Real-time Vibration Research Articles

Indoor tests of sensor-enabled piezoelectric geocable–geogrid composite structure for slope rehabilitation and monitoring

Sensor-enabled piezoelectric geocables were combined with a geogrid to acquire a sensor-enabled piezoelectric geogrid (SPGG) based on the impedance–strain relationship. Tension, pullout, and straight shear tests were conducted on this SPGG configuration. The tension test results indicated that the tensile strain–normalized impedance curves were exponential in form within the first 7 % of strain and the rate of shift in impedance was independent of the tension loading rate. An excellent correspondence between the peak strength and impedance inflection point was observed in the results of the pullout and straight shear tests. Additional validation of the proposed SPGG was conducted through a collapse test of the reinforced soil slope model. The results indicated that the SPGG-obtained strains were similar to actual strain gauge measurements but provided a larger measurement range and that the SPGG was able to sense real-time vibrations during the slope collapse using a voltage analysis, confirming that the proposed SPGG can simultaneously provide soil reinforcement, strain monitoring of reinforcement materials, and vibration sensing. This research is expected to inform the development of a dynamic and static monitoring, large range, and accurate method for monitoring the conditions of reinforced soil over their entire lifecycles.

DEVELOPMENT OF RS-WZ3 SENSOR IN IOT BASED VIBRATION MONITORING SYSTEM

This article develops a vibration monitoring system on the machine using the RS-WZ3 sensor, which is based on MEMS (Micro-Electro-Mechanical Systems) technology. The RS-WZ3 sensor can measure vibrations on three axes (X, Y, and Z), as well as the surface temperature of the motor. The MAX485 module and the ESP32 microcontroller transform the vibration data from the sensor into a digital signal, subsequently displaying it on a web interface accessible remotely in real time. Sensor calibration is carried out by comparing the measurement results of the RS-WZ3 sensor with those of the GM63A vibration meter, which shows good accuracy with an average error of 2.01%. Testing of this system shows that the RS-WZ3 sensor is effective in measuring and monitoring machine vibrations in real time, enabling more efficient predictive maintenance and reducing maintenance costs.

Evaluation of hand-arm vibration (HAV)exposure among groundskeepers in the southeastern United States.

The objectives of this study were to evaluate daily hand-arm vibration (HAV)exposure among groundskeepers, characterize power tools used, and estimate lifetime cumulative HAV exposure dose. Seventeen groundskeepers and ten office workers employed at two US southeasterrn institutions were recruited as a target exposure group and a reference group, respectively. A 6-dexposure assessment of HAV was scheduled, and vibration dosimeters were used to obtain daily vibration exposure value, A(8). Information on power tools used and corresponding operation duration was recorded to assign the real-time vibration data collected from the dosimeters for tool characterization in terms of vibration total value (ahv) and frequency. Lifetime cumulative exposure dose, ahv-lifetime, was determined using ahv for all tools used and lifetime exposure duration obtained through a questionnaire. The individual groundskeepers' average A(8) ranged from0.8 to 2.6 and from1.0 to 2.6 m/s2 for the right hand and left hands, respectively. Among 11 power tools used by the groundskeepers, grass trimmers contributed the most to the vibration exposure. The average ahv of the individual tools ranged from 8.0 (chainsaws) to 1.9 m/s2 (seating mowers and handheld blowers) for the right hand and from 6.4 (push mowers) to 1.4 m/s2 (backpack blowers) for the left hand. The highest acceleration peak of grass trimmers, edgers, backpack blowers, pole saws, riding blowers, and hedgers was observed between 100 and 200 Hz while riding mowers, seating mowers, push mowers, and chainsaws showed the highest acceleration peak at lower frequencies (≤63.5 Hz). The groundskeepers' average ahv-lifetime was 76,520.6 and 61,955.5 h m/s2 for the right and left hands, respectively. The average ahv-lifetime of office workers was 2,306.2 and 2,205.8 h m/s2 for the right and left hands, respectively, which was attributed to personal hobby activities. Three groundskeepers' average A(8) reached 2.5 m/s2, the Action Limit recommended by the American Conference of Governmental Industrial Hygienists (ACGIH). The highest contribution to the vibration exposure was observed during grass trimmer operations with a major acceleration peak at 100 Hz. The groundskeepers' ahv-lifetime was 33 and 28 times higher for the right and left hands, respectively, than the office workers.

Real-Time Vibration Feedback from a Smartphone Application Reduces Sedentary Time but Does Not Increase Physical Activity Among Medical Students

Background: Sedentary behavior is associated with various adverse health outcomes. Medical students often experience high academic demands, leading to increased sedentary time. This study aimed to evaluate the effectiveness of a mobile app providing real-time feedback in reducing total sedentary time and prolonged sedentary bouts and in promoting physical activity among medical students. Methods: Seventy-seven medical students from Jazan University (mean age: 21.4 years; range: 20–25 years) participated in this study. Participants were assigned to either the control group (n = 40) or the intervention group (n = 37). The intervention group received real-time vibration feedback via a mobile app, prompting movement every 30 min of sedentary time, while the control group received no intervention. Sedentary behavior and physical activity levels were assessed using the Activities Completed Over Time in 24 h. Paired t-tests were conducted to examine within-group changes, and a two-way ANOVA was used to assess the interaction effect of time and group on sedentary time and physical activity. Results: After six weeks, the intervention group showed significant within-group reductions in their total sedentary time of 1.82 h (p = 0.01) and prolonged sedentary bouts of 1.91 h (p = 0.001), while the control group had no significant changes. Physical activity levels did not significantly change within either group. The two-way ANOVA revealed that there was no significant change over time between the two groups in their total sedentary time F (1, 75) = 1.590, p = 0.21, prolonged sedentary bouts F (1, 75) = 3.499, p = 0.06, or physical activity F (1, 75) = 0.565, p = 0.45. Conclusions: Real-time feedback from mobile apps resulted in significant within-group reductions in total and prolonged sedentary time among medical students in the intervention group. Low-cost mobile apps providing real-time feedback may be an effective intervention for reducing sedentary behavior among medical students, potentially improving their health and well-being.

A flexural-beam-type wide-frequency piezoelectric energy harvester for vertical shaft lifting system vibration monitoring

Abstract The coal mine lifting system may experience serious safety accidents due to severe problems with the bucket guides and rolling guide shoes. A piezoelectric energy harvesting (PEH) device for vibration sensor monitoring of shaft lifting system is proposed for the first time to monitor health of shaft lifting system. However, there are differences in the vibration frequencies, the working conditions are complex, leading to issues such as low energy recovery efficiency of the PEH and difficulty in achieving self-powered. To enhance PEH adaptability and reliability, a specifically designed flexural-beam-type wide-frequency piezoelectric energy harvester(FBT-WF-PEH) and a method of achieving real-time vibration monitoring through auxiliary power supply have been proposed. The results indicate when the excited frequency is 17Hz, the highest external output voltage is 11.2V, and under an external load of 17.5kΩ, the maximum output power is 7.168mW, demonstrating a good performance in terms of output power, and energy harvest bandwidth. The captive power supply test verified the PEH can utilize the vibration environment to achieve auxiliary power supply for monitoring systems under working conditions, which is of great significance for conducting research on health monitoring systems for lifting equipment. On the other hand, the new structure proposed in this study matches the operating frequency in the shaft lifting system, and the energy harvest efficiency is higher.

Intelligent vibration control of tensile cable based on deep reinforcement learning

Vibration of tensile cables commonly occurs in the engineering structures such as cable-stayed bridges, which may have negative effect on the driving comfort and safety, and further lead to the fatigue problems of the structure. This paper proposes a semi-active control strategy based on deep reinforcement learning and magneto-rheological (MR) damper, for the vibration control of tensile cables, and the corresponding algorithm have been developed. Through the interaction with the cable-damper system, the intelligent agent can evaluate every possible control parameter and achieve an effective control policy. Then, according to the real-time vibration state, the agent would determine an action current for the MR damper and thus change the damping coefficient of the damper, further make influences on the vibrating cable. Basically, the proposed strategy realizes the model-free semi-active control of cable vibrations. To validate the effectiveness of the proposed semi-active control strategy, a scale model test has been conducted, where the test cases of passive and semi-active control strategies are carried out and compared. Results show that, the semi-active control shows a prior performance in vibration reduction compared to the passive control strategy, with regard to the vibration profile, the vibration energy, as well as the energy dissipation of MR damper.

Composite metastructure with tunable ultra-wide low-frequency three-dimensional band gaps for vibration and noise control

Low-frequency vibration and noise control present enduring engineering challenges that garner extensive research attention. Despite numerous active and passive control solutions, achieving multiple ultra-wide attenuation regions remains elusive. Addressing vibration and noise control across a multidirectional broad low-frequency spectrum, three-dimensional metastructures have emerged as innovative solutions. This study introduces a novel three-dimensional composite metastructure featuring multiple ultra-wide three-dimensional complete band gaps. The research emphasizes the design strategy of elastic ligaments to achieve multiple ultra-wide attenuation regions spanning from 0.7 to 40 kHz. The band structures are elucidated through modal analysis and further substantiated by an analytical model based on a spring-mass chain with an additional resonator. The underlying physical mechanism for the formation of multiple ultra-wide band gaps is revealed through novel vibration modes from finite element analyses. Furthermore, we demonstrate that the distribution and the relative width of the ultra-wide band gaps can be tuned by modifying the geometric parameters of the metastructure. Utilizing additive manufacturing, prototypes are fabricated, and low-amplitude vibration tests are conducted to evaluate real-time vibration attenuation properties. Consistency is observed among theoretical, numerical, and experimental results. The proposed structure shows significant potential for high-performance meta-devices aimed at controlling noise and vibration across an extremely wide low-frequency spectrum.

Integrating machine learning techniques for predicting ground vibration in pile driving activities

This study focuses on the assessment of ground vibrations due to pile driving activities. Given the likelihood of excessive vibration due to the driving process, it is imperative to predict vibration levels during the design phase. The primary goal of this work is to integrate machine learning techniques, specifically Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANNs) for real-time vibration prediction. The training dataset was generated using a validated numerical model and the trained models were validated based on experimental results. This validation process highlights the efficiency and accuracy of Extreme Gradient Boosting in predicting the-free-field response of the ground.

Self-powered system for real-time wireless monitoring and early warning of UAV motor vibration based on triboelectric nanogenerator

Unmanned aerial vehicle (UAV) is widely used in various industries due to their high flexibility and large maneuverable space. Abnormal vibration of UAV motors can prevent it from working properly due to the abnormal state of the motors and the harsh external environment. There is no suitable method to monitor the UAV motors vibration in real time. At the same time, commercial sensors require an additional power supply to power the sensor, which indirectly limited their application in UAVs. A vibration sensor (AV-TENG) with magnetic spring structure was proposed in this paper for monitoring the vibration of UAV motors. The experimental results show that the magnetic spring has excellent stability. AV-TENG can achieve frequency detection in a wide frequency band. It also has excellent linearity with a maximum error of only 0.0062 %. Simultaneously, to verify the performance of AV-TENG in various environments and simulate its loading, the results demonstrate that AV-TENG can adapt well to the working conditions of UAV. Finally, this paper constructs a low-cost (less than $7), easy-to-manufacture signal acquisition system, and conducts a field demonstration, realizing the monitoring of the vibration frequency and acceleration of the UAV motors, and realizing the early warning, which is expected to promote the development of the field of UAV as well as the practical application of the triboelectric nanogenerator.

Cooperation mechanism between the vacuum levels and interface dipole energy of triboelectric materials for real-time vibration recognition

Triboelectric Nanogenerator (TENG) device, acting as sensors of energy harvesting and material identification, are attracting intensive attention. Nevertheless, vibration wave recognition cannot be achieved by the developed devices owing to device structure and working mechanism limitations. Here, a cooperation mechanism between the vacuum level and interface dipole energy of triboelectric materials is proposed to explore triboelectric charge generation and migration. Hence, metallized cottons of high specific surface area encapsulated by polydimethylsiloxane (PDMS) of low elastic modulus are developed as TENG-based sensors to realize vibration recognition. The results indicate electrons flow from PDMS to metallized cotton with the generation of an extremely thin interfacial dipole barrier layer because the vacuum level of metallized cotton is higher than that of PDMS, resulting in the accumulation of triboelectric charges on the PDMS surface under vibration, further leading to generating distinct triboelectric signals that can be simply differentiated using signal amplitude or peak shape with 98.3 % recognition accuracy for vibration waveform. Besides, combined with the deep machine learning and triboelectric effect, a vibration perception system integrated with a TENG-based sensor, data processing and display modules is also developed for real-time vibration monitoring. This work contributes to further clarifying the theoretical mechanisms of triboelectric charge excitation under vibration.

Turbine bearing fault diagnosis based on embedded system

Abstract Failure of the main components of the transmission mechanism of large machinery and equipment usually uses sensors to collect real-time vibration or audio information, followed by data processing on the computer to analyze whether the equipment is operating normally. In this paper, a multi-core embedded fault diagnosis platform based on ARM and DSP is designed. It is small in size. It can independently set the signal types, ranges, and filter parameters of multiple acquisition channels, and synchronously set high-precision acquisition and storage of voltage signals output from various sensors. The DSP adopts the fast Fourier algorithm to analyze the multi-channel sampling data in real time and extracts the fault characteristics through the analysis of envelope demodulation. The operation results show that the system can meet the routine online fault diagnosis of mechanical equipment transmission mechanism, and the fault analysis and diagnosis can reach 95% accuracy within one minute, with high real-time accuracy.

Automated classification of drill string vibrations using machine learning algorithms

Downhole vibration can cause drill string failures and significant economic losses. Recognizing harmful vibrations in real time is highly significant for optimizing drilling operations. This work aims to identify whirls and stick-slip using machine learning (ML) from downhole measured accelerations. A rotor dynamics model is utilized to simulate whirl and a coupling method between whirl and stick-slip is presented. 448 sets of simulated acceleration data are used for the training of ML classification models. 14 features for whirl identification and 8 for stick-slip are defined in each acceleration. The generalization abilities of the trained whirl and stick-slip classifiers are validated using ABAQUS simulation data and field-measured data, respectively. Decision Tree, Naive Bayes, Support Vector Machine (SVM), and Neural Network algorithms are tested. The classification accuracy is highest when whirl is divided into synchronous forward whirl, synchronous backward whirl, and discontinuous contact whirl. Both SVM and Neural Network algorithms can achieve nearly 100% accuracy in rotor dynamics data and ABAQUS data. The highest accuracy is attained by utilizing both tangential and radial acceleration. However, using only tangential acceleration can still fulfill engineering requirements. The stick-slip classifier, when using SVM and Neural Network, can achieve 100% recognition accuracy on all test data. The proposed methods can lessen sensor need for downhole measurements and speed up data processing, and may be applied to other problems involving rotating soft shafts.

Edge Machine Learning-Enabled Predictive Fault Detection System for Conveyor Belt Maintenance Optimization in Industrial Settings

Digital transformation is essential for industrial and manufacturing sectors, especially in conveyor belt systems. On the other hand, Innovative electronic components and software applications offer opportunities for advanced fault prediction, minimizing operational disruptions, reducing inefficiencies, and enhancing overall industrial efficiency. Efficient conveyor belt systems are crucial for industrial operations, requiring early fault detection to prevent costly downtime and ensure safety. The utilization of embedded machine learning via the Edge Impulse platform enhances data collection and spectral feature extraction using wavelet approximation. This article presents a novel methodology for predictive fault detection in conveyor belt systems employing edge machine learning. Leveraging the ESP32 microcontroller and accelerometer sensor for real-time vibration data capture, a four-layer Artificial Neural Network (ANN) model, trained on 336 feature samples, is deployed via Edge Impulse and TensorFlow scripts. TensorFlow Lite compresses the model for microcontroller integration. The ESP32 microcontroller, acting as an edge device, achieves real-time predictions with 97.5% accuracy and 0.20 minimal loss. This work promises significant strides in predictive maintenance, offering cost savings, operational reliability, and efficiency gains in industrial settings, revolutionizing maintenance strategies.

AiroTouch: enhancing telerobotic assembly through naturalistic haptic feedback of tool vibrations.

Teleoperation allows workers to safely control powerful construction machines; however, its primary reliance on visual feedback limits the operator's efficiency in situations with stiff contact or poor visibility, hindering its use for assembly of pre-fabricated building components. Reliable, economical, and easy-to-implement haptic feedback could fill this perception gap and facilitate the broader use of robots in construction and other application areas. Thus, we adapted widely available commercial audio equipment to create AiroTouch, a naturalistic haptic feedback system that measures the vibration experienced by each robot tool and enables the operator to feel a scaled version of this vibration in real time. Accurate haptic transmission was achieved by optimizing the positions of the system's off-the-shelf accelerometers and voice-coil actuators. A study was conducted to evaluate how adding this naturalistic type of vibrotactile feedback affects the operator during telerobotic assembly. Thirty participants used a bimanual dexterous teleoperation system (Intuitive da Vinci Si) to build a small rigid structure under three randomly ordered haptic feedback conditions: no vibrations, one-axis vibrations, and summed three-axis vibrations. The results show that users took advantage of both tested versions of the naturalistic haptic feedback after gaining some experience with the task, causing significantly lower vibrations and forces in the second trial. Subjective responses indicate that haptic feedback increased the realism of the interaction and reduced the perceived task duration, task difficulty, and fatigue. As hypothesized, higher haptic feedback gains were chosen by users with larger hands and for the smaller sensed vibrations in the one-axis condition. These results elucidate important details for effective implementation of naturalistic vibrotactile feedback and demonstrate that our accessible audio-based approach could enhance user performance and experience during telerobotic assembly in construction and other application domains.

Cat paw-inspired magnetorheological elastomer embedded mechanical metamaterials with active sensing and switching stiffness for vibration isolator

Real-time vibration monitoring, assessment, and isolation play critical roles in the safety and maintenance of engineering industries and traffic facilities. Recently, magnetorheological elastomers (MRE) with tunable stiffness have been considered promising solutions for vibration isolation. However, for some heavy-load conditions, such as motors or machine tools, MRE vibration isolators are insufficient for realizing vibration sensing and high bearing capacity. Therefore, it is critical to design a novel MRE isolator system that enables real-time vibration monitoring, isolation, and bearing capacity. Herein, we propose a cat's paw-inspired oct-MRE vibration isolator coupled with a soft MRE for energy buffering embedded in octet metamaterials to support the load. For vibration sensing, a sliding triboelectric nanogenerator (TENG) was constructed using polytetrafluoroethylene (PTFE) and a copper foil around the oct-MRE inside a vibration isolator to monitor and evaluate the vibration state in real-time. Experimental tests show that our all-in-one vibration isolator can monitor motor vibration accurately and actively modulate it under resonant conditions, the vibration acceleration can be reduced from 17.0 m/s2 to 12.9 m/s2. This study provides an ideal solution for intelligent health monitoring of motors and other autonomous, smart, and long-term engineering tasks.

Metamaterial-based passive analog processor for wireless vibration sensing

Real-time, low-cost, and wireless mechanical vibration monitoring is necessary for industrial applications to track the operation status of equipment, environmental applications to proactively predict natural disasters, as well as day-to-day applications such as vital sign monitoring. Despite this urgent need, existing solutions, such as laser vibrometers, commercial Wi-Fi devices, and cameras, lack wide practical deployment due to their limited sensitivity and functionality. Here we proposed a fully passive, metamaterial-based vibration processing device, fabricated prototypes working at different frequencies ranging from 5 Hz to 285 Hz, and verified that the device can improve the sensitivity of wireless vibration measurement methods by more than ten times when attached to vibrating surfaces. Additionally, the device realizes an analog real-time vibration filtering/labeling effect, and the device also provides a platform for surface editing, which adds more functionalities to the current non-contact sensing systems. Finally, the working frequency of the device is widely adjustable over orders of magnitudes, broadening its applicability to different applications, such as structural health diagnosis, disaster warning, and vital signal monitoring.

Vibration IOT Control Algorithm for Steel Structures of Concrete Buildings with Elastic Boundary Constraints

Abstract Because of the influence of steel structure’s elastic properties in concrete buildings, the traditional vibration Internet of Things (IOT) control algorithm of steel structures has the problem of low control efficiency. Based on the research subject of concrete buildings, a corresponding steel structure model is established, and the corresponding vibration control standard is determined according to the characteristics of the model. In this model, the boundary elastic constraints of the steel structure are set according to its structural characteristics, and the vibration characteristics of the steel structure are analyzed under the constraints. The external vibration signal is simulated, and the response of the building’s steel structure under different signal excitations is observed. According to the response characteristics of the steel structure, the vibration controller is designed and installed, the real-time vibration signal is input into the controller, and finally the vibration control of steel structure of concrete building is realized. By comparison with the traditional vibration IOT control algorithm, it is concluded that the vibration control method designed by combining vibration frequency and vibration amplitude has smaller error and shorter time cost, that is, the designed vibration IOT control algorithm has more advantages in control efficiency.

Investigating power loss in a wind turbine using real-time vibration signature

Wind energy is one of the fastest-growing sources of renewable energy; hence, a comprehensive understanding of all the environmental and mechanical factors that influence the power output from wind turbines is essential. Despite vibration being a crucial degradation indicator (and thus linked to wind turbine health), its role in influencing power loss is rarely explored. In this paper, we propose to use a novel feature, the vibration index, derived from the vibration data, to understand the power loss in wind turbines. The methodology adopted in this work involves modelling the gearbox health as the loss of generator speed using a data-driven methodology with vibration index as input. It was observed that vibration had a significant impact on the generator speed. Vibration of different parts of the turbine hub was found to lead to a loss of rotational speed in the generator, which in turn led to power loss. Wind turbine vibration was found to explain about 57% (R2: 0.57) of the loss in speed. Further, the generator speed is linearly mapped to power with an accuracy of 85% (R2:0.85), and consecutively, the predicted loss in the generator speed is mapped to the power loss in the turbine. The accuracy of power loss prediction was about 55% (R2: 0.55). The proposed methodology is applied to data obtained from a real-world onshore wind farm located in the state of Gujarat (India). This approach offers a data-driven and efficient methodology that augments the understanding of wind turbine operations and the complex relationship between vibration and the power output of a wind turbine. The outcomes of this research can contribute to improving wind turbine performance and enhancing decision-making for system maintenance and operation.

Robust Grasping of a Variable Stiffness Soft Gripper in High-Speed Motion Based on Reinforcement Learning.

Industrial robots are widely deployed to perform pick-and-place tasks at high speeds to minimize manufacturing time and boost productivity. When dealing with delicate or fragile goods, soft robotic grippers are better end effectors than rigid grippers due to their softness and safe interaction. However, high-speed motion causes the soft robotic gripper to vibrate, leading to damage of the objects or failed grasping. Soft grippers with variable stiffness are considered to be effective in suppressing vibrations by adding damping devices, but it is quite challenging to compromise between stiffness and compliance. In this article, a controller based on deep reinforcement learning is proposed to control the stiffness of the soft robotic gripper, which can accurately suppress the vibration with only a minor influence on its compliance and softness. The proposed controller is a real-time vibration control strategy, which estimates the output of the controller based on the current operating environment. To demonstrate the effectiveness of the proposed controller, experiments were done with a UR5 robotic arm. For different situations, experimental results show that the proposed controller responds quickly and reduces the amplitude of the oscillation substantially.

Bridge progressive damage detection using unsupervised learning and self-attention mechanism

This paper presents a novel unsupervised method for detecting progressive damage in bridge structures. Traditional supervised learning approaches require a large number of damaged samples to train a high-performing detection model, which poses challenges when applied to normal in-service bridges. Since in-service bridges usually lack data from damage scenarios and existing methods fail to achieve real-time detection, unsupervised learning has gained popularity due to its ability to work without damaged samples and offer better real-time performance. In light of this, this paper proposes a novel unsupervised damage detection method that utilizes an unsupervised attention-convolutional auto-encoder to reconstruct real-time vibration signals from bridge. Two damage indicators are employed to evaluate the effectiveness of the reconstruction process, aiming to capture potential changes in the progressive damage of bridges. The results demonstrate that: the attention-convolutional auto-encoder achieves excellent reconstruction performance for vibration signals from intact structures, which reconstruction error converging to 0 and structural similarity approaching 1. However, the reconstruction performance is less satisfactory for vibration signals in damage scenarios, deteriorating further as the damage level increases. By analyzing the variation patterns of vibration signals in attention-convolutional auto-encoders, it was confirmed that the reliability of the proposed evaluation effects by the two different types of damage indicators. Finally, the trend of progressive damage was extracted through these indicators, showcasing the effectiveness of the method in detecting progressive damage. This approach facilitates the identification of potential progressive changes in the normal operation process of bridges, thus enhancing real-time damage detection capabilities and contributing to the knowledge of bridge monitoring.