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This study presents a convolutional neural network (CNN) deep learning approach for identifying damage in submerged structure-foundation systems using vibration data. Firstly, foundation damage in a lab-scale caisson-foundation system is simulated to measure time-history responses. Singular value decomposition (SVD) responses are derived from the time-history responses. Secondly, the 1-D CNN deep learning model is trained using both the time-history responses and SVD responses. Finally, the trained CNN models are implemented to evaluate the foundation damage under conditions of noise contamination and partially untrained data. The experimental results demonstrate the effectiveness of CNN models for damage identification and highlight the comparative strengths of time-history and SVD data. The CNN model trained using SVD data outperforms the other model when under noise contamination conditions, while the CNN model trained using time-history data maintains better accuracy in partially untrained data conditions. Integrating both types of data enhances the accuracy of damage classification.

Monitoring induced vibrations caused by blasting works is becoming an increasingly common form of preventive activity conducted in open-pit mines. Measurement stations also record other events unrelated to blasting works. This article presents a comparison of the intensity of vibrations induced by blasting works in an open-pit mine and mining tremors in an underground mine. The recorded data and conducted analyses of vibration intensity and frequency structure also allowed for a comparison of the impact of vibrations on a building structure. Calculations and analyses, conducted in accordance with the procedures provided in the standard PN-B-02170:2016-12 and the rules for applying the Mining Seismic Intensity Scale MSIS-2017, demonstrated a stronger impact on the building from induced vibrations in an underground mine located 10 km away compared to vibrations induced by blasting operations conducted in an open-pit mine, which is approximately 600 m away from the building. The presented material constitutes a unique set of data that can be used to introduce any necessary corrections in the methodology of analyzing vibrations regarding their harmfulness to building structures. The velocity value of vibrations correlated with frequency alone, without taking into account the vibration duration, can lead to incorrect interpretation.

Vibration monitoring is essential for maintaining the optimal performance and reliability of industrial machinery, which experiences dynamic forces and vibrations during operation. This study delved into a comprehensive analysis of vibration monitoring in hydraulic presses, utilizing advanced measurement systems equipped with accelerometers. The proposed system included a three-axis accelerometer, data acquisition unit, and dedicated measurement software, facilitating the precise monitoring and analysis of vibrations. The influence of the sensor placement and sampling frequency on the measurement results was examined. A time and frequency analysis of the recorded measurements was performed. The results demonstrated the correlation between vibration levels and various production parameters, such as the number of parts simultaneously produced and press pressure. These findings underscore the potential for vibration measurement as a pivotal component in controlling production parameter settings.

Mechanical action can damage physical objects, such as building structures. The transfer of energy to the ground through the falling mass, forming a crater, can cause kinematic forces in the soil medium. An effect of these forces, connected with the technology of geotechnical machines, is vibrations of the ground and building structures. When finding the energy impact on the object is difficult or even impossible, vibration monitoring and indirect estimation of the negative energy impact effects is possible. Commercial off-the-shelf solutions exist for vibration monitoring and recording, but they are dedicated to one application type and are relatively expensive. This paper presents a proprietary system for monitoring and recording vibrations of building objects transmitted through the ground. This allows for local registration and remote administration and presentation of data via a computer network. Vibrations can be analysed according to the requirements of developers and clients. The developed system shows that creating an effective approach for collecting data on building vibrations using components from different suppliers is possible. The waveforms collected using the system confirm the effectiveness and flexibility of the presented solution, providing a possibility of using the results to decide whether the vibrations are within the standard limits.

Active and passive vibration control systems are of paramount importance in many engineering applications. If an external load excites a structure’s resonance and the damping is too low, detrimental events, such as crack initiation, growth and, in the worst case, fatigue failure, can be entailed. Damping systems can be commonly found in applications such as industrial machines, vehicles, buildings, turbomachinery blades, and so forth. Active control systems usually achieve higher damping effectiveness than passive ones, but they need a sensor to detect the working conditions that require damping system activation. Recently, the development of such systems in rotating structures has received considerable interest among designers. As a result, the development of vibration monitoring equipment in rotating structures is also a topic of particular interest. In this respect, a reliable, inexpensive and wireless monitoring system is of utmost importance. Typically, optical systems are used to measure vibrations, but they are expensive and require rather complex processing algorithms. In this paper, a wireless system based on a commercial MEMS accelerometer is developed for rotating blade vibration monitoring. The proposed system measurement accuracy was assessed by means of comparison with a reference wired measurement setup based on a mini integrated circuit piezoelectric (ICP) accelerometer adapted for data acquisition in a rotating frame. Both the accelerometers were mounted on the tip of the blade and, in order to test the structure under different conditions, the first four blade resonances were excited by means of piezoelectric actuators, embedded in a novel experimental setup. The frequency and amplitude of acceleration, simultaneously measured by the reference and MEMS sensors, were compared with each other in order to investigate the viability and accuracy of the proposed wireless monitoring system. The rotor angular speed was varied from 0 to 300 rpm, and the data acquisitions were repeated six times for each considered condition. The outcomes reveal that the wireless measurement system may be successfully used for vibration monitoring in rotating blades.

In order to study the applicability of high-pressure gas expansion rock-cracking technology in hard rock tunnel near historic sites, theoretical analysis, field tests as well as vibration monitoring are conducted to obtain suitable rock mass cracking parameters for tunnel excavation. The results show that the ideal effect of rock mass cracking can be achieved with the cutting mode of “central vertical empty hole + double wedge cutting hole” and the auxiliary hole network parameter of “0.8 m × 0.7 m”. The measured vibration velocity is less than 0.1 cm/s at the monitoring point 60 m away from the tunnel face in the field test, which meets the vibration control requirements of the historic sites in the process of tunnel excavation. The research results show that as long as there is a high quality of hole plugging and no punching, the high-pressure gas expansion rock-cracking technology has the advantages of little vibration, low noise and less flying rocks, which provides a technical reference for the excavation of hard rock tunnels near ancient buildings and historic sites.

Double-shield TBM is more efficient than shield tunnel construction in hard rock strata. It is widely used in subway tunnel construction, such as in Qingdao and Chongqing in China. However, construction vibration problems caused by double-shield TBM critically affect human comfort in surface buildings. In order to study the vibration influence law of double-shield TBM, the on-site monitoring plan of double-shield TBM is developed, relying on a tunnel section of Qingdao Metro Line 4. Then, the vibration monitoring results are analyzed, and the time domain and frequency domain characteristics of vibration acceleration are obtained. Subsequently, taking vibration peak acceleration as the research object, the transverse and longitudinal vibration laws of double-shield TBM construction are studied. Finally, according to the standard of human comfort in buildings, the vibration influence ranges of double-shield TBM construction are obtained. The research results can provide a reference for the vibration effect of double-shield TBM passing through houses, highways and bridges. Moreover, it can also provide a basis for the disputes that double-shield TBM tunnel construction affects the lives of inhabitants.

Building structures are subject to various deformations caused by external and internal factors. Deformations are determined by various methods in the form of monitoring. It is very important to monitor the dynamic vibration response on bridge structures since these measurements allow us to identify any possible damage over time and take appropriate action. Our experiment, described in this article, is based on the use of non-contact methods, among which we used a geodetic instrument RTS (Robotic Total Station) and a seismograph to measure vibrations. The purpose and novelty of our work are reflected in the use of geodetic instruments to determine the dynamic response and synchronization of the obtained results. When using RTS technology, we increased data acquisition from 9 to 26 measurements per second. Comparative analysis of the measured signals was performed using FFT (Fast Fourier Transformation) and LSP (Lomb–Scargle Periodogram), based on LSSA (Least-Squares Spectral Analysis). The results showed us that when using the RTS geodetic instrument, it is possible to achieve frequency spectra comparable to those measured with a seismograph instrument. By increasing the number of measurements, the RTS method can be used to obtain more continuous data, which are essential for dynamic analyses.

Machinery with several rotating and stationary components tends to produce non-stationary and random vibration signatures due to the fluctuations in the input loads and process defects due to long hours of operation. Traditional heuristics methods are suitable for the detection of fault signatures, however, they become more complicated when the level of uncertainty or randomness exceeds beyond control. A novel methodology to identify these fault signatures using optimal filtering of vibration data is proposed to eliminate any false alarms and is expected to provide a higher probability of correct diagnosis. In this paper, a detailed pipeline of the algorithms are presented along with the results of the investigation that was carried out. These investigations are performed using open-source vibration data published by the NASA prognostics centre. The performance of these algorithms are evaluated based on the ground truth results published by NASA researchers. Based on the performance of these algorithms several parameters are fine-tuned to ensure generalisation and reliable performance.

Monitoring the structural health of wind turbine blades is essential to increase energy capture and operational safety of turbines, and therewith enhance competitiveness of wind energy. With the current trends of designing blades ever longer, detailed knowledge of the vibrational characteristics at any point along the blade is desirable. In our approach, we monitor vibrations during operation of the turbine by wirelessly measuring accelerations on the outside of the blades. We propose an algorithm to extract so-called vibration-based fingerprints from those measurements, i.e., dominant vibrations such as eigenfrequencies and narrow-band noise. These fingerprints can then be used for subsequent analysis and visualisation, e.g., for comparing fingerprints across several sensor positions and for identifying vibrations as global or local properties. In this study, data were collected by sensors on two test turbines and fingerprints were successfully extracted for vibrations with both low and high operational variability. An analysis of sensors on the same blade indicates that fingerprints deviate for positions at large radial distance or at different blade sides and, hence, an evaluation with larger datasets of sensors at different positions is promising. In addition, the results show that distributed measurements on the blades are needed to gain a detailed understanding of blade vibrations and thereby reduce loads, increase energy harvesting and improve future blade design. In doing so, our method provides a tool for analysing vibrations with relation to environmental and operational variability in a comprehensive manner.

Vibration-condition monitoring aims to detect bearing damages of rotating machinery’s incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior.

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accelerometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The method proposed in this paper was trained with transfer learning, a deep learning model that replaced the VGG19 model with a Fully Connected Layer (FCL) and Global Average Pooling (GAP) by converting the vibration signal into a short-time Fourier transform (STFT) or Mel-Frequency Cepstral Coefficients (MFCC) spectrogram and converting the input into a 2D image. As a result, the model proposed in this paper has seven times decreased trainable parameters of VGG19, and it is possible to quantify the severity while looking at the defect areas that cannot be seen with 1D.

Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator (HI) to infer the current condition of the component, and modelling the degradation process in order to estimate the future behavior. Although many signal processing and data-driven methods have been proposed to construct the HI, most of the existing methods are based on manual feature extraction techniques and require the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the HI. For this purpose, the continuous wavelet transform (CWT) technique was used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (MD) between the healthy and failure stages was measured as the HI. The proposed method was tested on a benchmark bearing dataset and compared with several other traditional HI construction models. Experimental results indicate that the constructed HI exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults.

Electromagnetic dampers are emerging as alternatives to conventional dampers applied to stay cables of bridges because they can reduce maintenance costs and allow vibration monitoring owing to their permanent driving characteristics and self-generation function. In this study, the main equations (including those for the induced electromotive force of the active coils and the total damping force of the damper) were derived through magnetic circuit analysis using the main parameters of the electromagnetic damper model. Characteristic tests were performed on electromagnetic damper prototypes to analyze the hysteretic dynamics and derive characteristics according to their structure and excitation conditions. On the basis of the results, we proposed a regenerative hybrid electrodynamic damper with an oxygen-free copper tube and teeth structure. Its physical and electromagnetic behaviors were examined through an electromagnetic analysis of the finite element model of the proposed damper. The results confirmed that attenuation occurred via strengthened magnetic flows, and the estimated power production is suitable for energy harvesting applications. Therefore, we confirmed the feasibility of constructing a system that can simultaneously perform cable attenuation and vibration monitoring using the proposed damper.