Vibration Events Research Articles

Contribution of anthropogenic vibration sources to crack growth in natural rock arches

Natural arches are culturally valued rock landforms common in sedimentary rocks of the Colorado Plateau and additionally occur broadly around the world. Recent notable collapses of some of these landforms have highlighted the need to better understand the mechanics of their failure. While environmentally driven weathering has been the focus of most previous studies of arch collapse, comparably little attention has been given to anthropogenic vibration sources and how these often slight- to moderate-magnitude shaking events might steadily weaken arches over time. We collected 12–15 months of continuous ambient vibration data from arches and nearby bedrock in both anthropogenically ‘noisy’ and ‘quiet’ locations and used these datasets to develop an annual model of arch peak ground velocity based on magnitude-cumulative frequency distributions. Working from these models, we added vibration events of varying magnitude or frequency of occurrence, informed by field data, imitating arch vibration in response to different anthropogenic activities such as helicopter flights or induced earthquakes. We then applied subcritical fracture mechanics principles to predict annual crack growth rates in an idealized arch under these different vibration conditions. Our results demonstrate that in a single year, cracks grow minimally longer (∼1%) in ‘noisy’ environments than in areas not experiencing anthropogenic vibration energy. Few (1+) 30-s moderate-magnitude events (∼15 mm/s) or many (>37,000) 30-s low-magnitude events (∼2 mm/s) cause markedly increased crack growth. Our approach provides a valuable new framework for assessing the range and frequency of occurrence of vibrations experienced by an arch, and for predicting arch damage. Our results, in turn, yield important new outputs applicable in support of conservation management of these and similar landforms world-wide under exposure to a range of human-induced vibration activity.

A Novel Distributed Vibration Sensor Based on Fading Noise Reduction in Multi-Mode Fiber.

Multi-mode fiber (MMF) is used in a polarization-sensitive optical time domain reflectometer (OTDR) for vibration event location and spectrum analysis. The vibration events acting on MMF are considered to be the optical polarization state and phase diversifying process for fading noise reduction. In addition, data averaging with continuous positions and the fast Fourier transform (FFT) method is proposed to extract the spectrum of the vibration events. In the experiment, the vibration events are loaded at the positions of 5.167 and 10.145 km, respectively, along MMF. The experimental results demonstrate that the vibration event can effectively diversify the optical polarization state and phase of the Rayleigh scattering light to make the averaged OTDR trace behind the vibration position converge rapidly, which helps to locate corresponding vibration events and extract the vibration spectrum. It is inferred that the new distributed vibration sensor shall have a lower false alarm rate, as it can greatly reduce the errors caused by randomness of the sensing light signals. Additionally, it also saves time in comparison with the method that analyzes the vibration spectra for all the positions along the fiber under test.

Co-Simulation-Based Verification of Torsional Vibration Protection of Electric-Driven Railway Vehicle Wheelsets

Torsional vibration is an oscillation phenomenon occurring at driven railway vehicle wheelsets. As the resulting dynamic stresses can be significantly larger than the maximum static motor torque, axle and press fit are at risk of failure. To prevent dangerous vibration events and with these, press fit and axle from failure, traction drive manufactures nowadays used to implement vibration suppression algorithms in drive controls. In this paper, the effectiveness of such suppression algorithms is analyzed. Furthermore, as a pilot survey, we analyze to what extend traction controls influence the excitation of torsional vibration.

Optical Fiber Vibration Signal Recognition Based on the Fusion of Multi-Scale Features.

Because of the problem of low recognition accuracy in the recognition of intrusion vibration events by the distributed Sagnac type optical fiber sensing system, this paper combines the traditional optical fiber vibration signal recognition idea and the characteristics of automatic feature extraction by a convolutional neural network (CNN) to construct a new endpoint detection algorithm and a method of fusing multiple–scale features CNN to recognize fiber vibration signals. Firstly, a new endpoint detection algorithm combining spectral centroid and energy spectral entropy product is used to detect the vibration part of the original signal, which is used to improve the detection effect of endpoint detection. Then, CNNs of different scales are used to extract the multi–level and multi–scale features of the signal. Aiming at the problem of information loss in the pooling process, a new method of combining differential pooling features is used. Finally, a multi–layer perceptron (MLP) is used to recognize the extracted features. Experiments show that the method has an average recognition accuracy rate of 98.75% for the four types of vibration signals. Compared with traditional EMD and VMD pattern recognition and 1D–CNN methods, the accuracy of the optical fiber vibration signal recognition is higher.

Bayesian inference based parametric identification of vortex-excited force using on-site measured vibration data on a long-span bridge

In recent years, vortex-induced vibration (VIV) events have occurred on several long-span suspension bridges around the world. Normally, the VIV of a long-span bridge is investigated in wind tunnel tests or computational fluid dynamics. However, examination of bridge VIV through full-scale field test data has rarely been conducted. Because of the rapid development of high precision sensors and high-frequency data transmission devices, the acquisition of structural modal information utilizing field test data from structural health monitoring systems is emerging as a powerful tool to explore the structural dynamic status and locate potential damage. Therefore, it is possible and necessary to inspect the bridge VEF (vortex-excited force) parameters from full-scale field test data and then to simulate and estimate the structural VIV response based on VEF parameters. Existing VEF parametric identification techniques allow structures (sectional model or full-scale bridges) to be tested under laminar flow in wind tunnel tests with known dynamic properties (inertial frequency and damping ratio), requiring measurement of responsive signals and VEF signals synchronously. However, for the actual field test of the full-scale bridge, the flow field is turbulent, and the structural responsive signal is unavoidably contaminated by measuring noises. Furthermore, it is impractical to synchronously record the aerodynamic force applied on the bridge deck during the field test. In this study, a Bayesian inference approach is introduced for the identification of VEF parameters using field vibration data. Using the fast Fourier transform (FFT) of field vibration data, a frequency domain formulation is proposed focusing on the structural vibration mode excited during VIV events. This method fully considers the influence of random vibration induced by ambient excitation and instrument measurement error on the field vibration data, and only the responsive data are needed without measuring the aerodynamic force information.

Study on Movement Law of High-Position Thick and Hard Roof and Mine Earthquake Control by Ground Fracturing Technology

The field monitoring data showed that the fracture of high-position thick and hard roof (THR) was the main reason for the frequent occurrence of high-energy mine earthquake events ( magnitude > 2.0 ) in the no. 6 mining district of Dongtan coal mine. In order to study the fracture characteristics of high-level THR and seek a reasonable and effective control method, taking the 63up06 working face as the engineering background, the FLAC model was built to simulate the strata movement law before and after fracturing. The simulation results showed that the THR first breaking span was 340 m, the tensile failure runs through the whole THR, and the subsidence increased rapidly when THR reached the first breaking span. The THR breakage is related to the continuous upward transfer of horizontal compressive stress, which results in the concentration of horizontal compressive stress in THR. After ground fracturing, the first breaking span of THR decreased, and different fracturing positions have a great impact on the THR fracture form. When the fracturing position is close to the center of the first breaking span, there is no large cantilever breaking form in the THR, the THR breaks on both sides of the top and sinks along the hydraulic fracture in the middle, and the collapse degree is relatively uniform. Then, the fracturing test was carried out in the 63up06 working face, the results showed that during the first 400 m of the 63up06 working face mining after fracturing, 702 microseismic events occurred in total, including 12 high-energy vibration events which include 4 mine earthquakes of magnitude 2.0 and 8 mine earthquakes of magnitude 1.0-2.0. Compared with the adjacent working face, the quantity of high-energy mine earthquake events in the 63up06 working face has been significantly controlled.

On-line mechanical life prediction method for a conventional circuit breaker based on multi-parameter particle swarm optimization-support vector regression using vibration detection

For on-line mechanical condition monitoring of conventional circuit breakers, based on the analysis of vibration signal measurements and the generation principle in the circuit breaker breaking motion process, the time parameters for mechanism action with clear physical meaning are correlated with vibration events. Thus, an on-line mechanical life prediction method based on vibration detection and multi-parameter support vector regression (MP-SVR) optimized by particle swarm optimization is proposed. This method aims to use the action time parameter as the main monitoring parameter to reflect the degradation of mechanical conditions and the life failure criterion. First, the complete MPs of life degradation are obtained through the designed on-line measurement algorithm for the action time parameter and the assisted time-domain features of vibration signals in the corresponding time. Then, a quantitative life prediction model is constructed based on MP-SVR. Finally, the degraded MPs are used as input to the prediction model to predict the remaining mechanical life. Our experiment shows that this on-line model for life evaluation is definitely effective and practical in engineering applications.

Multi-parameter measurement of a multi-point high-frequency vibration signal in an OFDR system.

The phase cross-correlation function of an optical frequency domain reflectometry (OFDR) system is proposed to detect a multi-point vibration event, which is verified in detail by theoretical simulation and experiment. An OFDR system based on a non-tunable laser source with digital sweep frequency is developed. It is verified experimentally that the location and frequency resolution of multi-point high-frequency vibration can be detected by analyzing the phase cross-correlation function of the sensing signal. High-frequency signals of 50 kHz and 20 kHz are located and separated on the 8 km optical fiber. The frequency resolution is 1.26 kHz, and the minimum spatial error is 11.5 m.

Influence of the Order of Phase Differentiation and Unwrapping on Retrieval of Temporal Waveforms in Phase-Demodulated φ-OTDR

Generally, phase differentiation and unwrapping are both used to retrieve vibration events in phase-demodulated φ-OTDR. We investigate the influence of the order of these two operations on vibration retrieval, i.e., unwrapping the differential phase (UDP) and differentiating the unwrapped phase (DUP). The results demonstrate the UDP would add an extra DC value on the resulting phase and be unable to exactly restore temporal waveforms of vibration signals. Hence, it is necessary to firstly unwrap the original demodulated phase before performing differentiation to reliably restore the disturbances. Before retrieving temporal waveforms of vibration signals, a sliding rectification average (SRA) is applied to locate multiple events for better noise performance. In our experiment, the temporal waveforms of two different vibration sources at different positions are retrieved by phase changes extracted by the DUP. It is observed that the SNR is considerably improved due to reduction of background noises, and time-domain vibration signals are well recovered by the DUP-acquired phase changes.

High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time-Space Analysis.

Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications.

An innovative measurement-while-drilling method applied in construction of underground stress relief hole with more vibration events

An innovative measurement-while-drilling method applied in construction of underground stress relief hole with more vibration events

Vibration Detection and Localization Using Modified Digital Coherent Telecom Transponders

We demonstrate a vibration detection and localization scheme based on bidirectional transmission of telecom signals with digital coherent detection at the receivers. Optical phase is extracted from the digital signal processing blocks of the coherent receiver, from which the vibration component is extracted by bandpass filtering, and the position along the cable closest to the vibration's epicenter is recovered by correlation. We demonstrate our scheme first using offline experiment with 200-Gb/s DP-16QAM, and we report field trial results over installed fiber to detect real-world vibration events.

Time shifting deviation method enhanced laser interferometry: ultrahigh precision localizing of traffic vibration using an urban fiber link

Using a fiber network as a huge sensing system will enrich monitoring methods of public infrastructures and geological disasters. With the traditional cross-correlation method, a laser interferometer has been used to detect and localize the vibration event. However, the random error induced by the cross-correlation method limits the localization accuracy and makes it not suitable for ultrahigh precision localizing applications. We propose a novel time shifting deviation (TSDEV) method, which has advantages over the cross-correlation method in practicability and localization accuracy. Three experiments are carried out to demonstrate the novelty of the TSDEV method. In a lab test, vibration localization accuracy of ∼ 2.5 m is realized. In field tests, TSDEV method enhanced interferometry is applied to monitor the urban fiber link. Traffic vibration events on the campus road and Beijing ring road have been precisely localized and analyzed, respectively. The proposed technique will extend the function of the existing urban fiber network, and better serve the future smart city.

Dynamic motion monitoring of a 3.6 km long steel rod in a borehole during cold-water injection with distributed fiber-optic sensing

Abstract. Fiber-optic distributed acoustic sensing (DAS) data find many applications in wellbore monitoring such as flow monitoring, formation evaluation and well integrity studies. For horizontal or highly deviated wells, wellbore fiber-optic installations can be conducted by mounting the sensing cable to a rigid structure (casing/tubing) which allows for a controlled landing of the cable. We analyze a cold-water injection phase in a geothermal well with a 3.6 km long fiber-optic installation mounted to a 3/4 in. sucker rod by using both DAS and distributed temperature sensing (DTS) data. During cold-water injection, we observe distinct vibrational events (shock waves) which originate in the reservoir interval and migrate up- and downwards. We use temperature differences from the DTS data to determine the theoretical thermal contraction and integrated DAS data to estimate the actual deformation of the rod construction. The results suggest that the rod experiences thermal stresses along the installation length – partly in the compressional and partly in the extensional regime. We find strong evidence that the observed vibrational events originate from the release of the thermal stresses when the friction of the rod against the borehole wall is overcome. Within this study, we show the influence of temperature changes on the acquisition of distributed acoustic/strain sensing data along a fiber-optic cable suspended along a rigid but freely hanging rod. We show that observed vibrational events do not necessarily originate from induced seismicity in the reservoir but instead can originate from stick–slip behavior of the rod construction that holds the measurement equipment.

Free vibration analysis of polymer composite plates reinforced with graphene platelets

Composite materials are used because of its wide range of applications in structural design, especially due to its stringent stiffness and strength requirements in lightweight structures. They are used in aviation and space technology, motorsports and many industrial machines for decades. Reinforcements are provided to improve the tensile strength and young’s modulus of composite plates. Carbon nanotube (CNT) are commonly used as reinforcement in composite structure because of its preferable mechanical properties. However, due to high manufacturing costs, difficulty to attain uniform dispersion and highly anisotropic attributes of CNT-reinforced composite structures, its development is prevented. Hence graphene and its derivatives are used due to their supreme mechanical properties, improved reinforcing effects, considerable electrical and thermal conductivities and moderate cost. Structural vibration occurs when dynamic forces generated by compressors, pumps and engines causes the structure to vibrate. Natural frequency, also known as eigenfrequency, is thefrequencyat which asystemtends to vibrate in the absence of any driving or damping force oris thefrequencyat which it vibrates naturally once it has been set into motion. To detect abnormal vibration events and to evaluate the overall condition of composite plates, vibration analysis is a necessity. Designing the response of a structure and to take necessary steps to control the structural vibrations and its amplitude vibration analysis has become very important. Various parametric studies are carried out in terms of natural frequency and mode shapes to describe the effectiveness of polymer composite plates. The literature studies reveals that the boundary condition influences the natural frequency and mode shapes of composite plates. The present investigation is to determine the natural frequency of polymer composite plates reinforced with graphene platelets using ANSYS software.

Microseismic monitoring to assess rock mass damaging through a novel damping ratio-based approach

Monitoring rock mass damaging through the analysis of seismic records is an affirmed tool to manage rock fall risk in view of mitigation strategies. This study proposes a novel approach, named DAmping RaTio Anomalies Monitoring (DaRtAM), to detect the rock mass damaging by measuring variation over time of the damping ratio related to in-situ recorded vibrational events. Although damping-based damage detection techniques are well known for structural monitoring, only few applications exist for rock mass damage investigation not including in-situ long term rock mass monitoring. Here, a three-month-long seismic dataset recorded on a rock wall (Acuto Field Laboratory) by means of six one-component accelerometers was analysed with a STA/LTA algorithm that allowed to extract 19013 vibrational events. The damping ratio of the signals was derived as a function of frequency, averaged in a daily time window and analysed over time to output short-term anomalies and long-term trend. Although the limited time period analysed did not allow to observe significant trend variation of the damping ratio, 131 short-term anomalies were found in the collected seismic records. As it results from this study, the proposed approach is suitable for detecting rock mass damaging and, as a future perspective, will be applied to yearly-lasting vibrational datasets. This approach may be more effective with data acquired in environments characterised by recurrent forcings, such as coastal cliffs forced by sea and wind storms, rock walls stressed by thermal cycles, quarry walls involved in human activities or rocky trenches close to roads and railways.

Tunnel disturbance events monitoring and recognition with distributed acoustic sensing (DAS)

Accurately identifying disturbance events along tunnels is essential for their safe operation, which constitutes an important part of tunnel health monitoring and abnormity warning. In recent years, distributed acoustic sensing (DAS), a state-of-the-art fiber-optic sensing technology, has developed rapidly in the field of earth science and engineering. Based on the principle of phase-sensitive optical time-domain reflectometry, DAS allows detecting acoustic/vibration signals along a common fiber-optic cable up to tens of kilometers. This brings new opportunities for monitoring of long perimeters such as underground tunnels. In this paper, we propose a DAS-based method for the recognition of disturbance events along tunnels. The EMD denoising algorithm is employed to optimize vibration signals to better extract the time–frequency domain features of monitored events. Different events are then recognized via a machine learning-based multi-class classification approach. The Random Forest algorithm is applied to analyze the DAS data acquired with fiber-optic cables deployed along the tunnel lining, successfully recognizing a variety of vibration events during the construction of the tunnel including unexpected disasters such as rockfalls, with a recognition accuracy of 92.31%. This DAS-based disturbance identification method may provide a new opportunity for unmanned, real-time monitoring of tunnel abnormal events.

Long-distance Φ-OTDR with a flexible frequency response based on time division multiplexing.

In this study, a long-distance phase-sensitive optical time domain reflectometry (Φ-OTDR) with a flexible frequency response based on time division multiplexing is proposed and experimentally demonstrated. Distributed flexible frequency vibration sensing over long distance can be realized by reconfiguring the system layout in a time-division-multiplexed manner by re-routing the Rayleigh backscattered signals for segmented processing with extra erbium-doped fiber amplifiers added only instead of any other complex signal amplification or pulse modulation mechanisms. Through time-division-multiplexed reconfiguration, the tradeoff between sensing distance and vibration frequency response in Φ-OTDR system is largely relieved. Compared with the traditional system layout, the proposed system allows a flexible frequency response in each sensing fiber segment without any crosstalk among them. In experiments, distributed vibration sensing with a frequency response up to 4.5 kHz is achieved over a sensing distance of 60km by the proposed system, which is not possible in a conventional Φ-OTDR system. Furthermore, the frequency response flexibility of the proposed system is further verified by successfully identifying a vibration event with a frequency of up to 20 kHz at the end of a 52-km-long fiber.

Vibration Behavior during Underground Drilling Based on an Innovative Measurement Method and the Application

Pressure relief drilling is one of the most common techniques to reduce the impact of rock burst, but the useful dynamic phenomena in the drilling process are ignored due to the lack of corresponding technical methods. Based on the fact, an innovative measurement method for vibration signals during the drilling construction by pneumatic drill rig is presented in this study. The acquisition and analysis of vibration signals show that the vibration information can reflect the drilling depth, the amplitude of different depth can reflect the pressure, and the vibration events of different depths can reflect the pressure relief effect. The method proposed in the study is of great significance to the supervision of the relief work quantity, the disclosure of the pressure situation, and the evaluation of the pressure relief effect.

Unidirectional Ultra-Long Distributed Optical Fiber Sensor

In this paper, we propose and experimentally demonstrate an ultra-long distributed fiber vibration sensing system using unidirectional forward transmission of a continuous-wave signal and coherent detection with digital signal processing. Two optical fibers, which are close to each other, are deployed for sensing. A loop-back configuration is formed by splicing these two optical fibers at the far end of these two fibers. The location of the vibration event is identified by analyzing the null points in the frequency spectrum of the extracted phase signal. Thanks to the nature of unidirectional forward transmission, the Rayleigh backscattering noise can be avoided. Meanwhile, forward transmission enables optical amplifiers to compensate for the signal loss and hence fundamentally overcome the sensing range limit. We successfully demonstrate the localization of single point and multi-point vibrations with measurement errors of less than ±100 m and ±200 m, respectively, over a 500-km sensing range. The proposed scheme opens new possibilities in ultra-long haul distributed optical sensing applications.