Vibration Monitoring Research Articles

Temperature characteristics and nonlinear calibration model of self-enabled piezoelectric geocable for infrastructure monitoring

Abstract Sensor-enabled piezoelectric geocables (SPGCs) are used for strain and vibration monitoring of various infrastructures structures. However, as a distributed sensor, the influence of temperature on the monitoring signal of SPGCs is inevitable. This study tested the impedance change of SPGCs in static temperature-varying tests and carried out dynamic tensile tests at different temperatures. The static temperature-varying tests showed that, at temperatures ranging from -10 to 50℃, the SPGC normalized impedance change value increased with increasing temperature. At temperatures ranging from -10 to 20℃ and 20 to 50℃, the normalized impedance change value and temperature were exponentially and linearly related, respectively. Dynamic tensile tests showed that the SPGC tensile strength and yield strain decreased with increasing temperature. With increasing temperature, the SPGC voltage waveform and value changed less, while the impedance response became more sensitive. Therefore, a nonlinear calibration model of the SPGC impedance-strain effect considering the temperature effect was proposed, which can be used for strain calculation under different field ambient temperatures. The test results revealed the influence of temperature on the mechanical and monitoring performance of SPGC, providing support for further engineering applications of this technology.

Identification of blade vibration parameters based on improved blade tip timing technology and two-factor analysis method

Abstract Long-term complex alternating loads will significantly impact the lifespan of blades. As a non-contact measurement method, Blade tip timing (BTT) is regarded as an efficient technique for blade vibration monitoring as it reflects the condition of blades. Most of the existing parameter identification methods need several sensor and strict probe layout requirements; thus, an inappropriate sensor layout may fail BTT measurements. Using fewer sensors can reduce the impact on the compressor casing strength and the cost of test system.
Considering arrangement, cost, and safety restrictions,in this work, a novel blade vibration parameters identification method via a single BTT sensor is proposed. A method of improved BTT techonlogy is presented which obtain two-channel vibration displacement sequence using a single sensor. The elliptic model between two-channel vibration displacement sequence is established. Based on the model, the relationship between blade vibration parameters and elliptic geometric parameters is derived. Numerical simulation and experimental results show that the method is highly feasible and robust. The advantage of this method is that it utilizes only one sensor and achieves high accuracy identification of blade vibration parameter, which provides a new idea to blade vibration monitoring.

Wireless Remote-Monitoring Technology for Wind-Induced Galloping and Vibration of Transmission Lines

In order to achieve wireless remote monitoring of wind-induced vibrations in power-transmission lines based on MEMS sensors, it is necessary to devise a method for reconstructing the wind swing curve, enabling the device’s real-time performance to promptly acquire, restore, and analyze data. Based on existing single-axis vibration-sensitive components, a measurement array using self-powered MEMS sensors and spacers has been designed. The Orthogonal Matching Pursuit (OMP) algorithm is selected to obtain displacement data collected by sensors installed on the transmission-line spacers. Leveraging the inherent sparsity of the data, a Gaussian white noise regularization matrix is chosen to establish the observation matrix. Through the algorithm, wind data curve reconstruction is achieved, enabling the reconstruction of large-span wind-induced vibration information without distortion. The experimental results demonstrate that when applying the orthogonal tracking algorithm in transmission-line curve reconstruction, sparsity is selected based on the sampling length, that is, the number of sensors installed on the spacers is determined by the span length; a portion of the observation values are selected to generate the observation matrix; and the wind galloping data curve of the transmission line is well restored.

Development of a Conceptual Scheme for Controlling Tool Wear During Cutting, Based on the Interaction of Virtual Models of a Digital Twin and a Vibration Monitoring System.

This article discusses the issue of the joint use of neural network algorithms for data processing and deterministic mathematical models. The use of a new approach is proposed, to determine the discrepancy between data from a vibration monitoring system of the cutting process and the calculated data obtained by modeling mathematical models of the digital twin system of the cutting process. This approach is justified by the fact that some coordinates for the state of the cutting process cannot be measured, and the vibration signals measured by the vibration monitoring system (the vibration acceleration of the tip of the cutting tool) are subject to external disturbing influences. Both the experimental method and the Matlab 2022b simulation method were used as research methods. The experimental research method is based on the widespread use of modern analog vibration transducers, the signals from which undergo the process of digitalization and further processing in order to identify arrays of additional information required for virtual digital twin models. The results obtained allow us to formulate a new conceptual approach to the construction of systems for determining the degree of cutting tool wear, based on the joint use of computational virtual models of the digital twin system and data obtained from the vibration monitoring system of the cutting process.

Ion‐Permeable Electrospun Scaffolds Enable Controlled In‐Vitro Electrostimulation Assay of Myoblasts

AbstractIn‐vitro models are fundamental for studying muscular cell contractility and for wide‐screening of therapeutic candidates targeting skeletal muscle diseases, owing to their scalability, reproducibility, and circumvention of ethical concerns. However, in‐vitro assays permitting reliable electrical stimulation of cell contractile activity still require technological development. Here, a novel approach to electrically stimulate differentiated muscular cell contractility is reported exploiting the ionic conductivity and mechanical flexibility of 3D nanofibrous scaffolds. The electrospun poly(L‐lactide‐co‐caprolactone) scaffold allowed for C2C12 murine myoblasts horizontal elongation and myotubes formation. Scaffold porosity enables high ionic conductivity and strong electric field generation, orthogonally oriented to the scaffold surface. Electrically induced cell contractility is determined with atomic force microscopy (AFM) enabling real‐time monitoring of scaffold vibrations in liquid environment. Differentiated cell actuation is found to be linearly correlated to current amplitude and number of current stimuli. Integrating the 3D nanofibrous scaffolds with real‐time AFM monitoring provides highly accurate in‐vitro assays for biomedical research. The induction of electric fields orthogonal to the scaffold surface allows for accurately mimicking the excitation‐contraction coupling mechanism observed in native skeletal muscle tissue. This work paves the way for the quantitative study of muscular cell dynamic behavior and physiology, further evaluating therapy effectiveness for muscular pathologies.

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.

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.

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

High sensitivity at low frequencies is required by many vibration monitoring scenarios, yet commercial piezoelectric vibration sensors often lack adequate sensitivity in this range. This work introduces a novel non-contact triboelectric vibration sensor featuring a spiral floating electrode structure (SFE-TEVS), achieving an impressive sensitivity of 100 V/g, 500 times higher than commercial piezoelectric vibration sensors and operating across a broad frequency range (1–1000 Hz). The non-contact design and integrated structure contribute to durability and maintain stable performance over 300,000 cycles. Additionally, the SFE-TEVS demonstrates excellent acceleration linearity (R² = 99.95 %) and frequency consistency, both of which are consistent with the results of theoretical analysis. Thus, it was employed to monitor low-frequency active seismic signals and machinery operating states. The SFE-TEVS was further fabricated into a hydrophone for monitoring very-low frequency underwater acoustic waves. It exhibited a sensitivity far superior to the commercial 8105 hydrophone in a 10–100 Hz frequency range, surpassing it by 31.7 dB at 65 Hz, meanwhile exhibiting excellent directivity. The SFE-TEVS combines high sensitivity, broad frequency range, and remarkable durability, showcasing the great potential for active seismic, machinery vibration, and underwater acoustics monitoring, further advancing the application of TENG in these fields.

Negative impact of vibration on process pipelines of a compressor station with electrically driven gas pumping units

Relevance. In gas industry, the amount of equipment that has outlived its intended service life is increasing every year. In accordance with the law “On Industrial Safety of Hazardous Production Facilities,” compressor stations are dangerous objects, the reliable operation of which largely determines the condition of main gas pipelines. Technological pipelines of a compressor station are important objects of this system. They are subject to strict requirements due to the action of static and dynamic loads on them. Reliability of compressor stations is based on the results of a study of the condition of the object and the main factors affecting increased wear of equipment. The solution to wear problem is timely monitoring of equipment, in particular level measurement and determining the causes of pipeline vibrations using vibration diagnostics methods, and frequency and modal analysis methods. Aim. To study the causes of increased dynamic loads on the technological piping that arise during the operation of electric and gas pumping units at a compressor station using vibration monitoring methods. Methods. Vibration monitoring methods to analyze the causes of increased vibration values in the process piping of a compressor station with an electric gas pumping unit. Results and conclusions. The authors have measured vibration in a wide frequency band and assessed the technological condition of the main equipment of the compressor station with EGPA-6.3/8200-56/1.44-R based on the regulatory documentation STO Gazprom 2-2.3-324-2009. The main frequencies of the root-mean-square value of the vibration velocity, at which the highest vibration values were observed, were identified, and a modal analysis was performed using ANSYS software. Based on the results of natural studies and modal analysis, it was concluded about the occurrence of resonant high-frequency oscillations, multiple of the rotor rotation frequency.

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.

Localized Bearing Fault Analysis for Different Induction Machine Start-Up Modes via Vibration Time-Frequency Envelope Spectrum.

Bearings are the most vulnerable component in low-voltage induction motors from a maintenance standpoint. Vibration monitoring is the benchmark technique for identifying mechanical faults in rotating machinery, including the diagnosis of bearing defects. The study of different bearing fault phenomena under induction motor transient conditions offers interesting capabilities to enhance classic fault detection techniques. This study analyzes the low-frequency localized bearing fault signatures in both the inner and outer races during the start-up and steady-state operation of inverter-fed and line-started induction motors. For this aim, the classic vibration envelope spectrum technique is explored in the time-frequency domain by using a simple, resampling-free, Short Time Fourier Transform (STFT) and a band-pass filtering stage. The vibration data are acquired in the motor housing in the radial direction for different load points. In addition, two different localized defect sizes are considered to explore the influence of the defect width. The analysis of extracted low-frequency characteristic frequencies conducted in this study demonstrates the feasibility of detecting early-stage localized bearing defects in induction motors across various operating conditions and actuation modes.

Order-difference of normalized square envelope spectrum and its applications in early chatter detection of roll grinder

Roll grinder is widely demanded in grinding the surface of rolls that will be used to mill steel strips for high-quality automobile outer panels. The grinding chatter generally leads to poor machining accuracy and massive economic losses. The significance of roll grinder vibration monitoring is to detect the early grinding chatter. However, in harsh working conditions, some chatter detection methods inevitably confuse the fault components and interference components in vibration signals and the chatter feature information cannot be extracted exactly. The dynamical model of roll grinder system reveals that the spectrums of vibration signals contain intrinsic frequency components and chatter frequency components under health and chatter conditions. Based on the analytical modelling, a new health index order-difference of normalized square envelope spectrum (ODNSES) is proposed to detect early chatter and evaluate the health degradation of the roll grinder. The normalized square envelope spectrum (SES) is used as the pretreatment of ODNSES to compare the frequency components in vibration signals under health and early chatter conditions. Furthermore, order-difference of normalized SES is proposed to suppress the noise interference and to enable ODNSES to characterize the grinding chatter process. The detailed simulation research is performed to verify three important properties of ODNSES. The experimental results show that ODNSES would not be interfered by background noise significantly and it is on average 29.12% more sensitivity to impulse amplitudes than classical health indexes. The testing results on actual roll grinder exhibit that ODNSES can detect the early chatter and quantify the abnormal vibration in the grinding process.

Deep Integration Between Polarimetric Forward-Transmission Fiber-Optic Communication and Distributed Sensing Systems.

The structural health of fiber-optic communication networks has become increasingly important due to their widespread deployment and reliance in interconnected cities. We demonstrate a smart upgrade of a communication system employing a dual-polarization-state polarization shift keying (2-PolSK) modulation format to enable distributed vibration monitoring. Sensing can be conducted without hardware changes or occupying additional communication bandwidth. Experimental results demonstrate that forward transmission-based distributed vibration sensing can coexist with PolSK data transmission without significant deterioration in performance. This proof-of-concept study achieved a sensitivity of 0.4141 μV/με with a limit of detection (LoD) of 563 pε/Hz1/2@100 Hz. The single-span sensing distance can reach up to 121 km (no optical amplification) with a positioning accuracy as small as 874 m. The transmission rate is 300 Mb/s, the QdB is 16.78 dB, and the corresponding BER is 5.202 × 10-12. For demonstration purposes, the tested vibration frequency range is between 100 and 200 Hz.

Design of monitoring system for material vibration screening equipment

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

Vibration monitoring of masonry bridges to assess damage under changing temperature

Structural Health Monitoring (SHM) is of utmost importance for the preservation and safe operation of historical arch bridges. This paper presents the development of a SHM strategy aimed at the model-based damage assessment of masonry bridges using frequency data. Structural damage induces natural frequency changes that are strictly related to the damage location. Consequently, a numerical model capable of reproducing the intact dynamic characteristics should allow to simulate damage scenarios, including the observed one, with the anomaly localisation being performed through the similarity between the experimentally detected frequency changes and the numerically simulated ones. The proposed methodology is based on the availability of an appropriate knowledge of the investigated structure, allowing to define a Finite Element (FE) model that accurately reproduces the system dynamic characteristics. Hence, the SHM strategy involves: (a) the use of the calibrated model to simulate different damage scenarios, so that a Damage Location Reference Matrix (DLRM) is defined through the associated frequency shifts; (b) the damage detection through statistical pattern recognition of vibration data; (c) the damage localisation through the comparison between the identified frequency changes and those defined in the DLRM matrix. Pseudo-experimental monitoring data, referring to a historical masonry viaduct, were generated and used to exemplify the reliability and accuracy of the developed algorithms in detecting and localizing damage.

Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis

Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis

Synchronous LoRa sensor nodes for modal identification in footbridge vibration monitoring

AbstractThis article aims at presenting a preliminary investigation of using long-range and low-cost LoRa (Long Range) technology and two synchronous LoRa sensor nodes for cost-effective footbridge structural health monitoring (SHM). Two sensor nodes with LoRa modules and accelerometers were employed for vibration monitoring and a new attempt was made to use synchronous LoRa nodes for modal identification. In this article, a modal identification method based on a lightweight synchronization concept was proposed. This method is able to identify the fundamental mode of vibrating beam structures. Meanwhile, maximum accelerations can also be tracked periodically from the simultaneously recorded acceleration data by the synchronous LoRa nodes. Specifically, synchronization was achieved through the wireless peer-to-peer (P2P) communication between the two LoRa nodes, with the aim of initializing simultaneous acceleration recordings. The fundamental frequency and the phase information derived from the on-board calculation of the synchronized nodes can then be used to effectively identify the vertical bending mode and torsion mode. A series of laboratory tests were also conducted on a beam structure for the purpose of validation. The test results showed that the fundamental mode of the vibrating beam can be obtained rapidly and accurately using the synchronized LoRa nodes, with an average synchronization accuracy of 4.45 ms. The maximum acceleration data recorded by the LoRa nodes also showed high accuracy when compared with the raw acceleration data collected from a commercial Bluetooth accelerometer node. The fundamental frequencies obtained from both types of nodes also compare reasonably. The proposed modal identification method using two synchronous LoRa sensor nodes provides a basis for the development of a low-cost footbridge SHM system with the integration of IoT techniques. An attempt has been made to perform a preliminary field test on a cable-stayed footbridge, where the fundamental frequency and the mode shape type of the footbridge were successfully identified and its serviceability condition was also found to satisfy the code requirements.

Blade tip timing signals parameters identification based on Displacement Offset Decreased and Compressed Sensing

High-speed rotating blade vibration monitoring is of great significance for the safe operation of turbomachinery. Blade Tip Timing (BTT) is considered a promising technology in the field of blade vibration monitoring. The BTT signal analysis has the following problem, the displacement offset was caused by speed variation which affected the accuracy of the blade vibration parameter identification. Owing to the under-sampled characteristics of the BTT signals and multiple sensor data are required to identify the parameters. The accurate extraction of blade vibration characteristics is challenging. Thus, a vibration analysis method is proposed based on Displacement Offset Decreased and Compressed Sensing (DOD-CS). Firstly, the BTT signal pairwise is subtracted to reduce the impact of the displacement offset, and based on the subtractive results, a basis function is constructed to rebuild the dictionary matrix of the compressed sensing method. Then, we solved the signal sparse representation equation and obtained the spectrum estimation result, that is, the structure vibration parameters. The analysis of simulation data shows that the parameter identification accuracy of the DOD-CS method is higher than that of the OMP-CS (Orthogonal Matching Pursuit and Compressed Sensing) method, and the identification errors of frequency and amplitude are within 1% and 5%, respectively. The experimental results confirm the feasibility of the proposed method in identifying vibration parameters. Compared with existing methods, the innovation of the proposed method is that it eliminates the influence of displacement offset on the accuracy of parameter identification. For under-sampled signals, only two sensor data information are needed to complete spectrum estimation and parameter identification.

Quantitative Assessment of Sand Particulates in Gas-Water Slug Flow Using Deep Learning

Summary The weak collision response excited by micrometer-scale sand particulates is prone to overmixing with strong slug noise, significantly reducing the characterization and monitoring accuracy of sand particulate information in slug flows. Therefore, we developed a quantitative assessment method for sand particulates in slug flow that combines triaxial vibration monitoring and deep learning. First, a migration behavior characterization method of sand particulates is proposed combining nonlinear statistics, multifrequency coherence, and multiscale time frequency. The multifrequency response characteristics corresponding to the multiscale flow behavior of the sand-carrying slug flow were successfully characterized on the 2D time-frequency plane, namely, the mixed sand migration behavior [Intrinsic Mode Function 1 (IMF1)], liquid slug sand carrying (IMF2), forward liquid film and Taylor bubble sand carrying (IMF3), and reflux liquid film sand carrying (IMF4). Furthermore, the influence mechanism of gas superficial velocity (1.5–3.5 m/s), liquid superficial velocity (0.95–2.14 m/s), and sand content (0–20 g) on the triaxial vibration response of slug particulate flow with different migration behaviors is elucidated. Finally, a convolutional neural network (CNN)-gated recurrent unit (GRU)-self-attention mechanism (SATT) model for sand content assessment is developed based on the characterized multiscale migration behavior information and achieves an average recognition accuracy of 95.55% for data sets representing different sand migration behaviors in slug flow. This provides a new method for precisely identifying and monitoring sand production information of multiphase pipe flow.

Close Range and High-Resolution Detection of Vibration by Ultrasonic Wave Using Silicon-on-Nothing PMUTs.

This article presents a novel method for close-range, high-resolution ultrasonic time-of-flight (ToF) ranging using piezoelectric micromachined ultrasonic transducers (PMUTs) operating below the device resonance in air. The proposed method involves cross correlation techniques to accurately detect the reflected echo signals despite the presence of ringdown signal interference. For the experiments, a high fill-factor array of silicon-on-nothing (SON) PMUTs was used to enhance the signal-to-noise ratio (SNR). A thorough investigation was conducted to determine the optimal driving frequency for below-resonance ToF ranging, to improve resolution and minimize detection errors. The results of the experiments showed that the system was able to accurately measure sub- μ m vibrations of a metal plate placed 13 mm away from the PMUT array. The system exhibited the ability to detect target object vibrations with a peak-to-peak displacement under 6 μ m and sub- μ m floor noise. Moreover, the maximum detectable vibration frequency reached up to 1 kHz. This study highlights the potential of the proposed ToF ranging method in noncontact vibration monitoring applications across various fields, such as robotics and predictive maintenance.