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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]

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]

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]

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]

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]

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]

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]

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.

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]

The paper concerns the wide range of strategies used to protect structures against man-made dynamic excitation. The most popular approaches applied worldwide are compared, and the main differences and similarities are summarized. The literature studies are supported by the results of the measurements performed on different types of real structures, which are sensitive and insensitive to the dynamic load. To make the conclusions more general, various types of excitation forces are examined (long-term and short-term excitations, traffic load, and loads resulting from geotechnical works). The main issue raised in the paper is the problem of unequivocal and accurate assessment of the potential structure damage, based on the different legislations. It can be seen that the application of different codes can even result in opposite conclusions about the safety of the structure.

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.

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.

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.

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.

The monitoring of displacement and strain distribution of vibrating structures is very important for maintaining structural safety. In this paper, a monitoring method of structural vibration deformation based on Fiber Bragg Grating (FBG) strainmeter array and accelerometer array is proposed for two different cases of whether the structure surface can be directly pasted with FBGs. In addition, a dynamic deformation estimation based on strain and acceleration data fusion, and a vibration morphology monitoring algorithm based on displacement reconstruction combined with finite element simulation results are proposed, respectively. An FBG accelerometer is designed based on an equal strength beam, for which the geometric parameters are determined by finite element simulation. The test results show that the accelerometer has a stable operating frequency band of 3-20Hz, an average sensitivity of 70pm/g, and a resonant frequency of 56Hz, which provides excellent linearity and consistency. The vibration deformation monitoring experiments were carried out on a cantilever aluminum plate. The results indicate that the measuring system is able to accurately sense the shape change of the structure in real-time with a displacement measurement error is < 6%. For the aluminum plate structure without FBG on the surface, the measuring system successfully reconstructed the displacement and strain distribution with a strain measurement error is < 10%.

Vibration deformation of a reflector antenna under a moving load will reduce pointing performance. Current vibration suppression technology for reflector antennas, such as the application of mesh reflector antennas, the construction of large radomes, the use of antennas with high stiffness, and the improvement of the shaft servo systems, can reduce the impact of vibrations. However, the disadvantages of high-frequency transmission, high cost, increased weight, and servo bandwidth limitations, are unavoidable. A cable actuation system is proposed as a method for reducing the vibration of reflector antennas while having little influence on the original structure. In this method, the acceleration of the reflector is measured, and an unbiased minimum variance estimator of the system state is introduced to observe the vibration state of the reflector. Then using the LQR approach, drivers on the slide rail pull the cables to suppress the vibration of the reflector. Moreover, the position of the drivers on the slide rail can be adjusted depending on the moving load condition. Finally, the experimental results show that the vibration time of the reflector without control was approximately 7 s, while the vibration time of the reflector with control was reduced to approximately 1 s when the pendulum fell from the same height to excite the reflector. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

As the spherical hinge in the bridge swivel structure bears huge vertical pressure, the material and its structural load-bearing capacity are therefore highly-required. In the latest research, the ultrahigh performance concrete material is applied to the spherical hinge structure and the author of this article has conducted a detailed study on the mechanical properties and failure mechanism of this structure; however, there is still no real bridge application at present. In order to ensure the stability of the structure, based on an actual project, this research proposes a monitoring method for the stability of the UHPC spherical hinge horizontal rotation system, i.e., using theoretical calculations and numerical analysis methods. Besides, the mechanical characteristics of the bridge during the process of rotation are predicted, and the monitoring data of the stress of the UHPC spherical hinge, the bending moment of the pier bottom, as well as the acceleration time history of the cantilever beam end are made a comparison to judge whether the rotating posture of the structure is stable. The results show that UHPC spherical hinge features high strength and will not cause axial damage; also, the horizontal rotation system will not cause the unstability due to wind-induced vibration and structural self-excited vibration. Briefly concluded, the theoretical model is basically consistent with the measured data, i.e., the mechanical properties of the structure can be accurately predicted.

Wind energy is one of the most relevant clean energies today, so wind turbines must have good health and be reliable in operation. Current wind turbines have slender and elastic structures that can be easily damaged through vibrations and compromise their health; therefore, vibration monitoring is essential to ensure safe operation. Here, we present a method for simple wind turbine vibration monitoring in the laboratory by means of an accelerometer placed on a weathervane under different scenarios, with recording of different amplitudes of vibrations caused at a constant speed of 10 km/h. The variables, trends, and data captured during vibration monitoring were then used to implement a prediction system of synthetic failure using machine learning methods such as: Medium Trees, Cubic SVN, Logistic Regression Kernel, Optimized Neural Network, and Bagged Trees, with the last demonstrating an accuracy of up to 0.87%. [ABSTRACT FROM AUTHOR]

The stability of the Great Wall is mainly affected by traffic vibrations and natural hazards, such as strong winds, heavy rainfall, and thunderstorms, which are extremely harmful to the safety of the Great Wall. To determine the impact of the above factors on the Great Wall, a comparative analysis based on MEMS (micro-electro-mechanical system) accelerometer data was conducted between the non-impacts and the impacts of the above factors. An analysis of the relationship between vibration acceleration and each potential hazard based on a visual time series chart was presented using the data of accelerometers, traffic video, meteorology, rainfall, and wind. According to the results, traffic vibration is one of the primary dangerous factors affecting the stability of the Great Wall, Moreover, the intensity of the vibrations increases with the traffic flow. Thunderstorms also influence the stability of the Great Wall, with enhanced thunderstorm excitation resulting in increased vibration displacement. Furthermore, wind load is an influencing factor, with average wind speeds greater than 9 m/s significantly affecting the stability of the Great Wall. Rainfall has no impact on the stability of the Great Wall in the short term. This research can provide important guidance for risk assessment and protection of the Great Wall. [ABSTRACT FROM AUTHOR]

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. [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.

The development of an effective hydraulic unit (HU) diagnostic system, which recognizes critical fault at an early stage of their development, is a relevant and practically important objective for many hydroelectric power plants (HPP). The main trends in recent years in this area are the expansion of the hardware base and the introduction of digital technologies to handle the increasing flow of data monitoring. At the same time, much less attention is paid to the improvement of algorithms for potential defect recognition and the creation of new diagnostic rules; much of the monitoring data are used only for the trend construction, and do not participate in the diagnosis. The method of vector diagrams is proposed for estimating actual forces applied towards the support joints of an HU: each mode of operation is represented on a phase plane in the form of a vector proportional to the vibration displacement. The vector difference determines the actual forces of mechanical, electromagnetic and hydraulic origin in each operating mode, taking into account the individual characteristics of the HU. The example of vector diagram analysis shows how to obtain more useful information about the technical status of the unit from the same amount of basic vibration monitoring data, to predict possible deterioration in the operation of the equipment well before the generation of a warning or an alarm signal by the diagnostic system.

Embedded smart bearing is a kind of structural unit with the characteristics of self sensing, self diagnosis, self-adaptive and self-regulation, which is one of the development directions of high-end bearing at home and abroad. Based on Ioannides-Harris theory, the load distribution of rolling bearing is analyzed. Combined with the finite element simulation model, the influence of outer ring grooving on bearing reliability is analyzed, and the radial grooving structure of outer ring of rolling bearing is determined. According to the theoretical analysis and numerical simulation, the embedded bearing vibration monitoring system is designed by combining the vibration sensor with the bearing body. By using the bearing life testing machine, the bearing life test is carried out and the vibration signals of smart bearings with different structures are collected. The experimental results show that the amplitude of vibration signal of smart bearing is higher than that of traditional bearing pedestal monitoring method, and it can effectively monitor the running state of the bearing; compared with the structure of axial grooving, the service life of the proposed smart bearing is similar to that of the ordinary bearing, which provides a new method for the condition monitoring and fault diagnosis of the rolling bearing.

Here we propose a novel approach to image magnification that enables frame-by-frame motion magnification application at a video streaming rate of around 30 fps. This novel approach can be used instead of batch processing an image file in conventional phase-based magnification (PBM). This new PBM method can instantly show a magnified video streaming on display at 30 fps, which is helpful for vibration measurement and for monitoring tasks where magnified images need to be viewed simultaneously. To accomplish video streaming-rate magnification, the proposed PBM uses time-domain convolution in temporal bandpass filtering for frame-by-frame operation whereas conventional PBM employs a frequency-domain filter that is applied to the entire image file at once. An experiment was conducted to monitor the vibration of a cantilever using a webcam streaming at the same frame rate, and data were collected simultaneously using a laser Doppler vibrometer for comparison. The experiment confirmed that the proposed PBM approach is more effective than the conventional magnification method and it also analyzed the system performance for vibration measurement. Additionally, the proper orthogonal mode could be found through the singular value decomposition from vibration displacement data of the cantilever that was collected instantly from the magnified image frames. Furthermore, the dominant mode could be effectively extracted from excitation at the resonance frequency. Because of magnification factors, the vibration displacements from the proposed method were estimated using linear regression and the accuracy of the estimated displacements was within the permissible error bound. Since the proposed PBM is a frame-by-frame operation, instantaneous adjustment of magnification parameters is available, even while the magnification is being processed. In addition, the proposed PBM is independently adjustable to the number of image frames for temporal FIR filter order.

In recent years, the ENEA has introduced a novel methodology based on motion magnification (MM) into the Italian cultural heritage protection and monitoring field. It consists of a digital video signal processing technique able to amplify enormously the tiny movements recorded in conventional videos, while preserving the general topology of the acquired frames. Though the idea of such a methodology is not new, it has recently been provided with an efficient algorithm that makes possible a viable and low-cost magnification. Applications are extremely varied in almost every field of science and technology; however, we are interested in its application to the safeguarding of architectural heritage, a sector of the utmost importance for Italy. As ancient buildings can be extremely sensitive to even minimally invasive instrumentation, most common monitoring sensors can be replaced by contactless tools and methods, such as video-based techniques like MM. It offers many advantages: easy to use, contactless devices, virtual sensors, reusability of the videos, practicality, intuitive graphical results, quantitative analyses capability and low costs. These characteristics are well suited to the monitoring of large ancient monuments; on the other hand, historical sites have peculiarities of their own, requiring careful approaches, proper tools and trained personnel. Moreover, outdoor applications of MM present quite notable difficulties from a practical point of view, e.g., the dimensions of the studied objects, uncontrolled environmental conditions, spurious vibrations, lighting change/instability, etc. Here we give a general idea of the potential of MM and related issues, using some relevant in-the-field case studies in Italian heritage protection. [ABSTRACT FROM AUTHOR]

In this paper, an IoT sensor node, based on smart Bluetooth low energy (BLE), for the health monitoring of artworks and large wooden structures is presented. The measurements from sensors on board the node are collected in real-time and sent to a remote gateway. The sensor node allows for the monitoring of environmental parameters, in particular, temperature and humidity, with accurate and robust integrated sensors. The developed node also embeds an accelerometer, which also allows other mechanical quantities (such as tilt) to be derived. This feature can be exploited to perform structural monitoring, exploiting the processing of data history to detect permanent displacements or deformations. The node is triggered by acceleration transients; therefore, it can also generate alarms related to shocks. This feature is crucial, for instance, in the case of transportation. The developed device is low-cost and has very good performance in terms of power consumption and compactness. A reliability assessment showed excellent durability, and experimental tests proved very satisfactory robustness against working condition variations. The presented results confirm that the developed device allows for the realization of pervasive monitoring systems, in the context of the IoT paradigm, with sensor nodes devoted to the monitoring of each artwork present in a museum or in a church. [ABSTRACT FROM AUTHOR]

We propose the adaptation of vibration-based condition monitoring systems and techniques, popularly used in industrial condition-based maintenance, for identifying the possibility of compromising the privacy of personal computing systems. This work exploits the automated fan-based heat dissipation features and read/write operations of disk-based storage, commonly present in personal computers, to read computing task-specific vibration signatures on the computer’s cabinet/case. These vibration signatures are then used to identify the broad classes of tasks being executed on a separate computer without ever needing to log into the monitored machine. This work builds upon the premise that heterogeneous tasks have distinct computing requirements, which translates to variations in the amount of heat generated by the computer’s processor, eventually leading to variations in the computer’s heat control fan speed. The variations in the fan’s speed and the frequency of read/write operations to disk-based storage create unique vibration signatures, which maps uniquely to the computer’s processing operations, leading to a breach of privacy of the computer. Our work’s preliminary results suggest that computer-based tasks can be mapped from their vibration signatures with an accuracy of at least $70\%$ 70 % . We additionally study the task identification granularity of such an approach. [ABSTRACT FROM AUTHOR]

In this study, we developed a real-time vibration visualization system that can estimate and display vibration distributions at all frequencies in real time through parallel implementation of subpixel digital image correlation (DIC) computations with short-time Fourier transforms on a GPU-based high-speed vision platform. To help operators intuitively monitor high-speed motion, we introduced a two-step framework of high-speed video processing to obtain vibration distributions at hundreds of hertz and video conversion processing for the visualization of vibration distribution at dozens of hertz. The proposed system can estimate the full-field vibration displacements of 1920 × 1080 images in real time at 1000 fps and display their frequency responses in the range of 0–500 Hz on a computer at dozens of frames per second by accelerating phase-only DICs for full-field displacement measurement and video conversion. The effectiveness of this system for real-time vibration monitoring and visualization was demonstrated by conducting experiments on objects vibrating at dozens or hundreds of hertz. [ABSTRACT FROM AUTHOR]

Dynamic deformation measurement is an important approach to monitor structure stability. Due to its rapid imaging capabilities, ground-based multi-input–multi-output (GB-MIMO) radar has shown great application potential in bridge structure monitoring. This article proposes a dynamic deformation estimation method based on radar image series. First, an improved clutter suppression method is used to overcome the estimation error in complex working status. Second, a two-step pixel extraction method is adopted to ensure both the quantity and quality of sample pixels. Finally, frequency- and time-domain environmental stimulating methods are jointly considered for stable mode shape estimation. This article systematically measured the dynamic deformations of bridge structures, including two suspension bridges and a cable-stayed bridge. The time-series deformation, deflection, vibration frequency, and amplitude on the bridge structure are obtained by image-domain measurement. For the first time, the mode shapes of bridges are obtained through GB-MIMO radar. The correctness of structure parameter measurement is verified with the finite element model (FEM). Experimental results prove that with the proposed method, stable results could be acquired against weak and complex stimulation conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Used in many industrial applications, centrifugal pumps have optimal operating criteria specified at design. These criteria may not be precisely adhered to during operation which will ultimately reduce the life of the asset. Operators would therefore benefit from anticipating how often the design point is deviated from and hence how much asset degradation results. For centrifugal pumps, a novel set of covariates were proposed in this paper which formally partition observed operating zones with an Empirical Bivariate Quantile Partitioned distribution. This captured the dependency relation between operating parameters across plant configurations to predict the component wear that results from particular settings. The effectiveness of this was demonstrated through an operational case study in civil nuclear generation feedwater pumps where corroboration with bearing movements provides an indicator of plant wear. Such a technique is envisaged to inform operators of optimal plant configuration from multiple possibilities in advance of undertaking them. [ABSTRACT FROM AUTHOR]

The aim of the work is to create a computational and experimental method for the operational evaluation of the residual life of spur gears of technically complex products in the inter-repair period by conducting periodic vibration control. The proposed method is based on the main provisions of the theory of vibration-pulse diagnostics of gears developed in the Joint Institute of Mechanical Engineering of the National Academy of Sciences of Belarus. At the same time, the current value of the KV coefficient, which takes into account the internal dynamic load in the engagement of each gear, and its change during operation is determined based on the analysis of the time realizations of vibration accelerations. The main operation capacity criteria are the contact and bending strength of the teeth. An example of estimating the residual life of a gear pair is given that limits the reliability of a double-row planetary gear motor-wheel of a heavy-duty dump truck. The proposed method makes it possible at any stage of operation of a gear mechanism to assess the residual life of its gears, identify its most “weak” links, determine the probability of failure of the object, minimize the risk of accidents, optimize maintenance schedules, increase the inter-repair time. Thus, it is possible to organize highly effective monitoring of the reliability-limiting components of the technically complex products during operation and transition from planned preventive maintenance of machines to maintenance according to the actual condition.

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.

Vibration monitoring provides a good-quality source of information about the health condition of machines, and it is often based on the use of accelerometers. This article focuses on the use of accelerometer sensors in fabricating a low-cost system for monitoring vibrations in agricultural machines, such as rotary tedders. The aim of the study is to provide useful data on equipment health for improving the durability of such machinery. The electronic prototype, based on the low-cost AVR microcontroller ATmega128 with 10-bit ADC performing a 12-bit measurement, is able to acquire data from an accelerometer weighing up to 10 g. Three sensors were exposed to low accelerations with the use of an exciter, and their static characteristics were presented. Standard experimental tests were used to evaluate the constructed machine monitoring system. The self-contained prototype system was calibrated in a laboratory test rig, and sinusoidal and multisinusoidal excitations were used. Measurements in time and frequency domains were carried out. The amplitude characteristic of the preformed system differed by no more than 15% within a frequency range of 10 Hz–10 kHz, compared to the AVM4000 commercial product. Finally, the system was experimentally tested to measure acceleration at three characteristic points in a rotational tedder, i.e., the solid grease gearbox, the drive shaft bearing and the main frame. The RMS amplitude values of the shaft vibrations on the bearing in relation to the change in the drive shaft speed of two tedders of the same type were evaluated and compared. Additionally, the parameters of kurtosis and crest factor were compared to ascertain the bearing condition. [ABSTRACT FROM AUTHOR]

The development of an effective hydraulic unit (HU) diagnostic system, which recognizes critical fault at an early stage of their development, is a relevant and practically important objective for many hydroelectric power plants (HPP). The main trends in recent years in this area are the expansion of the hardware base and the introduction of digital technologies to handle the increasing flow of data monitoring. At the same time, much less attention is paid to the improvement of algorithms for potential defect recognition and the creation of new diagnostic rules; much of the monitoring data are used only for the trend construction, and do not participate in the diagnosis. The method of vector diagrams is proposed for estimating actual forces applied towards the support joints of an HU: each mode of operation is represented on a phase plane in the form of a vector proportional to the vibration displacement. The vector difference determines the actual forces of mechanical, electromagnetic and hydraulic origin in each operating mode, taking into account the individual characteristics of the HU. The example of vector diagram analysis shows how to obtain more useful information about the technical status of the unit from the same amount of basic vibration monitoring data, to predict possible deterioration in the operation of the equipment well before the generation of a warning or an alarm signal by the diagnostic system. [ABSTRACT FROM AUTHOR]

Gearboxes are frequently used in the mining industry, especially for power transmission between the electric drive and the ball mill; besides the extreme complexity of a ball mill gear transmission system, the fault diagnosis by vibration analysis can be easily distorted by the presence of impulsive noises due to the ball pulses on the mill shell. Although several works in the literature are related to the influence of an impulsive noise on the accuracy of the diagnosis, no dynamic model exists yet in the literature that can explain the influence of these forces on the dynamic behavior of gearboxes. This paper presents a new approach to determine the influence of the grinding forces in crack defects diagnosis. This approach is based on a hybrid numerical model of a 24-degree-of-freedom gearbox, simulating one gear train and two drive shafts. The impact forces of the mill drum are modelled by a discrete element method (DEM). The ball-filling rate ( F r ), the mill speed ( N r ), and the ball size ( D b ) are considered to study this phenomenon. The simulations results show by a time series representation, fast Fourier transform, and short-time Fourier transform (STFT), that the acceleration is significantly affected by the presence of the grinding forces, developing an impulsive noise due to the impact of the balls governed by the studied parameters. [ABSTRACT FROM AUTHOR]

The increase in railway coverage significantly increased the railway mileage in karst areas. However, the fragile ecosystem in karst areas and weak mechanical performance in cavities cause hidden railway safety hazards. Even with the crucial stability and safety of railways in karst areas, there is only limited research conducted on the dynamic response of railway subgrade and the influence of hidden soil holes on the stability under different conditions. In this study, the Guizhou section of the Shanghai–Kunming high-speed railway was chosen as the research area. On-site monitoring and simulations using ABAQUS were performed to study the dynamic response of the subgrade under the dynamic load of a train. The influence of the soil hole, axle load, and vehicle speed on the dynamic response law and stability of the subgrade were considered based on the model rationality verification. Previous studies have demonstrated the considerable impact of soil holes on the dynamic response characteristics and stability of roadbeds. The ambient vibration frequency of the train was concentrated at 10–60 Hz. Under train load, the amplitude of the dynamic response parameters of each subgrade structural layer increased with the axle load; however, the increase in speed exhibited a greater impact on the vertical dynamic displacement and dynamic stress than on the dynamic acceleration, which affected the stability of the soil hole and roadbed. This study summarized the dynamic response characteristics of subgrade in karst areas under different conditions, thereby providing a design reference for high-speed railways in karst areas.

For modern engineering infrastructures, the vibration information is critical in identifying the dynamic characteristics and assessing the structural health. Over the past few decades, Global Navigation Satellite System (GNSS) has played an increasingly important role in the monitoring of surface dynamic movement. The GNSS-based precise point positioning (PPP) has received much attention due to its technical feasibility and economic efficiency. More efforts are expected to be made in the research of high-rate PPP and the integration of multiple GNSSs for structural health monitoring. This study evaluates the performance of GPS/BDS kinematic PPP with 20-Hz observations in capturing vibrations generated from a linear shaker. A comparative analysis of the solutions obtained from GPS/BDS high-rate PPP, real-time kinematic (RTK) positioning, total station and accelerometer is performed in both frequency and displacement domains. The experimental results show that the displacement time series captured by GPS/BDS PPP and RTK can achieve an accuracy of 5.7–8.4 and 4.5–6.6 mm with respect to those from total station, respectively. The PPP-derived vibration frequency is consistent with that from RTK, total station and accelerometer, with a deviation less than 0.008 Hz among each other. For comparison, the GPS-only and BDS-only PPP results are also provided, and it is indicated that the dual-system integration can enhance the reliability and accuracy of monitoring results. For completeness, another experiment is conducted to investigate the performance difference between ambiguity-float and ambiguity-fixed PPP for vibration monitoring. [ABSTRACT FROM AUTHOR]