical multifunctional testing device. The deformation characteristics and dynamic instability process of the tunnel surrounding rock were analyzed using acceleration sensors, resistance strain gages, linear variable displacement transducers and motion camera. The study shows that the acceleration at the tunnel vault is significantly greater than at the straight wall and floor under blast loads, with differences reaching an order of magnitude. Acceleration waveforms were classified into three categories based on peak characteristics, explained through the propagation of explosive stress waves. Additionally, strain and displacement at the tunnel arch were also significantly greater than in other areas, indicating more severe stress concentration and dynamic damage at the arch, necessitating reinforced support in tunnel excavation. The entire dynamic instability process of the tunnel surrounding rock was successfully recorded using a motion camera. The dynamic failure process was divided into several phases, the appearance of cracks on the joint surface, particle ejection accompanied by the dropping of jointed blocks, a significant drop of the jointed blocks, and return to calm. The dynamic failure modes include the dropping and rotation of jointed blocks, local particle ejection, and shear cracks on jointed block.

Identification of Disc Cutter Wear via Operation Parameters Combined With Vibration Data: A Case Study

ABSTRACTThis paper proposed an approach to estimate disc cutter wear utilizing a combination of multiple operational parameters and vibration data collected during shield tunneling operations. The incorporation of vibration signals, notably those originating from acceleration sensors mounted on the back plate of the soil chamber, has markedly enhanced the accuracy of the model. Time‐frequency domain features were extracted through analysis methods such as Fast Fourier Transform (FFT), Short‐Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). A predictive model utilizing vibration and shield operation parameters was developed using the XGBoost algorithm, and a deep GoogLeNet Convolutional Neural Network (CNN) was trained on time‐frequency graphs from the CWT. In addition, this study also investigated the impact of signal duration on wavelet image information and model accuracy. In the Huang‐Shang Intercity Railway Project, the approach effectively assessed disc cutter wear during tunneling operations and dynamically optimized the operational parameters of the shield tunnel machine through predictive analysis.

Design of monitoring system for material vibration screening equipment

In order to effectively reduce the failure rate of vibrating screens, a vibration monitoring system design was proposed based on the structure and working principle. The system should be able to monitor the operating state of the vibrating screen in real time, including vibration amplitude, frequency, axial displacement, etc., and be able to detect abnormal conditions in time and give warning prompts. The key hardware of the monitoring system was designed, including acceleration sensor, power module, signal filtering circuit, etc. The sensitivity of the acceleration sensor was measured using comparative method and accurately completed parameter calibration. The eccentric block was used to simulate different deviation of excitation force for system vibration signal test and calibration. The results show that under normal operating conditions, the amplitude of each frequency of the vibrating screen is less than 0.04 g. Under the condition of excitation force deviation, the amplitude of 16 Hz is obviously lower than that of other frequencies, which means that the vibrating screen is currently experiencing lateral oscillation.

Development of Laminated Egg-Shaped Tsunami Shelter Structure Made of Steel-Cushioning-Steel

When a tsunami is caused by an earthquake or other event, spherical shelters are developed to protect people from the tsunami. This study proposes a new egg-shaped laminated tsunami shelter with a buffer layer to improve the functionality of traditional spherical shelters. The inner and outer shells of this shelter are made from thin-walled stainless steel, using the integral hydro bulge forming (IHBF) process. The space between these two layers was filled with urethane foam, providing an elastic buffer. This resulted in a laminated egg-shaped structure designed for tsunami protection. To verify the proposed laminated egg-shaped tsunami shelter and its processing method, an egg-shaped shell with an external shape (length 660 mm, width 493 mm) was fabricated using a 1.0 mm thick stainless plate, and a laminated egg-shaped tsunami shelter with a 25 mm thick intermediate layer made of urethane foam was fabricated. The shape accuracy of the processed egg-shaped laminated tsunami shelter structure was measured, and the maximum error between the surface shape of the molded egg-shaped shell and the true egg shape was -4.13 mm, and the relative error to the maximum radius of the egg shape of 246.5 mm was -1.68%. In addition, to assess the buffering effect under external impact loads, acceleration sensors were attached to both the inner and outer layers of the fabricated egg-shaped laminated tsunami shelters. A hammer was used to apply an impact load to the outer layer, and the response acceleration values recorded by the sensors on both layers were compared. It was found that the response acceleration of the inner layer was 15.81% lower than that of the outer layer.

Resonance Testing Data Evaluation Approaches for Scaling Onset Detection in Pipelines

Most industries dealing with pipelines face problems resulting from the buildup of deposits therein, such as reduced efficiency, downtime and increased maintenance costs. Although solutions to this issue have been sought for decades, no widely employed technique for monitoring growth of inorganic deposits (or ‘scaling’) in pipelines exists. In this research, a means of detecting the onset of scaling growth, by processing resonance testing data, was sought. For the resonance testing measurements the pipeline segment of interest is equipped with acceleration sensors which record signals generated by impacting the pipeline with a steel tip. The signals are Fourier transformed and analysed in the frequency domain, where a clear shift in frequency peak positions can be observed as the scaling thickness changes. How best to extract quantitative information from the generated frequency data is an open question. In this research, two data analysis approaches for scaling thickness prediction are compared: a supervised (binary classification) machine learning approach as well as a comparison-based change detection approach using cross-correlation. The supervised machine learning approach yields generalizable results for different acceleration sensors and impactor diameters whilst the change detection approach is sensitive from a scaling thickness of 0.5 mm. Whilst this research is specific to the pipe–scaling geometry—and type used in the experiments conducted, resonance testing can be applied to any pipe–scaling combination. The robustness of the data processing approaches presented in this work, when applied to other pipe–scaling materials and geometries, is the next point of research.

Electric vehicle ramp recognition based on fusion of vehicle mass estimation

Road slope is the necessary environmental information for intelligent electric vehicles. The accuracy of slope recognition directly determines the control quality of vehicles on the hill. Aiming at the problems that the existing ramp recognition algorithms have poor adaptability to the conditions and are unable to To satisfy the application requirements of mass production vehicles, this paper proposes an electric vehicle slope recognition method based on vehicle mass estimation. Firstly, the vehicle longitudinal dynamics model is established, and the signal characteristics of the acceleration sensor under the actual vehicle conditions are analyzed. The least square vehicle mass estimation strategy with forgetting factor is constructed to obtain the vehicle mass directly under the starting condition. Ramp recognition algorithms are designed for static parking and dynamic driving scenarios. In the static scenario, the filter latch strategy is used to deal with the interference factors such as activities in the vehicle. In the dynamic scenario, the Kalman filter algorithm based on measurement noise adaptation is designed to realize the fusion estimation of dynamic observation and kinematic observation for the slope. The effectiveness of the method is verified by Simulink -CarSim joint simulation. Finally, the real vehicle test is completed on Chery new energy's mass production electric vehicle platform and domain controller. road test results show that the mass estimation error is less than ±10 kg; the static estimation error of the slope is less than 0.001 rad, and the dynamic error is within 0.005 rad. The estimation accuracy and stability are greatly improved, which ensures the environmental adaptability of the intelligent electric vehicles.

Study of the Mechanism of Perceived Rotational Acceleration of a Bionic Semicircular Canal on the Basis of the "Circular Geometry Hypothesis".

Academia often uses the "circular geometry hypothesis" to explain the sensing principle of the human semicircular canal (SCC) system for angular acceleration, which is widely accepted as an important angular acceleration sensor in the human balance system. On the basis of this hypothesis and the anatomical structure of human SCCs, a series of physical SCC models with different geometries at 4× magnification were prepared via three-dimensional printing and modification of hydrogels. Theoretical models of the SCC perception mechanism were established. Then, impulse angular acceleration, sinusoidal rotation, and sinusoidal linear stimulation were applied to the models, and their responses were visually observed and analyzed in detail. As a result, the circular SCC model had a larger system gain and a smaller phase difference for angular acceleration stimulation but a smaller system gain and a larger phase difference for linear acceleration stimulation. These results verified that the circular semicircular canal was more sensitive to angular acceleration. Our bionic model is hoped to be used for demonstrating the human SCC working process, facilitating researchers in better understanding of the working mechanism of the human SCC, or as a manual model for medical staff to simulate the diagnosis and treatment of human SCC.

Effects of epileptic seizures on the quality of biosignals recorded from wearables.

Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri-ictal periods in performing a feature-based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring. This study assesses the signal quality of wearable data during peri-ictal phases of generalized tonic-clonic and focal to bilateral tonic-clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri-ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS. We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre- and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity. Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist-worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts.

Synthetic image data generation via rendering techniques for training AI-based Instance Segmentation

Within the scope of the development of the project, the acceleration and gyro signal, VR implementation, and User Control for VR were realized. The starting point was a list of MPU9250 data, which are acceleration and gyro data. The goal is to do the signal processing of the signal that is got from MPU9250 and implement the data with a specific format so that the LINAK LA36 actuator can be controlled in such a way that it performs as desired position. For this purpose, a multi-axis acceleration and position sensor is mounted on a surfing board which then collects the acceleration and Gyro data. A MATLAB Program had been written to convert the desired position into a CAN message. Before that, the raw acceleration data and gyro must be converted and filtered into actual data. First, the acceleration data was collected from another group. A signal processing method has been implemented. This includes gravitational force filter, noise filter, integration, and complementary filter method was used in order to get the accurate actual data that is able to be implemented into the system. For those methods, it was necessary to deal extensively with the characteristics of signal processing. VR application was implemented in this project to give the user a sensational surfing experience in the virtual world. User control has been implemented as well to allow users to control the video. Unity with Arduino work together so that both Arduino and Unity can communicate with each other, and signal messages will be sent to the Arduino from Unity and vice versa to achieve the synchronization of the video signal and Unity VR application.

High-sensitivity acceleration sensor detecting micro-mechanomyogram and deep learning approach for parkinson’s disease classification

High-sensitivity acceleration sensors have been independently developed by our research group to detect vibrations that are > 10 dB smaller than those detected by conventional commercial sensors. This study is the first to measure high-frequency micro-vibrations in muscle fibers, termed micro-mechanomyogram (MMG) in patients with Parkinson’s disease (PwPD) using a high-sensitivity acceleration sensor. We specifically measured the extensor pollicis brevis muscle at the base of the thumb in PwPD and healthy controls (HC) and detected not only low-frequency MMG (< 15 Hz) but also micro-MMG (≥ 15 Hz), which was preciously undetectable using commercial acceleration sensors. Analysis revealed remarkable differences in the frequency characteristics of micro-MMG between PwPD and HC. Specifically, during muscle power output, the low-frequency MMG energy was greater in PwPD than in HC, while the micro-MMG energy was smaller in PwPD compared to HC. These results suggest that micro-MMG detected by the high-sensitivity acceleration sensor provides crucial information for distinguishing between PwPD and HC. Moreover, a deep learning model trained on both low-frequency MMG and micro-MMG achieved a high accuracy (92.19%) in classifying PwPD and HC, demonstrating the potential for a diagnostic system for PwPD using micro-MMG.

Machine learning-driven intelligent tire wear detection system

Traditional methods detect wear by interpreting mathematical models and tire characteristics; however, these methods struggle to accurately reflect the actual rolling condition of the tire. In this study, we propose a machine learning-based tire wear detection module that can provide accurate results under tire test rig conditions. To develop this module, we designed three key components: integrated acceleration and PVDF sensors within the tire to capture vibration and deformation data; signal preprocessing algorithms to highlight multi-source signal differences under varying wear conditions; and deep learning algorithms to achieve precise tire wear grade identification. Experimental results demonstrate that, under different tire pressures, loads, speeds, and wear levels, the system can accurately identify tire wear grades with 99.99% accuracy by combining data from both sensors.

The calculation and evaluation of acceleration errors produced by position movement of acceleration sensors on the seat of a human centrifuge

Abstract With the increasing requirement for pilot anti-overload ability training, the man-machine engineering problems caused by the existing seat on a human centrifuge are becoming increasingly prominent, making it challenging to meet actual requirements and needing to be upgraded. After upgrading, the seat can be quickly adjusted in front, back, up and down. Acceleration sensors fixed on the seat will also generate displacement, which will result in errors. Three-axis acceleration values are a crucial technical index in human centrifuge systems, and their errors need to be within a certain accuracy range in order to make the system operate normally. Otherwise, the system will produce serious consequences such as alarm and shutdown. Analysis shows that the position movement of acceleration sensors on the seat has no impact on the control system, mainly affecting the safety system. This study discusses the situation when acceleration errors in seat adjustment reach the maximum values, and calculates the three-axis acceleration errors in detail. It is observed whether the values are within the set limits by the safety system in order to evaluate whether they have an impact on the safety of the entire system. The result indicates that the acceleration errors caused by the adjustment of the upgraded seat are within a controllable range and will not have any adverse effect on the safety system of the human centrifuge. Therefore, the acceleration errors will not have any adverse effect on the safety of the whole system.

Kinematic Analysis of the Vibration Harvesting Process of Lycium barbarum L. Fruit

The traditional shrub fruits harvesting method is manual picking, while the efficiency is low, which seriously restricts the development of Lycium barbarum L. industry. In order to mechanize the harvesting process of Lycium barbarum L. and improve the correct picking rate while reducing the damage rate of Lycium barbarum L. harvesting, it is very important to analyze the kinematic model of the fruit-bearing branch during vibration harvesting. Through the measurement and analysis of the natural characteristics and physical parameters of the branches, a simplified model of Lycium barbarum L. shrub fruit-bearing branch was built by Solidworks 2023 software, and the appropriate material properties were selected. Through modal analysis and harmonious response analysis, the response characteristics data of fruit-bearing branches of Lycium barbarum L. shrub were obtained. In Qinghai Nuomuhong Farm, the field vibration harvesting kinematic model feature analysis test was carried out, and the acceleration data of the vibration harvesting process were collected by using the acceleration sensor, and through the analysis of the frequency spectrum characteristics of the data, it was concluded that when the excitation frequency was maintained between 8 and 14 Hz, the Lycium barbarum L. fell off well and the picking rate can reach 97.56%, the efficiency can reach 6.88 pieces of fruit per second, and the branch damage was acceptable, which theoretically met the needs of harvesting.

A simulation study on visual preview control of vehicle magnetorheological suspension

Abstract At present, the boom in intelligent vehicles, especially automotives, has brought magnetorheological (MR hereinafter) suspensions into further focus. Generally, traditional MR suspensions work with acceleration sensors to respond with a non-negligible time delay issue due to the inductive property of MR damper (MRD hereinafter), which hinders their full performance especially at high-speed driving with a powerless “blind zone”. To address this issue, this study proposes a simulation study on visual preview control for vehicle MR suspension based on MPC (Model Predictive Control), since the road preview is able to compensate the time delay due to the future road information perceived in advance. First, a camera with a computer vision algorithm is employed to preview road information of target type, target size and distance to wheels. Besides, the error correction of radial and tangential distortions of camera lens are investigated, and the trajectory of wheels are predicted by a cycloid model because MR suspension only needs to respond to the road targets that the wheels will pass over. Further, the area in front of the vehicle is divided into a recognition zone and action zone, and the road targets within action zone (&lt;1.5 m) are calculated more precisely for a prepared immediate response from the MR suspension. Then, the performance of a simulating vehicle MR suspension is investigated through a joint simulation by the automotive simulation software CarSim and the model-solving Simulink. The evident results show that the MR suspension with visual preview control performs the best in ride comfort (mean square values of sprung mass acceleration is 82% and 52% lower compared to a passive suspension and a MR suspension with skyhook control, respectively). Therefore, the visual preview control strategy of MR suspension is able to enhance the performance on the discrete road conditions.