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Smart systems such as data-driven machine health monitoring are emerging as powerful technology for advanced manufacturing as a result of the availability of low-cost sensors, wireless communication, and advances in Machine Learning (ML) and Artificial Intelligence (AI). Predictive maintenance (PdM) has become increasingly popular in manufacturing, which can identify approaching failures, determine root causes of operation anomalies, estimate the current health state of a system, and predict the future state and time when a component will fail in the absence of an intervention. One weakness of many past studies is the lack of run-to-failure data from an actual production environment. This paper presents run-to-failure data for the air compressor of an injection molding machine. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is proposed to detect bearing faults in the air compressor, which can capture the long-term dependencies without losing the capability to identify local dependencies. The model achieves a 97.4% of prediction accuracy (95.3% of overall accuracy). Experiments for machine state classification are also conducted, and the classification performance compares favorably with conventional models. [ABSTRACT FROM AUTHOR]

To enhance the effectiveness of diagnosing axial bearing fault signals in moving trains, this study proposes a method that combines improved complementary set empirical modal decomposition and optimal minimum entropy deconvolutional adjustment. There are plans to develop a screening method based on intrinsic modal function components to further boost the diagnostic procedure's effectiveness. The simulation experimental validation showed that the fault eigenfrequencies from 1 to 7 octave may be identified by the research-proposed method after envelope spectral analysis. Case Western Reserve University dataset validation indicated that the proposed method is superior in terms of bearing fault signal processing results. The time-domain amplitude of the inner ring fault signal increased by 50% and was increased at all times compared to other methods. The eigen frequency of the inner ring fault signal was found to be between 1 and 9 octaves, whereas the outer ring fault signal was found to be between 1 and 14 octaves. The findings show that the suggested approach is capable of accurately diagnosing axlebox bearing fault signals in the locomotive group and of directly localizing the fault location based on the envelope spectrogram's characteristic frequency. [ABSTRACT FROM AUTHOR]

This work explores the potential of self-powered MEMS devices for application in the preventive conservation of cultural heritage. The main objective is to evaluate the effectiveness of piezoelectric aluminum nitride MEMS (AlN-MEMS) for monitoring vibrations and to investigate its potential for harvesting energy from vibrations, including those induced by visitors. A preliminary laboratory comparison was conducted between AlN-MEMS and the commercial device Tromino®. The study was then extended to the Picture Gallery of Ursino Castle, where joint measurements with the two devices were carried out. The analysis focused on identifying natural frequencies and vibrational energy levels by key metrics, including spectral peaks and the Power Spectral Density (PSD). The results indicated that the response of the AlN-MEMS aligned well with the data collected by the commercial device, especially observing high vibrational energy around 100 Hz. Such results validate the potential of AlN-MEMS for effective vibration measurement and for converting kinetic energy into electrical power, thereby eliminating the need for external power sources. Additionally, the vibrational analysis highlighted specific locations, such as the measurement point Cu4, as exhibiting the highest vibrational energy levels. These points could be used for placing MEMS sensors to ensure efficient vibration monitoring and energy harvesting. [ABSTRACT FROM AUTHOR]

Vibration sensors are widely applied in the detection of faults and analysis of operational states in engineering machinery and equipment. However, commercial vibration sensors with a feature of high hardness hinder their usage in some practical applications where the measured objects have irregular surfaces that are difficult to install. Moreover, as the operating environments of machinery become increasingly complex, there is a growing demand for sensors capable of working in wet and humid conditions. Here, we present a flexible, superhydrophobic vibration sensor with parallel microcracks. The sensor is fabricated using a femtosecond laser direct writing ablation strategy to create the parallel cracks on a PDMS film, followed by spray-coating with a conductive ink composed of MWCNTs, CB, and PDMS. The results demonstrate that the developed flexible sensor exhibits a high-frequency response of up to 2000 Hz, a high acceleration response of up to 100 m/s2, a water contact angle as high as 159.61°, and a linearity of 0.9812 between the voltage signal and acceleration. The results indicate that the sensor can be employed for underwater vibration, sound recognition, and vibration monitoring in fields such as shield cutters, holding significant potential for mechanical equipment vibration monitoring and speech-based human–machine interaction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Micronanoelectronic Technology is the property of Micronanoelectronic Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ground Improvement is the property of Journal of Ground Improvement Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Maintenance of mechanical elements is considered to be the most crucial task in industry and affects the economics, predominantly. There are many techniques available for fault diagnosis and condition monitoring of rotatory machine elements and one of the popular tools is vibration monitoring. Analyzing vibrations would be useful in supervising equipment/machine components in an attempt to detect the significant change which is the cause of growing failures or faults. Broadly speaking, it comes under prognostics and health monitoring (PHM) and prevents unexpected failure and costly repair. Proficient predictive monitoring and on-board recognition of failures is a need of the Fourth Industrial Revolution. Several types of vibration analyzers and sensors are commercially available, however, their total budget is not affordable. This motivates reviewing the suitability of low-cost sensors for vibration monitoring. An inclusive review of a variety of applications wherein microelectromechanical system (MEMS)-based accelerometer is used is presented in this paper. This would support an open-design movement for creating open. [ABSTRACT FROM AUTHOR]

This study introduces an Enhanced Autoregressive Integrated Moving Average (E-ARIMA) model for anomaly detection in time-series data, using vibrations monitored by CA 202 accelerometers at the Kirkuk Gas Power Plant as a case study. The objective is to overcome the limitations of traditional ARIMA models in analyzing the non-linear and dynamic nature of industrial sensory data. The novel proposed methodology includes data preparation through linear interpolation to address dataset gaps, stationarity confirmation via the Augmented Dickey-Fuller Test, and ARIMA model optimization against the Akaike Information Criterion, with a specialized time-series cross-validation technique. The results show that E-ARIMA model has superior performance in anomaly detection compared to conventional Seasonal ARIMA (SARIMA) and Vector Autoregressive models. In this regard, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) criteria were utilized for this evaluation. Finally, the most important achievement of this research is that the results highlight the enhanced predictive accuracy of the E-ARIMA model, making it a potent tool for industrial applications such as machinery health monitoring, where early detection of anomalies is crucial to prevent costly downtimes and facilitate maintenance planning. [ABSTRACT FROM AUTHOR]

The Induction Motor (IM) is one of the most frequently used prime movers in most industrial and transportation systems. The motor's stable and safe operation directly influences the secure and reliable operation of such prime movers. Developing an intelligent fault diagnosis system for such motors is very significant. This paper presents an intelligent fault diagnosis method based on the improved functionality of a Convolutional Neural Network (CNN) through its hyperparameter optimization using a nature‐inspired Artificial Bee Colony Optimization (ABCO) algorithm. The proposed diagnostic method introduces and analyses various possible mechanical and electrical faults in the IM. The validation of the proposed method is presented with three different modalities, including vibration, acoustic, and infrared thermography, with their comparative performance analysis. Vibration and acoustic‐based detection are done with time‐frequency scalograms using Constant Q Transform (CQT), which provides enhanced time resolution for lower and higher frequencies. The obtained result indicates that the infrared thermography‐based anomaly detection outperforms the vibration and acoustic‐based diagnosis with 100% classification accuracy. The results signify the potential to diagnose different mechanical and electrical faults in IM with substantial reliability and robustness. [ABSTRACT FROM AUTHOR]

This work is dedicated to theoretical and experimental research on constructing a digital twin for a 24‐story building located in a seismic zone with the possibility of seismic vibrations of up to eight on the Richter scale. A digital model of the building was developed considering the changes in its geometry (damage or absence of structural elements). This requires detailed field inspections of partially constructed buildings and consideration of the inspection results when constructing a digital model. The boundary conditions, in addition to the geometric characteristics of the building, include the physical and mechanical characteristics of all its structural elements obtained through nondestructive testing methods. Discrepancies between the structural elements' design and actual concrete classes were identified. The initial conditions included real accelerograms at the location of the building, which served as input data for the direct dynamic analysis. Thus, there is a need to develop a hybrid system for modeling the stress–strain state of a 24‐story building that uses information technologies (Internet of Things) with sensors during research. Such an IoT system, combined with one information network using cloud technologies, allows us to obtain the necessary information in real‐time, highlighting the features of the building (the second stage of constructing the digital twin). The bar and spatial models of buildings with distributed parameters of structural stiffness at bending and shear and the mass and mass moment of inertia were considered. The results of numerical studies of the first form of natural vibrations for the building rod (according to the expressions obtained by the authors) and spatial models (performed using the LIRA‐CAD software package developed in Ukraine based on the finite element method) are presented. A comparison with the first experimental form of natural vibrations confirmed that considering the moment of inertia of the building mass makes it possible to determine the ordinates of the self‐form of vibrations and seismic horizontal loads more accurately. The experimental first frequency and form of natural vibrations of a 24‐story residential building in Odesa were obtained from the results of vibration monitoring at the construction stage (16 floors were erected) and after all 24‐floor erection. The direct dynamic method of building calculations for seismic effects specified by earthquake accelerograms was used along with the method proposed by the authors to consider damping during forced building vibration calculations and the actual physical and mechanical characteristics of concrete and reinforcement obtained by non‐destructive testing methods. The calculated vibration amplitudes at the top of the building cantilever model were compared with the damping parameters obtained using the Voicht model and the authors' method under the influence of an accelerogram recorded during the powerful 1977 Bucharest earthquake with an epicenter in the Vrancea area (Romanian Carpathians). [ABSTRACT FROM AUTHOR]

An attempt has been made to develop a non-intrusive and robust technique for online detection of roping in hydrocyclones using commercially available vibration-based sensors. A 50.8 mm diameter hydrocyclone was operated with two different silica slurry size distributions till roping was achieved in the underflow. Vibration signals were captured simultaneously at the spigot region for various operating conditions. Empirical Mode Decomposition (EMD) was applied to the raw vibration signal for extracting Intrinsic Mode Function (IMF) within the frequency range of the natural frequency of the hydrocyclone. Subsequently, Power Spectral Density (PSD) and frequency spectrum were computed along the axial direction. The Grms (square root of the area under PSD) and peak frequency values were found to be different for the spray and rope discharge which were used for detecting roping conditions. The differences were considered to be associated with the changes in the axial velocity fluctuations of the air core at the spigot region. Further, a flowchart has been proposed based on the methodology suggested in this work, which has the potential for online detection of roping in the hydrocyclone. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Blasting vibration in roadway excavation blasting is an important factor affecting the stability of road-ways and filling bodies. Frequent blasting vibration can cause damage to the surrounding rock of roadways and filling bodies, ultimately leading to instability and failure. By establishing a blasting vibration monitoring system and improving blasting schemes and monitoring positions, the attenuation law of blasting vibration velocity in the rock mass and filling body is studied. The differences in vibration attenuation laws between rock mass and filling body are obtained, thereby optimizing the blasting charge of Jiaojia Gold Mine. The results show that the closer to the blast source, the more pronounced the blasting vibration velocity, which gradually decreases with increasing distance from the blast center. The maximum vibration velocity occurs near the blast source, and blasting in the stope causes greater disturbance and damage to the filling body. The average vibration velocity in the filling body at a distance equal to the explosion point is 0.223 cm/s, greater than the average vibration velocity inside the rock of 0.087 cm/s. Based on the characteristics of blasting vibration velocity propagation in Jiaojia Gold Mine, combined with GB 6722-2023 Blasting Vibration Safety Regulations, the corresponding relationship between the maximum charge for a single section and the safe distance is derived, and a new dynamic stress ratio method is proposed. This not only provides a basis for optimizing rock blasting design and blasting charge optimization design but also provides a reliable guarantee for the safe and efficient mining of deep-seated ore deposits in Jiaojia Gold Mine. [ABSTRACT FROM AUTHOR]

Due to the capability of multiplexing thousands of sensors on a single optical cable, ultra-weak fiber Bragg grating (UWFBG) vibration sensing technology has been utilized in monitoring the vibration response of large-scale infrastructures, particularly urban railway tracks, and the volume of the collected monitoring data can be huge with the great number of sensors. Even though the train-induced vibration responses of urban railway tracks constitute the most informative and crucial component, they comprised less than 7% of the total operational period. This is mainly attributed to the temporal sparsity of commuting trains. Consequently, the majority of the stored data consisted of low-informative environmental noise and interference excitation data, leading to an inefficient structural health monitoring (SHM) system. To address this issue, this paper introduced an adaptive monitoring strategy for railway track structures, which is capable of identifying train-load patterns by leveraging deep learning techniques. Inspired by image semantic segmentation, a U-net model with one-dimensional convolution layers (U-net-1D) was developed for the pointwise classification of vibration monitoring data. The proposed model was trained and validated using a dataset obtained from an actual urban railway track in China. Results indicated that the proposed method outperforms the traditional dual-threshold method, achieving an Intersection over Union (IoU) of 94.27% on the segmentation task of the test dataset. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of metro construction, shield machines inevitably have to traverse a variety of complex geological conditions, leading to the frequent occurrence of geological disasters, equipment failures, building vibration and other problems. Vibration, as an important feature of the shield tunneling process, has received more and more attention in recent years. This paper summarizes the relevant research progress of vibration monitoring during shield construction from 2015 to 2023. It analyzes the shield vibration generation mechanism, monitoring methods and application areas. Firstly, the shield vibration type is divided into mechanical vibration triggered by internal excitation and forced vibration triggered by external excitation, and the principles of vibration generated by shield main bearing, gearbox and disc cutter are discussed. Then, the commonly used vibration monitoring methods are outlined according to the installation location of the sensors (inside and outside of the shield). Finally, the applications of vibration signals in the diagnosis of shield faults, the identification of geologic conditions, and the evaluation of the current status of the interference with the buildings are summarized. This paper discusses the development trend of vibration monitoring during shield tunneling based on the current research situation and the current technology level, which provides valuable insights to enhance the safety and intelligence of shield construction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mechanical equipment is ubiquitous in industrial production, and the vibration energy generated by its operation usually cannot be effectively harvested, resulting in huge energy waste. Meanwhile, real‐time monitoring of machine operation can be achieved by collecting vibration information. Indeed, vibration energy harvesting and vibration monitoring are of great significance for the development of green energy and machine fault diagnosis. As an emerging power generation technology, triboelectric nanogenerator (TENG) has shown extraordinary potential in the field of vibration energy harvesting and vibration monitoring. First, the theoretical basis, working modes, and triboelectric materials are described. Then, TENG devices for vibration energy harvesting are classified and introduced based on the structural characteristics, and the main advantages and disadvantages are compared. Furthermore, the current research progress of a self‐powered vibration monitoring system based on TENG is introduced. Finally, the shortcomings of triboelectric nanogenerators in this field are analyzed and summarized, and future research directions and application scenarios are prospected. [ABSTRACT FROM AUTHOR]

Rolling bearings are essential components in a wide range of equipment, such as aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete system failure, and it accounts for approximately 45 %–50 % of failures in rotating machinery. Hence, it is imperative to establish a thorough and accurate predictive maintenance program that can efficiently foresee and prevent mishaps or malfunctions. The literature has employed a variety of techniques and approaches, from conventional methods to contemporary machine learning (ML) and ML-integrated IoT-based solutions, to categorise bearing faults. This article provides an overview of the most recent research and models used in the classification of bearing faults. The literature summary highlights various significant challenges in current models, such as issues with the classification function, complexities in the neural network structure, unrealistic datasets, dynamic working conditions of rotating machines, noise in the dataset, limited data availability, and imbalanced datasets. In order to tackle the problems, researchers have endeavored to improve and apply different methods, such as convolutional neural networks, deep belief neural networks, and LiNet, among others. Researchers have primarily developed these approaches using datasets from publicly accessible sources. This study also identified research gaps and deficiencies, including limited data availability, data imbalance, and difficulties in data integration. The nascent technologies in the field of problem diagnosis and predictive maintenance are acknowledged as Internet of Things-based ML and vision-based deep learning techniques, which are currently in their initial phases of advancement. Ultimately, the study puts forth several prospective suggestions and recommendations.

In the treatment of dentofacial deformities in oral and maxillofacial surgery, a maxilla-mandibular bone is cut using a rotary cutting tool. Generally, an oral and maxillofacial surgeon determines the end of cutting with a cutting bar by sensory change transmitted from the cortical bone(hard layer) to the cancellous bone (soft layer) in the hand of the surgeon. It is required the extensive experience and advanced surgical skills to avoid a risk of sequelae due to excessive damage to blood vessels and nerves caused by excessive cut. Therefore, it is important to establish an objective and quantitative index for the cancellous bone reaching in order to perform the procedure safely. The purpose of this study is to construct an evaluation index for judging the accession of the cutting bar at the cancellous bone. This study focuses on the density of the frequency spectral component of the vibration acceleration of the handpiece and defines the change in the density of the spectral component as a new evaluation index. The accuracy of the proposed evaluation index was evaluated by the artificial bone cutting tests simulating the oral and maxillofacial surgery. In addition, the effects of proficiency in the surgery on the judgment results were examined.

Experimental monitoring of blade vibration in turbomachinery is typically based on blade-mounted strain gauges. Their signals are used to derive vibration amplitudes which are compared to modal scope limits, including a safety factor. According to industrial guidelines, this factor is chosen conservatively to ensure safe operation of the machine. Within the experimental campaign with the open-test-case composite fan ECL5/CATANA, which is representative for modern lightweight Ultra High Bypass Ratio (UHBR) architectures, measurements close to the stability limit have been conducted. Investigation of phenomena like non-synchronous vibrations (NSV) and rotating stall require a close approach to the stability limit and hence demand for accurate (real-time) quantification of vibration amplitudes to ensure secure operation without exhaustive safety margins. Historically, short-time Fourier transforms of vibration sensors are used, but the complex nature of the mentioned coupled phenomena has an influence on amplitude accuracy, depending on evaluation parameters, as presented in a previous study using fast-response wall-pressure transducers. The present study investigates the sensitivity of blade vibration data to evaluation parameters for different spectral analysis methods and provides guidelines for fast and robust surveillance of critical vibration modes. [ABSTRACT FROM AUTHOR]

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.

This work explores the potential of self-powered MEMS devices for application in the preventive conservation of cultural heritage. The main objective is to evaluate the effectiveness of piezoelectric aluminum nitride MEMS (AlN-MEMS) for monitoring vibrations and to investigate its potential for harvesting energy from vibrations, including those induced by visitors. A preliminary laboratory comparison was conducted between AlN-MEMS and the commercial device Tromino®. The study was then extended to the Picture Gallery of Ursino Castle, where joint measurements with the two devices were carried out. The analysis focused on identifying natural frequencies and vibrational energy levels by key metrics, including spectral peaks and the Power Spectral Density (PSD). The results indicated that the response of the AlN-MEMS aligned well with the data collected by the commercial device, especially observing high vibrational energy around 100 Hz. Such results validate the potential of AlN-MEMS for effective vibration measurement and for converting kinetic energy into electrical power, thereby eliminating the need for external power sources. Additionally, the vibrational analysis highlighted specific locations, such as the measurement point Cu4, as exhibiting the highest vibrational energy levels. These points could be used for placing MEMS sensors to ensure efficient vibration monitoring and energy harvesting.

An attempt has been made to develop a non-intrusive and robust technique for online process monitoring of hydrocyclones using commercially available vibration sensors. Vibration signals were captured first at the spigots of 50.8 mm and 76.2 mm diameter hydrocyclones while using water only at different operating conditions. For denoising the captured raw vibration signals, a wavelet-based method coupled with median filtering-based technique was used. The Root Mean Square (RMS) values of vibration accelerations induced in the hydrocyclone were found to be proportional to the total volumetric feed rate. Based on this observation, it has been demonstrated how suitable empirical correlations can be developed which can ultimately be used as an online process monitoring tool for hydrocyclones. [ABSTRACT FROM AUTHOR]

Bearings play a crucial role in the functionality of rotating machinery, and any defects in these components can result in machine failure. Detecting, diagnosing, and prognosing bearing faults are crucial steps in machine failure diagnostics to prevent malfunctions and breakdowns. While various methods exist for fault detection, including acoustic emission analysis, visual inspection, thermography, ultrasonic, motor current analysis, wear-debris analysis, oil analysis, and vibration analysis, the latter stands out as a popular non-destructive method. This paper focuses on time and frequency domain vibration analysis techniques for detecting faults in floating bush bearings. Particularly beneficial for online monitoring, remote and non-human intervention areas, and hazardous locations, the time-frequency domain approach enhances diagnostic capabilities. The vibration data collected during these experiments has been rigorously analyzed using data acquisition system and applied a comprehensive approach that includes evaluating the data in both the time and frequency domains, as well as utilizing advanced signal processing techniques, notably the high-frequency resonance technique. Test results underscore the effectiveness of specific parameters in identifying defects: waveform, form factor, parameter K, and cepstrum excel in pinpointing external defects, while kurtosis, crest factor, and skewness prove adept at identifying internal faults. In the frequency domain, the enveloped spectrum emerges as a robust method for comprehensive defect detection. The vibration data presented in this paper will prove to be an invaluable resource for professionals engaged in the field of vibration measurement and analysis. [ABSTRACT FROM AUTHOR]

The dynamic compaction mechanism of high-groundwater-level (HGL) foundations is still unclear, which seriously affects the engineering application of dynamic compaction in the riverbed and land reclamation. In this study, a dynamic compaction model test was established to analyze the vibration acceleration of the tamper, the soil surface, and the soil interior under different depths of groundwater level. Meanwhile, the propagation of the vibration wave was also analyzed, and the energy dissipation of dynamic compaction under the HGL foundation was studied combined with the modal damping ratio (ζ). The results showed that HGL resulted in poor foundation improvement. The main effects were the energy consumption and the friction reduction between the particles when the tamper was in contact with the water, which resulted in vibration acceleration and a sharp decrease in crater depth. The crater will not maintain its cylindrical shape, and further tamping will negatively affect the reinforcement. Vibration analysis showed that dynamic compaction results in dense soil with an increased damping ratio, but when the soil is tamped at the HGL foundation, the soil tends to oscillate, and the damping ratio decreases. In terms of energy, more than 70% of energy was dissipated directly under the tamping point of the high-groundwater-level foundation. Dynamic compaction has been extended from land foundation reinforcement to artificial reclamation foundation as an economical and environmentally friendly foundation treatment technique. The groundwater level in the coastal area is usually higher than that on land, which plays a decisive role in the success of dynamic compaction. However, there are few theoretical and experimental studies on the dynamic compaction of high-groundwater-level foundations. This paper analyzed the energy transfer and consumption of the dynamic compaction method for high-groundwater-level foundations from the perspective of vibration waves. The results showed that compared with dry sand, dynamic compaction of high-groundwater-level foundations will obviously consume reinforcement energy. Dynamic compaction causes groundwater levels to rise around the tamping point. The higher the tamping energy level, the more obvious the groundwater level rise. The results can provide a reference for expanding the treatment range of the dynamic compaction method and applying the dynamic compaction method to high groundwater foundations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Monumental buildings are of great importance because they are remainders of culture and history of every country. These structures need special attention, particularly when a new project is going to be constructed close to these buildings. Subways, are among those constructions that generate noticeable vibration during passing of trains. This vibration can have negative effect on historical buildings. This paper aims to develop a new continues monitoring system for gathering and analyzing vibrations from historical building when a subway train passes close to these monuments. The vibrations are then compared with allowable vibration limits. The monitoring system developed here is installed in three valuable monuments located in Isfahan. The data is accessible online in the form of a website (www.imvms.ir). In this website the time-history and frequency content of vibrations are accessible during 24 hours a day. [ABSTRACT FROM AUTHOR]

To meet the monitoring requirements for a certain type of tower-mounted wind turbine blade， a structural monitoring method based on a fiber Bragg grating （FBG） sensing network is proposed. A threedimensional model of the blade is established and finite element simulation is conducted to obtain the strain distribution under operational conditions. The design of FBG packaging and layout scheme enables real-time monitoring of strain values and their variations at each sensing point. Through fast Fourier transform analysis of the vibration characteristics， the influence of environmental factors such as temperature and wind speed on the reliability of the monitoring system is investigated. The results indicate that the strain-vibration testing method based on FBG can effectively monitor the blade’s response to wind pressure and vibration frequency， with an error range within 0.04 Hz， thus meeting the requirements for practical engineering applications. [ABSTRACT FROM AUTHOR]