Wireless Acceleration Research Articles

Efficient bridge steel bearing health monitoring using laser displacement sensors and wireless accelerometers

Steel bearings have been commonly used to counteract induced loading from thermal and traffic conditions in numerous bridges. However, their effectiveness has been compromised due to aging and maintenance limitations, potentially impacting the overall bridge system performance. Existing monitoring techniques for detecting malfunctioning steel bearings lack automation and precision, making them inadequate for long-term and real-time bridge dynamics assessment. This study proposes a response-based approach to identify bearing malfunction by analyzing the traffic-induced response in the bearing vicinity. To implement this approach, laser displacement sensors and wireless acceleration sensors were employed to monitor both malfunctioning and well-functioning steel bridge bearings. Significant differences in bearing performance were observed through response analysis and comparison. Laser sensor measurements revealed larger vertical deflections in the girder at malfunctioned bearing under traffic loading. Moreover, the investigation of the acceleration response in the bearing locality indicated that bearing malfunction could alter the vibrational characteristics of the vicinity, significantly affecting Cross Power Spectral Density (CPSD) and cross-correlation. To quantitatively evaluate the performance of steel bearings, a Condition Score (CS) was introduced. The CS exhibited a strong correlation with bearing damage, providing valuable insights for maintenance and decision-making processes in bridge asset management. This study offers a comprehensive and automated method for identifying steel bridge bearing malfunction by utilizing advanced monitoring techniques and introducing the CS for assessment. The results obtained from this approach can enhance bridge maintenance strategies and contribute to effective bridge asset management.

Low energy split-cluster wireless acceleration sensors for motion and posture change recognition in sports

The rise of the national fitness trend has led more and more people to join the ranks of physical fitness, and the current physical education resources and human resources have not been able to keep pace with the development of sports. Due to the lack of guidance, many beginners of sports are not standardized, and their movements are not effective and even cause injuries. Sports action recognition technology can make up for this lack and realize the action recognition and guidance for sports players. In this paper, we build a sports movement and posture recognition model based on acceleration sensors, introduce Selectable Mode Vocoder (SMV) algorithm to optimize the performance, and ensure the effectiveness of sensor data transmission through a low energy multi-layer clustered wireless sensor network. The experiment mainly compares the generation results of the first energy depleted node in the entire network with the number of rounds with energy depleted nodes reaching 10 %. The experimental results show that the low-energy multi-layer clustered wireless sensor network effectively shortens the data transmission path, reduces the energy consumption of nodes, extends the network life time, ensures the data transmission effectiveness, and provides network guarantees for sports action recognition. In addition, in the comparison experiments, the action recognition model in this paper shows better recognition effect and efficiency with obvious performance advantages.

A Passive Wireless Acceleration Sensing System Based on Patch Antenna and FMCW Radar

Traditional acceleration sensors use cables to transport data and batteries to supply power, which may increase failure risk, deployment inconvenience and maintenance difficulty. This paper proposes a passive and wireless acceleration sensing system, namely a newly designed patch-antenna-based sensing node interrogated by frequency-modulated continuouswave (FMCW) radar. The acceleration is correlated with the location of a cantilever beam, whose oscillation alter the additional capacitance of the patch antenna and consequently the resonant frequencies. The testing range can be adjusted by change the material and dimensions of the cantilever beam, which is designed up to 1 110m/s2 with a sampling rate of 500Hz to meet the requirements of different conditions. The working mechanism of the sensing system is analyzed theoretically and then verified by the simulation in COMSOL Multiphysics. Then an experiment is carried out to evaluate the performance of the sensing system. Both the resonant frequency and remaining power can be utilized to extract the acceleration response, and results show a good fit with the actual response (i.e., an average error of 4.5%). Empowered by FMCW Radar, the passive accelerometer may have great potential in identifying structure strain mode, impedance-based detection of bolt loosening, automation in construction, etc.

Development and Application of Vibration Comfort Detection Device for Escalator Based on MEMS Acceleration Sensor

Vibration comfort is an important indicator for evaluating escalator riding experience. The measuring principle of vibration comfort of escalator step and armbands was analyzed. The calculation methods of vibration acceleration extraction, frequency meter weight, root mean square (RMS) and vector sum were summarized. A wireless triaxial acceleration data acquisition module was developed based on micro-electromechanical system (MEMS) acceleration sensor, MCU and high-speed WIFI module, and a vibration comfort detection application (APP) based on the Android operating system was developed. The calibrated detection device was applied to escalator field test. The results showed that the maximum vibration RMS and average RMS of the step and the armband on both sides can be quickly calculated, and the vibration comfort level can be divided, which provided important data reference for equipment maintenance.

Research on Online Monitoring and Analyze of Hydraulic Turbine Based on Low Cost, Fast and Internet of Things Technology

In view of the problems such as difficulty in and high cost of vibration signal collection of rotating mechanical rotating shaft, this paper proposes a low-cost, fast, stable and reliable hydraulic turbine rotating wheel vibration signal online monitoring scheme specially designed to the imbalance and dynamic balance counterweight of hydraulic turbine rotating shaft. Three high-frequency wireless acceleration sensors are installed on the rotating shaft to monitor the vibration signals of the three directions in real time. Vibration signals are sent to the cloud platform to facilitate data storage. The acceleration signal is decomposed by WOA-VMD. The time domain signal shows the running track of the shaft to guide the dynamic balance counterweight of the rotor. The frequency domain signal is used to analyze the machine vibration in the shaft frequency domain and predict the health status of the shaft according to the frequency change. According to this system, completed the experiment of a hydraulic turbine experiment, proved the feasibility and rationality of the system, and has good application prospect and practical value.

Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model

Rolling bearings are one of the most widely used bearings in industrial machines. The technical condition of the rolling bearings has a significant impact on the total condition of rotating machinery. Typically, the rolling bearing condition monitoring is based on signal processing from accelerometers mounted on the machine housing. AI-based signal processing methods allow achieving high results in the diagnosis of rolling bearings. Moreover, hybrid NN-based signal processing methods provide the best diagnostic results. However, the diagnostic value of each signal from the housing-mounted accelerometers is highly depend on the location of the corresponding accelerometer. On the other hand, mounting a sensor on the rotating shaft allows expanding and increasing the signal diagnostic value. This paper proposes a novel hybrid CNN-MLP model-based diagnostic method which combines mixed input to perform rolling bearing diagnostics. The method successfully detects and localizes bearing defects using acceleration data from a wireless acceleration sensor which is mounted on a rotating shaft of the machine. The detection and localization efficiency of the hybrid model with various size datasets (more than 27 k samples and about 1900 samples) for training was estimated. Moreover, the detection and localization efficiency of the trained hybrid model with a dataset of another shaft rotating speed for testing was estimated. The experimental results show that applying the hybrid model allows detecting and localizing the bearing faults with up to 99.6% accuracy.

Experimental Investigation on Pedestrian Walking Load in Steel Footbridges

Accurate simulation of walking load is of great significance in conducting human-induced vibration analyses. However, accurate pedestrian walking load data obtained from long-span footbridges is scarce and data reliability depends on the sensor used for measurement. In the current work, Yanluo Footbridge with 102 m span was adopted as test site and Xnode high-precision acceleration wireless sensor was applied for measurements. An experimental investigation was performed on walking loads according to bipedal walking force model. In experimental studies, single-person and multi-person walking tests were performed at Yanluo Footbridge to measure corresponding stride frequency and dynamic load factor. The acceleration time-histories of walking pedestrians were accurately recorded using three-axis wireless acceleration sensor Xnode. Furthermore, the equation of dynamic load factor was derived by analyzing time-histories and power spectra and the design models of pedestrian walking load and crowd load were developed based on a great number of experimental data. Time histories of pedestrian walking loads showed regular periodic changes and dynamic load factor increased by increasing stride frequency. Using the walking load model developed in this work, the reliable structure response of human-induced vibration analysis can be obtained.

Development and Validation of a Framework for Smart Wireless Strain and Acceleration Sensing

Civil infrastructure worldwide is subject to factors such as aging and deterioration. Structural health monitoring (SHM) can be used to assess the impact of these processes on structural performance. SHM demands have evolved from routine monitoring to real-time and autonomous assessment. One of the frontiers in achieving effective SHM systems has been the use of wireless smart sensors (WSSs), which are attractive compared to wired sensors, due to their flexibility of use, lower costs, and ease of long-term deployment. Most WSSs use accelerometers to collect global dynamic vibration data. However, obtaining local behaviors in a structure using measurands such as strain may also be desirable. While wireless strain sensors have previously been developed by some researchers, there is still a need for a high sensitivity wireless strain sensor that fully meets the general demands for monitoring large-scale civil infrastructure. In this paper, a framework for synchronized wireless high-fidelity acceleration and strain sensing, which is commonly termed multimetric sensing in the literature, is proposed. The framework is implemented on the Xnode, a next-generation wireless smart sensor platform, and integrates with the strain sensor for strain acquisition. An application of the multimetric sensing framework is illustrated for total displacement estimation. Finally, the potential of the proposed framework integrated with vision-based measurement systems for multi-point displacement estimation with camera-motion compensation is demonstrated. The proposed approach is verified experimentally, showing the potential of the developed framework for various SHM applications.

Research and Development of a Wireless Self-Powered Sensing Device Based on Bridge Vibration Energy Collection.

Traditional bridge monitoring has found it difficult to meet the current diversified needs, and frequent replacement of sensor batteries is neither economical nor environmentally friendly. This paper presents a wireless acceleration sensor with low power consumption and high sensitivity through integrated circuit design, data acquisition and wireless communication design, package design, etc. The accuracy of the sensor in data collection was verified through calibration and performance comparison tests. The ability of triangular piezoelectric cantilever beam (PCB) was tested through design and physical manufacture. Finally, the self-powered performance of the sensor was tested by connecting the sensor and the triangular PCB through a circuit, which verifies the feasibility of using the PCB to collect bridge vibration energy and convert it into electrical energy to supply power for sensor, and also explore the green energy collection and application.

An Autonomous Vibration-Sensing System for Power Transmission Lines Monitoring

An autonomous vibration-sensing system including of ring magnetoelectric energy scavenger, an energy management module, a plurality of wireless acceleration sensor nodes and APP mobile terminal for power transmission lines monitoring was presented. The ring-type ferrite/piezoelectric composite with strong magnetoelectric couplings and a coil wound around it is used to scavenge the electromagnetic energy around the conductor, and the energy management module successively fulfill the functions of inductive/capacitive resonance matching, rectification and voltage stabilization and storage/release of the scavenged electric energy, and then provides driving power for signal acquisition and data transmission of 4G wireless acceleration sensor nodes. Testing results show that the collected energy can fulfill the power-supplying requirements of ADXL345, and the real-time as well as historical data curves within a specified time can be acquired through 4G and narrowband internet of things technologies.

Demonstration Experiment of Autonomous Wireless Acceleration Monitoring System Using La doped and Laminated PZT Element as Power Source

Demonstration Experiment of Autonomous Wireless Acceleration Monitoring System Using La doped and Laminated PZT Element as Power Source

Ankle foot motion recognition based on wireless wearable sEMG and acceleration sensors for smart AFO

Ankle joint is one of the important anatomical structures of the human body, smart ankle-foot orthosis(AFO) can assist human walking and improve the ankle motion for patients. This study focused on ankle foot movements recognition based on data fusion via sEMG and acceleration sensors. A wireless signal acquisition system (WAS) was designed specifically, forming a platform to demonstrate and record individual sEMG and acceleration data simultaneously. In the experimental tests, three channel sEMG signals from Tibialis Anterior (TA), Gastrocnemius (GM) and Soleus (SO), as well as three-axis acceleration data of the ankle joints, were collected when subjects performed four kinds of typical motions including dorsiflexion, plantar flexion, eversion and inversion. A total of 21,600 frames of sEMG /acceleration action data were constructed and then different kinds of classification algorithms were studied to classify the motions by the principal component analysis (PCA) based data fusion signal features. Results showed that the classification accuracy of bi-directional long short-term memory (BiLSTM) algorithm performed the best compared with traditional networks such as support vector machine(SVM), artificial neural network (ANN) and reached 99.8 %. These results demonstrated the potential application for accurate ankle foot intent identification by sEMG and acceleration sensors, which provided the basis for further implementation of subsequent smart AFO manipulation.

A cantilever-driven rotor for efficient vibration energy harvesting

The pervasive renewable vibration energy has been considered as a promising alternative to electrochemical energy of batteries for powering wireless sensors and wearable electronics, but its efficient harvesting is still an unsolved problem. To tackle this issue, this paper presents an innovative mechanical modulation mechanism, which we name ‘cantilever-driven rotor’, to convert vibrations to uni-directional rotation aiming to achieve improved energy harvesting performance. Compared with the conventional cantilever-based energy harvesters (CBEHs), the rotor-based energy harvester (RBEH) can provide both enhanced output power (1.8 mW versus 0.3 mW) and extended working bandwidth (4.5 Hz versus 1.9 Hz) under a harmonic vibration of 0.8 g (1 g = 9.8 m/s2). Moreover, electric outputs of the RBEH can persist for 27 s after the external excitation vanishes. With the electric energy generated by the RBEH from the harmonic vibration, a wireless acceleration sensor could be powered to perform with its full functionality. When attached to the human ankle, the RBEH can maintain the normal operation of a Timer under a walking speed of 6 km/h. This work provides a basically different vibration-to-rotation conversion mechanism with superior performance in vibration energy exploitation and potential applications in self-sustained wireless sensors and wearable electronics.

Theoretical and experimental study of the looping pendulum

We investigate the looping pendulum phenomenon proposed by the International Young Physicists’ tournament (IYPT) in 2019 with experiments and theoretical model based on Newtonian mechanics. When using high-speed video to measure the motion of objects, the acceleration measurement error may worsen significantly at a high frame rate due to position uncertainty. This issue is emphasized and can be resolved by additionally using a wireless acceleration sensor. The falling acceleration curves of heavy-load given by our theoretical model are in good agreement with the experimental measurement data. The mass ratio-release angle parameter space for a successful demonstration is given by numerical simulation, showing that a large value of heavy-light mass ratio should be more likely to make a successful demonstration experiment. The experiments reported here are appropriate for junior undergraduates to carry out experimental research.

Vibration measurement of a steel building with viscoelastic dampers using acceleration sensors

With the rapid development of sensor technology, wireless measurement systems are applied to various fields, such as safety monitoring and health evaluation of existing buildings. This study aims to develop a vibration measurement based on wireless acceleration sensors. This paper presents a simple response evaluation method of a steel structure with a stud-type damper composed of viscoelastic material and two steel columns. The series of measurements consists of micro-tremor measurements and seismic motion monitoring before and after seismic retrofitting. The changes in vibrational properties indicate the damping effect. To obtain the vibrational properties of the structure correctly, we investigated the natural frequency, deformation mode, and restoring force characteristics. The effectiveness of using a wireless system in the evaluation of existing buildings and the feasibility of the evaluation method can be obtained using the results of this experiment. Despite being the result of only one structure, this simple vibration measurement method's effectiveness is demonstrated.

Evaluation of Driving Characteristics of Wireless Acceleration Sensor by Vibration Power Generation Using La-doped Laminated PZT Elements

Evaluation of Driving Characteristics of Wireless Acceleration Sensor by Vibration Power Generation Using La-doped Laminated PZT Elements

Mathematical modeling and multi-objective optimization design of eccentric telescopic rod conveyor

In order to improve the conveying performance of the eccentric telescopic rod conveyor, theoretical analysis is used to find rotation center position of the telescopic rod and the relationship among various structural parameters of the conveyor with position alteration of the eccentric axis. A mathematical model of one periodic conveying capacity of the device and the velocity and acceleration equations of the telescopic rod end are established. The multi-objective optimization analysis are done by the virtual prototyping technology and the multi-island genetic algorithm, which takes the maximum conveying volume of one periodic and minimum acceleration of the rod end as the optimization goals. The results show that after multi-objective optimization, the conveying efficiency is improved, while the impact of telescopic rod striking on materials during the conveying process is reduced and the overall performance is better. Then, an example is taken to verify the mathematical models and multi-objective optimization analysis results, and the wireless acceleration test system is used to test the telescopic rod end acceleration before and after the structural optimization. The experimental and mathematical model calculation results show good agreement before and after the structural parameters’ optimization. The study shows that the mathematical model and the multi-objective optimization results are credible. It provides a theoretical and methodological reference for the practical design and optimization of eccentric telescopic rod conveyor.

Improved modal identification using wireless continuous dynamic monitoring systems without real time synchronization

In the context of vibration based Structural Health Monitoring using automated operational modal analysis, data synchronization methods permit a reduction in the cost of wireless monitoring systems, since the use of extra hardware for perfect data synchronization can be avoided. Furthermore, the relaxation of data synchronization requirements permits a more flexible topology (higher distance between sensors, no need of external antennas for data synchronization using GPS, lower power consumption), which is particularly advantageous in the dynamic monitoring of large structures. In this paper, continuous dynamic monitoring without real-time synchronization is studied. Firstly, using numerical simulations, the effects of synchronicity faults on operational modal analysis is deeply analysed, showing that time delays have a great impact on modal identification. Secondly, a new data synchronization method based on mode shapes identified with operational modal analysis methods is proposed to improve the accuracy of modal identification. This does not need the assumption of proportional damping. Therefore, it overcomes this limitation that up to the knowledge of the authors is present in all other available methods. The proposed processing strategy is validated in the processing of monitoring data continuously collected at a long span arch bridge (Infante D. Henrique Bridge) with two wireless acceleration nodes. The full-scale application shows high time synchronization accuracy and proves that the proposed synchronization method is adequate for continuous dynamic monitoring, providing reliable modal parameters estimates with any assumptions about the damping, permitting a reduction in the monitoring costs, easier installation and power consumption reductions (very important to increase the autonomy of wireless nodes). In short, hardware costs are replaced by a smarter processing.

Tunnel damage identification method based on relative entropy of wavelet packet energy: An experimental verification

Ineffective methods used to detect damage in subway tunnels can result in severe safety risk. Traditional detection methods use laser scanning, image recognition, and inspection to identify damages in the inner lining of a tunnel after the subway is out of service; these methods have low efficiency and accuracy. In this study, a new method is proposed to analyze the vibration signal of a moving train based on the relative entropy of the wavelet packet energy, which can quickly identify and evaluate the damage of the subway tunnel and its auxiliary structure in real time. The test model includes a three-dimensional printed tunnel and a moving vehicle to simulate the train running in the tunnel. Wireless acceleration sensors are installed inside the vehicle and at the top of the tunnel to record the vibration signals of the car and the tunnel lining. After obtaining the acceleration signal of the vehicle, the relative entropy of energy is calculated using wavelet packet transform, in which sudden changes in entropy reflect damages. The model test results show that the vertical acceleration of the vehicle is sensitive to tunnel damage and the signal energy is mainly concentrated at 30–80 Hz and 200–400 Hz. By comparing the energy of the relative entropy between healthy and damaged signals, the location and degree of damage can be identified.

Monitoring of human body running training with wireless sensor based wearable devices

With the rapid pace of today’s society, work pressure, less exercise time, people began to pay more attention to their health. Walking and running have become the first choice of moderate exercise for many young people. The recognition of human running state based on wireless acceleration sensor will play an increasingly important role in the fields of motion detection, energy consumption evaluation and health care. It is of great significance to develop and design a kind of wearable multi-functional wireless sensor which can monitor the running state of human body. In this paper, a wearable human body monitoring system based on wireless acceleration sensor technology is proposed for real-time monitoring of daily running volume of human body. Hardware and upper computer design: stm32f405 is used as the main control chip, and ma8451q is used to collect human motion data. In this paper, aiming at the problem that three kinds of motion states of human body are easy to be confused and difficult to distinguish, based on the in-depth study of the complex structure mode and self similarity characteristics of non-stationary acceleration signal, a method of human body motion state recognition based on single fractal and multi fractal is proposed. In this method, the fractal dimension and the generalized dimension are used as the feature variables, and the correlation judgment method is used to distinguish and recognize different motion states. Experiments show the validity and feasibility of single fractal and multifractal in walking and going up and down three kinds of motion state recognition. On the basis of multifractal motion state recognition, this paper combines fractal theory with wavelet multiresolution analysis, and proposes a matrix fractal human motion state recognition method based on wavelet transform. The fractal matrix based on wavelet transform quantifies the fractal characteristics of the component signals of walking and going up and down in different wavelet scales, and then describes the complexity and self similarity of the original acceleration signals. Experimental results show that the average recognition rate of walking, jogging and fast running can reach over 93% under the premise of less prior information.