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"Vibration monitoring"
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A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing
This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that represent the degradation condition of slew bearing rotating at very low speed (≈ 1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algorithms in a wide range of applications such as vibration analysis, time series analysis and bio-medical signal processing, is discussed. Some features are applied in vibration slew bearing data acquired from laboratory tests. The selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure.
Journal Article
Research on mechanical vibration monitoring based on wireless sensor network and sparse Bayes
Mechanical vibration monitoring for rotating mechanical equipment can improve the safety and reliability of the equipment. The traditional wired monitoring technology faces problems such as high-frequency signal pickup and high-precision data collection. Therefore, this paper proposes optimization techniques for mechanical vibration monitoring and signal processing based on wireless sensor networks. First, the hardware design uses high-performance STM32 as the control center and Si4463 as the wireless transceiver core. The monitoring node uses a high-precision MEMS acceleration sensor with a 16-bit resolution ADC acquisition chip to achieve high-frequency, high-precision acquisition of vibration signals. Then, the bearing vibration signal optimization method is studied, and the sparse Bayes algorithm is proposed as a compressed sensing reconstruction algorithm. Finally, the difference in reconstruction accuracy between this method and the traditional reconstruction algorithm is compared through experiments and the effect of this method on the reconstruction performance is analyzed when different parameters are selected.
Journal Article
Experimental and numerical investigation of the effect of blast-induced vibration from adjacent tunnel on existing tunnel
Since the new tunnel is close to existing tunnel, the vibration wave induced by blasting endangers the safety and stabilization of the surrounding rock and the lining of existing tunnel. In the tunnel blasting vibration monitoring and safety prediction, Peak Particle Velocity (PPV) and vibration frequency are used widely as safety standards. To investigate the effect of blast-induced vibration from adjacent tunnel on existing tunnel, field monitoring experiments and a numerical method that is Finite Element Method (FEM) were adopted to study the blasting vibration velocity and vibration frequency of existing tunnel. Combined blasting vibration velocity with vibration frequency, the paper studied axial and radial blasting vibration velocity distributions and the corresponding Power Spectral Density (PSD) distributions of the existing tunnel under the effect of blast-induced vibration from adjacent subway tunnel. And the parameters of constitutive model and blasting loads were also discussed. It is shown that field monitoring experiment and numerical simulation can optimize blasting excavation program and provide a reference for other similar engineering projects.
Journal Article
Implementation of a Condition Monitoring Strategy for the Monastery of Salzedas, Portugal: Challenges and Optimisation
The implementation of condition monitoring for damage identification and the generation of a reliable digital twin are essential elements of preventive conservation. The application of this promising approach to Cultural Heritage (CH) sites is deemed truly beneficial, constituting a minimally invasive mitigation strategy and a cost-effective decision-making tool. In this light, the present work focuses on establishing an informative virtual model as a platform for the conservation of the monastery of Santa Maria de Salzedas, a CH building located in the north of Portugal. The platform is the first step towards the generation of the digital twin and is populated with existing documentation as well as new information collected within the scope of an inspection and diagnosis programme. At this stage, the virtual model encompasses the main cloister, whose structural condition and safety raised concerns in the past and required the implementation of urgent remedial measures. In the definition of a vibration-based condition monitoring strategy for the south wing of the cloister, five modes were identified by carrying out an extensive dynamic identification. Nonetheless, significant challenges emerged due to the low amplitude of the ambient-induced vibrations and the intrusiveness of the activities. To this end, a data-driven Optimal Sensor Placement (OSP) approach was followed, testing and comparing five heuristic methods to define a good trade-off between the number of sensors and the quality of the collected information. The results showed that these algorithms for OSP allow the selection of sensor locations with good signal strength.
Journal Article
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method.
Journal Article
Optimal Positioning of Vibration Monitoring Instruments and their Impact on Blast-Induced Seismic Influence Results
The major downside of blasting works is blast vibrations. Extensive research has been done on the subject and many predictors, estimating Peak Particle Velocity (PPV), were published till date. However, they are either site specific or global (unified model regardless of geology) and can give more of a guideline than exact data to use. Moreover, the model itself among other factors highly depends on positioning of vibration monitoring instruments. When fitting of experimental data with best fit curve and 95% confidence line, the equation is valid only for the scaled distance (SD) range used for fitting. Extrapolation outside of this range gives erroneous results. Therefore, using the specific prediction model, to predetermine optimal positioning of vibration monitoring instruments has been verified to be crucial. The results show that vibration monitoring instruments positioned at a predetermined distance from the source of the blast give more reliable data for further calculations than those positioned outside of a calculated range. This paper gives recommendation for vibration monitoring instruments positioning during test blast on any new site, to optimize charge weight per delay for future blasting works without increasing possibility of damaging surrounding structures.
Journal Article
Real-Time Monitoring of Concrete Vibration Depth Based on RFID Scales
The vibration of concrete is a typical concealed construction process, in which mature supervisory methods are lacking. The quality of vibration relies heavily on the subjective experience and sense of responsibility of the vibration operators. For the widely used hand-held concrete vibrators, existing methods for monitoring the quality of vibration primarily focus on the horizontal positioning of the vibrator. Due to the limited measurable range of vibration depth, these methods are inapplicable for monitoring the vibration depth during the vibration of deeper structures such as walls, columns, and large volumes of concrete. This paper makes the initial attempt to address the issue of monitoring concrete vibration depth, presenting a method that broadens the measurable range of depth in vibration monitoring. Inspired by the principles of optical and magnetic scales, this paper introduces a radio frequency identification (RFID) scales positioning system for the real-time monitoring of vibration depth. The proposed RFID scales vibration depth monitoring method theoretically has no upper limit on the measurable vibration depth, rendering it applicable to monitoring vibration depth of any extent. By comparing the positioning accuracy of different RFID scales hardware compositions, the optimal RFID scales hardware composition and the most effective RFID scales positioning algorithm were identified. The feasibility and accuracy of the vibration depth monitoring method based on RFID scales were validated through engineering field application. This method achieves centimeter-level accuracy in monitoring vibration depth, offers a tool for the precise control of vibration depth, and helps avoid potential quality issues in vibration.
Journal Article
Long-Term Dynamic Monitoring of Post-Tensioning External Tendons: Temperature Effect Evaluation
Cables and tendons are crucial elements in bridge engineering but also are vulnerable structural elements because they are usually subjected to fatigue and corrosion problems. Thus, vibration-based non-destructive techniques have been used for external post-tensioning tendon assessment. Regarding continuous monitoring systems, tendon assessment is carried out through the continuous tracking of its natural frequencies and the subsequent estimation of the tension force, as this parameter is essential for the bridge’s overall structural performance, thus providing useful information about bridge safety. However, for long-term monitoring assessment, two main challenges have to be addressed regarding practical applications: (i) double-peak spectra and other spurious factors that affect the frequency estimation, and (ii) temperature dependency, which needs to be carefully treated since frequency/tension variation may be explained by temperature variation, thus masking potential structural anomalies. On this subject, this paper presents the experimental long-term monitoring of several post-tensioning external tendons in a high-speed railway bridge in which a sectorized weighted peak-picking frequency identification procedure is proposed for frequency estimation, alongside a cascade clustering process, which allows meaningful frequency estimates to be selected. Finally, the selected frequency estimates, which show variations from 1 to 2% for all analyzed frequencies, are used for the long-term assessment of the tension force.
Journal Article
Vibration-Based Monitoring and Diagnostics of Complicated Rotary-Type Machinery Using a Nonstandard Approach
The results of assessment for the current technical condition of one of the complicated rotary-type machines with the use of a technology based on nonstandard methods of vibration monitoring and machine diagnostics developed at the Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN) are presented. Using the developed technology based on data obtained under perennial rotor unit operation, incipient defects have been revealed, and the process of changes in the unit vibration condition have been traced using
S
-discriminants. In addition, the time dependences of spectral amplitudes have been constructed to identify developing defects by calculating the stochastic interrelation between the time dependences of different vibration parameters, and an assessment of the current technical condition for such a unique machine as a moving neutron reflector has been carried out.
Journal Article