Vibration Sensor Research Articles

Underwater Vibration Sensor to Enable Automated and Contactless Voice Recognition

AbstractIndividuals suffering from voice disabilities have limited access to currently available automation technologies that operate through voice commands. To address this issue, an alternative voice recognition approach is essential without directly monitoring the audio signals generated from the vocal cord. In this work, the design of a chemically reactive and conductive sponge is reported to create an underwater vibration sensor with a fast response time and high sensitivity, through orthogonal modulation of conductivity (40–2150 kΩ), water repellence (0°–154°) and mechanical properties (0.32–2.63 MPa). This class of porous sponge sensors enables the identification of subtle water waves generated at the air–water interface and extends its utility to detecting a variety of locomotion (squatting, jumping, walking, etc.), as well as automated voice recognition using a deep learning model without direct contact with the human body. Overall, this underwater vibration sensor provides a novel basis for remote interaction with automated technologies, which finds use in medical diagnostics, human‐machine interfaces, and underwater communication systems.

An advanced machine vision-based method for abnormal detection of transverse vibrations in ship propulsion shafting

The working environment of ship propulsion shafting is harsh and the force condition is complex, which often produces all-directional vibration. Its working condition will directly affect the navigation performance of the ship. To overcome the limitations of complicated installation route, tedious maintenance process and high cost of traditional contact vibration sensors, an approach of transverse vibration identification model based on machine vision was proposed to realize multi-point vibration displacement sensing and anomaly analysis of shafting. The displacement information of the video signal is extracted by the displacement sensing strip labeling method, and the abnormal state of the ship propulsion shafting is analyzed by the dynamic kernel principal component analysis (DKPCA) algorithm. The experimental results show that the approach can accurately detect the continuous vibration displacement of shafting in the range of 180 r/min, and can work normally under two abnormal conditions: sudden external excitation and continuous uneven external excitation. In addition, this approach can quickly and accurately monitor the motion state of shafting, and realize the perception and recognition of abnormal vibration state of shafting. The research and application of this approach in ship shafting vibration monitoring is of great significance to the development of unmanned and intelligent ships.

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.

Optimization of embedded sensor placement for small satellites

Abstract Dynamic environment testing is important in the manufacturing phase of a satellite before launch. However, redundancy is introduced into the testing by the massive placement of vibration sensors inside and outside the satellite, adversely affecting testing efficiency, especially in the development of satellite constellations. An intelligent algorithm-based sensor optimization method is proposed to maximize the relationship between measure points and reduce the number of internal sensors while ensuring the independence of external sensors. A frequency response matrix of internal and external sensors is constructed through a frequency-sweeping analysis of the satellite structure. Subsequently, external sensors are categorized into different groups by using principal component analysis (PCA) and K-means clustering analysis. A modified information entropy approach is applied to optimize the quantity of internal sensors. Through the practical application of sensor reduction in a specific satellite, the proposed method achieves a good reduction result.

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

Recognition of Impact Load on Connecting-Shaft Rotor System Based on Motor Current Signal Analysis.

Impact loads affect the operational performance and safety life of rolling equipment's connecting-shaft rotor system, even causing faults and accidents. Therefore, recognizing and investigating impact loads is of great significance. Hence, a load recognition method based on motor current information is proposed in this paper to recognize impact loads on the connecting-shaft rotor system. First, the fast Fourier transform is used to obtain the frequency domain information for the motor's current response signal from the rotor system load recognition test. Consequently, the required load response information can be presented more clearly using the singular value decomposition method to remove the power frequency components in the current signal. Then, wavelet packet decomposition is performed on the signal to generate energy analysis feature vectors. A qualitative recognition of the impact load on the system is achieved by learning vector quantization neural networks; the resulting load recognition results are good. These findings indicate that using the motor current as the analysis signal can solve the problem of the difficult layout for traditional vibration sensors in rolling sites. The preprocessing and recognition method of the current response signal can recognize the impact load, confirming the applicability and feasibility of the proposed method.

A New Type of Dynamic Vibration Fiber Sensor.

A new-type vibration sensor based on a fiber Bragg grating combined with a special structure-packaged design is proposed for monitoring the mechanical vibration signals. Three different sensing structures, including the film squeeze type, new film squeeze type, and elastic tape squeeze type are proposed for measuring the vibration signals with the frequency range from tens to thousands of Hz. In the comparison to experimental results, the new film squeeze structure has a nice sensing performance in the range from 100 to 1000 Hz with a sensitivity of 0.302 mV/g. For the elastic tape squeeze structure, the elastic tape is designed to encapsulate the optical fiber with a good frequency response from 1100 to 3100 Hz. In addition, by using the new film squeeze structure to measure the steady-state and non-steady-state vibration signals, the spectral components of sensing signals are analyzed by using the wavelet transformation for confirming the testing signals. These vibration fiber sensors can be applied in the measurement of high-end manufacture-facility vibration or earthquake vibrations etc.

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.