Vibration Sensor Research Articles

Establishment of a measuring unit based on Arduino board for recording vibrations on an agricultural tractor during tillage process

When it comes to agricultural tractor ergonomics, vibration is one of the most important factors to consider. However, little research has been done to evaluate this parameter and its impact on workers’ health. Therefore, the aim of this study was to fabricate a portable vibration measurement unit (PVMU) based on an Arduino microcontroller and a vibration sensor (accelerometer ADXL335). The PVMU performance was tested in combination with a commercially available three-axis USB vibration measurement device (VB10). Furthermore, the vibration measurements were used to calculate the tractor driver’s whole-body vibration load according to ISO 2631-1 along three perpendicular axes (X, Y, and Z) during soil tillage using a disk harrow under different tillage speeds of 4.0, 5.5, and 7.0 km/h at a tillage depth of 15 cm in a sandy loam soil. Performance tests of the PVMU and the commercially available three-axis USB vibration meter (VB10) revealed a highly significant level of agreement based on a correlation coefficient of 0.9822. It was found that the vibration level increases with increasing tillage speed. The highest vibration level, 0.80 m/sec2, was observed in the vertical direction (Z-axis) at a tillage speed of 7.0 km/h. The lowest vibration level, 0.12 m/sec2, was observed in the lateral direction (Y-axis) at a tillage speed of 4.0 km/h. The dominant frequency range of vibration transmitted to the tractor seat was found to be between 2.04 Hz and 20.14 Hz. The daily exposure values that is, A (8) determined during this study to assess the health hazards due to vibration for tractor drivers ranged from 0.01 m/sec2 to 0.06 m/sec2 lower than the recommended exposure action value (EAV) limit that is, 0.5 m/sec2. This means that there is no health risk for the driver during the soil tillage using a disk harrow plow. The PVMU proposed in this study is very reliable and can be used as a vibration assessment tool for other agricultural tractor-implement combinations.

Performance of vibration and current signals in the fault diagnosis of induction motors using deep learning and machine learning techniques

Induction motors (IMs) play a pivotal role in various industrial applications, powering critical systems such as pumps, compressors, fans, blowers, and refrigeration and air conditioning systems. Monitoring the health of these IMs is essential for ensuring reliable operation. Numerous sensors, including vibration, current, temperature, acoustic, and power sensors, can be employed for their health monitoring. This article conducts a comprehensive comparative analysis of two widely used sensors—vibration and current, for classifying different health states of IMs, such as a healthy condition, bearing fault, and misalignment. The study employed deep learning techniques, specifically 1D and 2D convolutional neural networks, trained on raw data. Additionally, machine learning techniques, including random forest and XGBoost, were utilized and trained on features derived from preprocessed signals using fast Fourier transform and discrete wavelet decomposition. Comparative results indicated that vibration signals achieved remarkably high accuracy, nearly 100%, in detecting the investigated mechanical faults, while current signals, after signal processing and manual feature extraction, achieved an accuracy of 87.41%. These results demonstrate that, though current sensors are a viable alternative to vibration sensors, their performance can be affected by the type and degree of the considered faults. This study also highlights the attributes of vibration and current signals in the health monitoring of rotating machinery such as IMs.

An optical vibration sensor enabled by coupling mechanoluminescence with photostimulable phosphor

An optical vibration sensor enabled by coupling mechanoluminescence with photostimulable phosphor

Fiber optics vibration sensor with selftest functionality

Fiber optics vibration sensor with selftest functionality

Low-Cost IoT-Based Sensors Dashboard for Monitoring the State of Health of Mobile Harbor Cranes: Hardware and Soft-ware Description

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality Control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server. The novelty of the QC-DAS design is that each function is encapsulated in a predefined module that is "immersed" in a message transmission medium. Modules interact by sending and receiving various signals through this medium. Modularity makes system design simpler, faster, and flexible. Thanks to modularity, users may incorporate their data processing modules when new sensors are added to match the system's needs. Thanks to modularity the DC-DAS can operate quality control hardware for any mobile cranes. There are several constraints in the quality control data acquisition system used by the Damietta Port Authority in Damietta, Egypt, SESCO TRANS company which cause the loading and unloading process to be slowed down. As a result, the SESCO TRANS company upgraded its quality control data acquisition system using the QC-DAS. The hardware was deployed for six months, during which the collected data was used to verify the crane's performance. The vibration produced by the slewing of the crane was monitored and compared with the bearing fault frequency limits, during the operation the wind speed was monitored and compared with the critical wind speed to stop the crane operation automatically, and the payloads data of the six months was collected and was used to calculate the working efficiency of the load and unload process of the crane. The results demonstrated that while maintenance costs were decreased, the crane load/unload procedure was improved. The SESCO TRANS company crane operators approved the developed approach and appreciated the achieved results.

Comparison of Surface Bonded Piezoelectric Transducers and Concrete Vibrational Sensors in Damage Detection of Reinforced Concrete Beams

Comparison of Surface Bonded Piezoelectric Transducers and Concrete Vibrational Sensors in Damage Detection of Reinforced Concrete Beams

A non-contact triboelectric vibration sensor with a spiral floating electrode structure for low-frequency vibration monitoring

A non-contact triboelectric vibration sensor with a spiral floating electrode structure for low-frequency vibration monitoring

Bearing fault diagnosis based on wavelet adaptive threshold filtering and multi-channel fusion cross-attention neural network

In industrial applications, it is difficult to extract the fault feature directly when the rolling bearing works under strong background noise. In addition, single-channel vibration sensor data pose limitations in providing a comprehensive representation of bearing fault features; how to effectively fuse data of each channel and extract features is a challenge. To solve the above-mentioned problems, a fault diagnosis method based on wavelet adaptive threshold filtering and multi-channel fusion cross-attention neural network is proposed in this paper. First, the multi-scale discrete wavelet transform is applied to obtain the wavelet coefficients of each channel. Adaptive threshold filtering is conducted to filter out noise and extract symbolic features. The threshold updates with the training of the network. Then, the wavelet coefficients are reconstructed and the channel attention is performed to further extract the symbolic features of the fault signal. Finally, the multi-channel fault signals are fused by a cross-attention module. This module can fully extract the features of each channel and fuse multi-channel data. To improve the generalization ability of the network, residual connections are added. To verify the effectiveness of the proposed method, experiments are carried out on the rolling bearing datasets of Case Western Reserve University and Xi’an Jiaotong University. In addition, the gas turbine main bearing dataset is also applied to prove the reliability of this method.

Detecting damage on engine mounts using hilbert-huang transform vibration analysis

Engine mounts, which are usually composed of elastomeric materials like rubber that can absorb excessive vibrations, are built to withstand vibration sources from engines. Engine mounts may eventually degrade from extended use. When rubber products age or sustain damage, they may grow harder and crack. Engine mounts need to be maintained regularly to ensure optimal performance. Visual examinations and the detection of excessive vibrations can be used to accomplish this. Vibration sensors are mounted on the engine mount in axial, vertical, and horizontal orientations using a vibration detection technique utilizing the Hilbert-Huang Transform (HHT) methodology. Matlab is used to examine the data that is gathered at three distinct rotational speeds: 750 rpm, 2000 rpm, and 3000 rpm. The HHT approach combines two key components: the Hilbert Transform, which analyzes the time-frequency signal of the first decomposition until only residuals remain, and Empirical Mode Decomposition (EMD), which breaks down the signal into Intrinsic Mode Functions (IMFs). According to test data, a damaged mount had an amplitude of 0.00005212 m/s² and a frequency of 8 Hz. On the other hand, in typical circumstances, the highest frequency was 7 Hz with the same amplitude. Five frequency increases were made in the damaged mount throughout this operation. In the damaged mount, the Hilbert Transform showed a frequency of 2124 Hz with an amplitude of 0.007594 m/s², indicating a significant resonance. This illustrates how the Hilbert-Huang Transform's capacity to handle non-stationary and nonlinear signal forms allows it to detect damage in components efficiently

Phase-Shifted Fiber Bragg Grating by Selective Pitch Slicing

This paper presents a new type of phase-shifted Fiber Bragg Grating (FBG): the sliced-FBG (SFBG). The fabrication process involves cutting a standard FBG inside its grating region. As a result, the last grating pitch is shorter than the others. The optical output signal consists of the overlap between the FBG reflection and the reflection at the fiber-cleaved tip. This new fiber optic device has been studied as a vibration sensor, allowing for the characterization of this sensor in the frequency range of 150 Hz to 70 kHz. How the phase shift in the FBG can be controlled by changing the length of the last pitch is also shown. This device can be used as a filter and a sensing element. As a sensing element, we will demonstrate its application as a vibration sensor that can be utilized in various applications, particularly in monitoring mechanical structures.

Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties

In the field of structural damage detection through vibration measurements, most existing methods demand extensive data collection, including vibration readings at multiple levels, strain data, temperature measurements, and numerous vibration modes. These requirements result in high costs and complex instrumentation processes. Additionally, many approaches fail to account for model uncertainties, leading to significant discrepancies between the actual structure and its numerical reference model, thus compromising the accuracy of damage identification. This study introduces an innovative computational method aimed at minimizing data requirements, reducing instrumentation costs, and functioning with fewer vibration modes. By utilizing information from a single vibration sensor and at least three vibration modes, the method avoids the need for higher-mode excitation, which typically demands specialized equipment. The approach also incorporates model uncertainties related to geometry and mass distribution, improving the accuracy of damage detection. The computational method was validated on a steel frame structure under various damage conditions, categorized as single or multiple damage. The results indicate up to 100% accuracy in locating damage and up to 80% accuracy in estimating its severity. These findings demonstrate the method’s potential for detecting structural damage with limited data and at a significantly lower cost compared to conventional techniques.

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.

Research on Highly Reliable Self-Powered Vibration Sensors for Geological Drilling

Vibration signals at the bottom of the drill string during geological drilling are crucial for lithological identification and drilling parameter optimization. However, existing downhole vibration sensors suffer from limitations in power supply and reliability. This study proposes a self-powered vibration sensor with high redundancy based on the triboelectric nanogenerator principle, which is capable of measuring both axial and transverse vibrations, thereby reducing the dependence on external power sources. The experimental results show that the sensor can measure axial vibration frequencies ranging from 0 to 11 Hz with an error of less than 4% and transverse vibration frequencies ranging from 0 to 5 Hz with an error of less than 5%. It can operate stably in temperatures from 0 to 180 °C and relative humidities from 0 to 95%. The sensor’s axial vibration measurement features six identical measurement structures, providing high redundancy and effectively enhancing its reliability. Furthermore, the sensor exhibits power generation capabilities. When an external load of 1 MΩ is applied to the axial measurement module and 10 MΩ to the transverse measurement module, the sensor achieves its maximum power output for both axial and transverse measurements, reaching 32.4 × 10−9 W and 2.1 × 10−9 W, respectively. Compared to traditional bottom-of-the-hole vibration sensors, this sensor possesses self-powering capabilities and high reliability, which can improve the operational efficiency and hold significant practical value for future applications.

Basic Analysis for Evaluation of Tennis Volley Skill Using Body Propagated Vibration Sensing

In sports that use tools, it is necessary to master tool manipulation with a high degree of accuracy because proficiency in tool manipulation leads to victory or defeat in a competition. The purpose of this study was to clarify the force transfer of the body, including the racket, from the perspective of vibration analysis to realize training to efficiently improve tennis skills. In this study, we measured the propagating vibration, surface electromyography of finger flexors and extensors, and racket movement under two conditions of strong- and weak-volleying in tennis and aimed to explain the meaning of the measured propagating vibration in terms of muscle control and racket kinematics. Based on the experimental results, it was confirmed that it is feasible to measure the change in stiffness from the racket to the wrist due to the muscle co-contraction owing to the propagating vibration signal intensity. These results indicate the possibility of evaluating tennis shot skill by analyzing the propagating vibrations measured using a wearable vibration measurement system.

Development of fiber Bragg grating vibration sensor for bidirectional monitoring of thin rod cantilever beam

Monitoring of vibration frequencies and forces in cables and suspension rods of bridges is crucial. In order to achieve high-precision and bidirectional low-frequency monitoring of cable force and suspension rod, in this work, a thin rod cantilever beam type FBG vibration sensor was developed for bidirectional monitoring based on theory, experiment, and finite element simulation. Firstly, elastic structure of the thin rod was analyzed, and the structural parameters of the sensor were optimized based on theoretical analysis and finite element simulation. Then, the frequency and sensitivity of the sensor were tested. The obtained test results showed that the resonant frequency of the sensor in the x/y direction was 48 Hz, sensitivity was 90.5/102.3 pm/g, and error in frequency was less than 2 %. Furthermore, the force in the cable measured by the sensor was verified through indoor tension tests. The test results showed that the maximum error in the force measured in the cable was 0.86 % relative to the actual tension in the cable, indicating high measuring accuracy of the developed sensor. Finally, the sensor was applied to monitor forces in actual engineering cables, where an error of 4.21 % was noticed, which further validated the accuracy and applicability of the sensor. The research results of the present would provide guidance for improving the ability of the bidirectional low-frequency FBG vibration sensors to measure low-frequency random vibrations.

Examination of Proprioceptive Reliance During Backward Walking in Individuals With Multiple Sclerosis

Background: Slowed somatosensory conduction in multiple sclerosis (MS) increases postural instability and decreases proprioception. Despite these delays, individuals with MS rely more on proprioception for balance compared to controls. This heightened reliance, combined with slowed signal transduction, increases fall risk. Backward walking (BW) increases proprioceptive reliance by reducing visual cues. However, no study has conclusively linked proprioception to BW. This study aims to assess proprioception’s role in BW compared to forward walking (FW) in MS and to compare differences in proprioception between MS fallers and non-fallers. Methods: Fifty participants (average age: 50.34 ± 11.84, median Patient Determined Disease Steps [PDDS]: 2) completed the study. Participants completed BW and FW at comfortable and fast speeds. We have previously established vibration sensation as a proxy measure for proprioception. Vibration thresholds were quantified at the great toe bilaterally using a 2-alternative forced-choice procedure. Results: Significant correlations were seen for vibration sensation and FW comfortable (ρ = 0.35), FW fast (ρ = 0.34), BW comfortable (ρ = 0.46), and BW fast (ρ = 0.46). After controlling for age, sex, and PDDS, vibration sensation significantly predicted performance during all walking tasks, with larger beta coefficients seen during BW (comfortable β = 0.57; fast β = 0.58) compared to FW (comfortable β = 0.41; fast β = 0.45). Fallers performed significantly worse than non-fallers for vibration sensation (P = 0.04). Discussion and Conclusions: Considering the notable decrease in proprioception in participants with MS and the clear distinction between fallers and non-fallers, it is crucial to conduct fall risk assessments and interventions focusing on proprioception. With its heightened reliance on proprioception, BW offers a promising method for assessing fall risk and could be an effective exercise intervention. Video Abstract available for more insights from the authors (see the Supplemental Digital Content available at: http://links.lww.com/JNPT/A490).

A factor analysis based automatic optimization method of vibration sensor points for ship acoustic reconstruction

To address the challenge associated with optimizing the number and positioning of accelerometer points in ship acoustic reconstruction, factor analysis is applied to the automatic optimization of accelerometer position in this work according to the source independence of ship’s underwater radiated noise. Based on the numerical simulation method that calculates the vibration noise response under specific operation conditions, the minimum number and the optimal position of the accelerometers can be obtained through the factor analysis and multi-parameter comprehensive evaluation method. The results of acoustic reconfiguration and testing are compared using one test cabin as a validation object to verify the effectiveness of the proposed method. The results show that the automatic optimization algorithm can improve the reconstruction accuracy concerning the uniform distribution method. The error is reduced from 15.46% (1.5 dB) to 8.35% (0.8 dB), and the correlation coefficient is improved from 0.86 to 0.89. The semi-automatic optimization method is slightly lower in terms of errors and correlation coefficients compared to the automatic optimization algorithm. After increasing the number of monitoring positions for automatic optimization, the acoustic reconstruction accuracy is further improved, while the average error is reduced to 7.37% (0.7 dB) and the correlation coefficient is improved from 0.89 to 0.92. The experimental results are proximate to the conclusions of the simulation calculation. Even under the circumstance of multi-factor noise interference, the correlation coefficient of multi-position automatic optimization can still reach 0.8, in comparison with the uniform distribution method, the error is reduced from 32.49% (3.4 dB) to 25.94% (2.6 dB). The proposed method effectively captures the dynamic characteristics of complex structures and their impact on underwater radiated noise, and the integration of factor analysis can prominently enhance the computational efficiency, thereby providing technical support for engineering applications to the ship acoustic reconstruction.

Current signal analysis using SW-GAT networks for fault diagnosis of electromechanical drive systems under extreme data imbalance

Abstract In complex industrial environments, ensuring the safe operation and effective maintenance of electromechanical equipment is of paramount importance. Intelligent fault diagnosis based on deep learning is currently the most popular data-driven method. However, conventional intelligent fault diagnosis techniques face several challenges: (1) Most diagnostic models rely heavily on analyzing vibration signals. However, vibration sensors are difficult to deploy in space-constrained environments, and vibration signals are frequently contaminated by strong noise. (2) The prevalence of class imbalance between normal and fault data in equipment condition monitoring can lead to model over-reliance on information from a few classes. (3) Traditional diagnostic models presuppose data independence, neglecting the coupling relationships between data. To address the aforementioned issue, this paper proposes a Self-Weighted Graph Attention Networks (SW-GAT) based on motor stator current signal analysis, aimed at solving the fault diagnosis problem of critical transmission components in electromechanical systems under severely imbalanced data scenarios. Firstly, the raw current data is preprocessed using Stacked Auto-Encoders (SAE), and then the decoded current frequency-domain data is utilized to construct graphical data, thereby enhancing the non-common features and weak fault information in the current signals. Secondly, by introducing the graph pooling attention mechanism into GAT, the model can more effectively focus on useful fault feature information within the graph data. Finally, a novel interclass adjustment loss function is designed to adaptively adjust and balance class weights, enabling the model to pay greater attention to minority class samples and thereby improving the recognition accuracy for minority class faults. Validating the proposed method on two cases and comparing it with other advanced approaches, our method achieved the highest accuracy among the compared methods.

Motor Fault Diagnosis and Detection with Convolutional Autoencoder (CAE) Based on Analysis of Electrical Energy Data

This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN through latent space representation. Traditional diagnostic methods relied on vibration and current sensors, empirical knowledge, or harmonic and threshold-based monitoring, but they had limitations in recognizing complex patterns and providing accurate diagnoses. Our model significantly enhances the accuracy of power data analysis and fault diagnosis by mapping each phase (R, S, and T) of the electrical system to the red, green, and blue (RGB) channels of image processing and applying various signal processing techniques. Optimized for real-time data streaming, this model demonstrated high practicality and effectiveness in an actual industrial environment, achieving 99.9% accuracy, 99.8% recall, and 99.9% precision. Specifically, it was able to more accurately diagnose motor efficiency and fault risks by utilizing power system analysis indicators such as phase voltage, total harmonic distortion (THD), and voltage unbalance. This integrated approach significantly enhances the real-time applicability of electric motor fault diagnosis and is expected to provide a crucial foundation for various industrial applications in the future.

Railways Health Monitoring Employing KSK Approach: A Novel AIIoT based Decision-Making Approach for Railways

Central to this AIIoT approach is the implementation of machine learning algorithms that can process vast amounts of data generated from the sensors. These algorithms can identify patterns and anomalies that might indicate wear and tear or incipient failures in critical systems. For example, vibration sensors on trains can detect irregularities in wheel dynamics, while track-side monitoring systems can check for track integrity. By integrating these insights into a centralized health monitoring platform, railway operators are not only able to understand the current health status of their assets but also make informed decisions about maintenance schedules and resource allocation. Moreover, the innovative use of edge computing in this AIIoT framework allows for localized data processing, reducing latency and enabling immediate responses to critical situations. This is crucial in a railway environment where timely interventions can prevent accidents and improve service reliability. Additionally, the combination of AIIoT with cloud computing creates opportunities for advanced data analytics and machine learning models that can continuously improve their accuracy over time as more data becomes available. In essence, this novel AIIoT approach not only enhances operational efficiency but also aligns with broader initiatives aimed at making rail transport more sustainable by reducing unnecessary maintenance trips and optimizing resource utilization. The system informs the decision based on Track condition, speed of train, train condition to the authority.