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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]

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]

This work aims to investigate the friction and vibration behaviors of a liquid–solid two-phase flow supported bearing-rotor system for the early monitoring and assessing of oil contamination resulting from externally ingested particles. Based on a Jeffcott rotor test rig, the lubricating oil in journal bearing was mixed with micron-scale silica particles of different sizes and concentrations. The vibration monitoring techniques utilizing eddy current, photoelectric, and piezoelectric sensors were conducted to detect the rotor displacement amplitude and bearing acceleration online. According to the quantitative evaluation, the vibration frequency spectrum, shaft trajectory, and the bearing surface characteristics were analyzed and classified at different conditions of particle parameters. Furthermore, the lubricating mechanism at bearing interface which possesses a strong interactive coupling effect with rotor vibration was deduced and illustrated. The results showed that although the sizes of ingested micron particles were less than the minimum oil-film thickness, they usually exhibited detrimental effects on the flow stability and fluid support. As the particle size or concentration increased, the interaction between particles and bearing surfaces led to the burrs in time-domain waveform, reverse displacements in rotor orbit, higher frequency harmonics in vibration spectrum, and the scratches on the bearing surface. The occurrence of continuous three-body friction could induce misalignment, blockage, oil starvation, and rub-impact fault. Furthermore, the detectability of particle contamination and damage microstructure of bearing surface was described for reference. [ABSTRACT FROM AUTHOR]

Various online techniques for performance and condition monitoring of hydrocyclones have been proposed during the past few decades. However, in the recent years, the subject has gained tremendous popularity as the depleting ore grades demand optimized performance of hydrocyclones in any mineral processing plant. Therefore, in this work, an attempt has been made to critically review the promising online techniques under five different categories. It is found that vibration-based technique appears to be the most attractive proposition from application point of view because of the several inherent advantages associated with it. [ABSTRACT FROM AUTHOR]

Among the main fault locations of wind turbines, downtime accidents caused by bearing faults account for the majority. Therefore, it is of practical significance to detect the fault signal of the fan bearing as early as possible, and to arrange the maintenance plan reasonably, which is of practical significance to reduce the maintenance cost and increase the power generation time. Therefore, this paper proposes an early fault vibration detection and analysis model for wind power transmission based on deep neural networks and sensors. First of all, this paper studies the failure mechanism, vibration mechanism and signal characteristics of common failures of rolling bearings, combined with sensor technology, summarises and designs various bearing failures and failure signal detection models. Secondly, the deep neural network algorithm is used for the interference situation of rolling bearing vibration signal and the process of data re-analysis. The experimental and simulation results verify the adaptability of the early fault vibration detection model of wind power transmission in the early fault processing results, which can provide a reference for the early weak fault signal processing of wind turbine bearings. [ABSTRACT FROM AUTHOR]

The Palace of Westminster is a Grade 1 listed building and a designated UNESCO World Heritage Site that is deteriorating faster than it can be repaired, increasing the risk of sudden and catastrophic failure. In 2018 the Restoration and Renewal (R&R) Programme was established to transform the Houses of Parliament and safeguard their future as the working home of British parliamentary democracy. It is a multifaceted and challenging undertaking, with key considerations for the architectural fabric and parliamentary heritage required to ensure it is fit for purpose in the twenty-first century. This paper will explore the impact conservation has had on planning, risk mitigation and project management by discussing how to raise heritage awareness and the complex approach to governance required due to the nature of the R&R Programme. It will focus on building information modelling, Palace surveys and sustainability considerations as specific case studies of conservation deliverables, whilst also showcasing the utilisation of innovative and cross-disciplinary techniques that have allowed the re-imagining of traditional construction methodologies. [ABSTRACT FROM AUTHOR]

Vibration generated by use of masonry equipment can be a threat to wall paintings. Its assessment, mitigation and control is of critical importance to risk management and the safeguarding of works of art, particularly those which are immovable. The planned programme of window restoration in the Royal Gallery, Palace of Westminster, started in 2016, directly above the monumental wall painting of Trafalgar, had the potential to cause serious damage to this painting. For a period of 18 months the Curator's Office worked with stakeholders, conservation scientists, conservators and stone masons to minimise risk. The project developed a monitoring strategy, set vibration thresholds, managed risk and negotiated the use of tools and working methods. To assess vibration data loggers equipped with 3-axis acceleration sensors were installed at critical locations. To corroborate these findings assessment was undertaken using a sound level analyser. This assessed acceleration at a series of frequencies in the 0–100 Hz range considered most damaging to artworks. However, a critical component was conservator-led in situ monitoring of vibration. When necessary, this live monitoring allowed conservators to dynamically assess and negotiate the adaptation of tools and techniques to reduce levels. This real-time approach proved essential in understanding peaks and changes in levels of vibration; informing discussion between stakeholders, ensuring monitoring translated into effective preservation. [ABSTRACT FROM AUTHOR]

Although vision-based methods for displacement and vibration monitoring have been used in civil engineering for more than a decade, most of these techniques require physical targets attached to the structures. This requirement makes computer vision-based monitoring for real-life structures cumbersome due to need to access certain critical locations. In this study, a non-target computer vision-based method for displacement and vibration measurement is proposed by exploring a new type of virtual markers instead of physical targets. The key points of measurement positions obtained using a robust computer vision technique named scale-invariant feature transform show a potential ability to take the place of classical targets. To calculate the converting ratio between pixel-based displacement and engineering unit (millimetre), a practical camera calibration method is developed to convert pixel-based displacements to engineering unit since a calibration standard (a target) is not available. Methods and approaches to handle challenges such as low contrast, changing illumination and outliers in matching key points are also presented. The proposed method is verified and demonstrated on the UCF four-span bridge model and on a real-life structure, with excellent results for both static and dynamic behaviour of the two structures. Finally, the method requires a simple, less complicated and more cost-effective hardware compared to conventional displacement and vibration monitoring measuring technologies. [ABSTRACT FROM PUBLISHER]

Worldwide urbanization has caused heritage buildings to be in ever-closer proximity to heavily travelled roadways and railways. As an example of this, the minaret of Little Hagia Sophia was analyzed due to its historic prominence and close proximity to heavy rail traffic. A vibration measurement program was performed on and around the minaret. After material properties were determined, three-dimensional numerical model of the minaret was constructed and validated based on frequency analyses of measured vibrations and modal analysis. Self-weight analysis and linear time-history analyses were applied to the model using ground motions measured at the minaret’s base. The results showed that stresses reached 32% of material strength and, critically, measured peak particle velocities of vibrations at times approached previously established damage thresholds. Therefore, construction and operation of traffic lines adjacent to heritage buildings in city centers should be analyzed carefully to prevent unintentional impacts on cultural heritage structures. [ABSTRACT FROM AUTHOR]

The first part of this article presents the feasibility study of a new structure for a 10-m-span bridge deck, taking into account the possibilities offered by new and high-strength materials and the advantages of a traditional environmental-friendly material. This 10-m-span element is formed by wooden beams braced at the ends, on supports atop a thin slab (7 cm thick) made of pre-cast ultra-high performance fiber-reinforced concrete, and fiber-reinforced polymer at the bottom of the wooden beams. The issue of connecting the ultra-high performance fiber-reinforced concrete to the wooden beams of high but possibly mismatching performance is addressed. An adhesive connection has been considered, following an increasing trend for composite structures where materials savings and optimal performance are searched in every component. The second part of this article presents the vibration-based damage monitoring. Small localized damages are hardly detected by global monitoring methods. The effectiveness of vibration-based detection depends on the accuracy of the modal parameter estimates and is limited by the low sensitivity of the modal parameters to a local stiffness reduction. A local reduction of stiffness related to frequency changes less than 1% was successfully detected on the bridge mockup after loading up to the serviceability limit state (SLS). Such a small decrease in the stiffness was not detected by the monitoring of the static load-deflection measurements but was confirmed by nonlinear local strain measurements. Statistical subspace-based damage detection successfully detected the change of the modal parameters of the investigated structure. Further analysis with a finite element model was conducted for assessing the consistency of the expected location and extent of the damaged elements. [ABSTRACT FROM PUBLISHER]

When monitoring complex engineering systems, sensors often measure mixtures of signals that are unique to individual components (component signals). However, isolating component signals directly from sensor signals can be a challenge. As an example, in vibration monitoring of a rotating machine, if different components generate vibration signals at similar frequencies, they cannot be distinguished using traditional spectrum analysis (non-inseparable). However, developing degradation signals from component signals is important to monitor the deterioration of crucial components and to predict their residual lifetimes. This article proposes a simultaneous signal separation and prognostics framework for multi-component systems with non-inseparable component signals. In the signal separation stage, an Independent Component Analysis (ICA) algorithm is used to isolate component signals from mixed sensor signals, and an online amplitude recovery procedure is used to recover amplitude information that is lost after applying the ICA. In the prognostics stage, an adaptive prognostics method to model component degradation signals as continuous stochastic processes is used to predict the residual lifetimes of individual components. A case study is presented that investigates the performance of the signal separation stage and that of the final residual-life prediction under different conditions. The simulation results show a reasonable robustness of the methodology. [ABSTRACT FROM PUBLISHER]

This work aims to develop an adaptive network-based fuzzy inference system (ANFIS) for surface roughness and vibration prediction in cylindrical grinding. The system uses a piezoelectric accelerometer to generate a signal related to grinding features and surface roughness. To accomplish such a goal, an experimental study was carried out and consisted of 27 runs in a cylindrical grinding machine operating with an aluminium oxide grinding wheel and AISI 8620 steel workpiece. The workpiece speed, feed rate and depth of cut were used as an input to ANFIS, which in turn outputs surface roughness (Ra) and vibration (az ). Different neuro-fuzzy parameters were adopted during the training process of the system in order to improve online monitoring and prediction. Experimental validation runs were conducted to compare the measured surface roughness values with the values predicted online. The comparison shows that the gauss-shaped membership function achieved an online prediction accuracy of 99%. [ABSTRACT FROM PUBLISHER]