Showing papers in "Measurement Science and Technology in 2017"
TL;DR: In this paper, a review of the literature and the commercial tools for insitu monitoring of powder bed fusion (PBF) processes is presented, focusing on the development of automated defect detection rules and the study of process control strategies.
Abstract: Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications currently involve industrial sectors (e.g., aerospace and bio-medical) where defects avoidance is fundamental. Because of this, there is the need to develop novel in-situ monitoring tools able to keep under control the stability of the process on a layer-by-layer basis, and to detect the onset of defects as soon as possible. On the one hand, AM systems must be equipped with in-situ sensing devices able to measure relevant quantities during the process, a.k.a. process signatures. On the other hand, in-process data analytics and statistical monitoring techniques are required to detect and localize the defects in an automated way. This paper reviews the literature and the commercial tools for insitu monitoring of Powder Bed Fusion (PBF) processes. It explores the different categories of defects and their main causes, the most relevant process signatures and the in-situ sensing approaches proposed so far. Particular attention is devoted to the development of automated defect detection rules and the study of process control strategies, which represent two critical fields for the development of future smart PBF systems.
505 citations
TL;DR: Schlieren and shadowgraph techniques are used around the world for imaging and measuring phenomena in transparent media as mentioned in this paper, and although it might seem that little new could be expected of them on the timescale of 15 years, in fact several important things have happened that are reviewed here.
Abstract: Schlieren and shadowgraph techniques are used around the world for imaging and measuring phenomena in transparent media. These optical methods originated long ago in parallel with telescopes and microscopes, and although it might seem that little new could be expected of them on the timescale of 15 years, in fact several important things have happened that are reviewed here. The digital revolution has had a transformative effect, replacing clumsy photographic film methods with excellent—though expensive—high-speed video cameras, making digital correlation and processing of shadow and schlieren images routine, and providing an entirely-new synthetic schlieren technique that has attracted a lot of attention: background-oriented schlieren or BOS. Several aspects of modern schlieren and shadowgraphy depend upon laptop-scale computer processing of images using an imagecapable language such as MATLABTM. BOS, shock-wave tracking, schlieren velocimetry, synthetic streak-schlieren, and straightforward quantitative density measurements in 2D flows are all recent developments empowered by this digital and computational capability.
230 citations
TL;DR: In this paper, the authors present a survey of the recent progress in the research and development of metal oxide gas sensor technology, including the significance of n-type/p-type switching, the enhancement of sensing performance of materials through the incorporation of secondary components and the advantages of interrogating sensors with alternating current rather than direct current.
Abstract: Since the first suggestion, during the 1950s, that high-surface-area metal oxides could be used as conductometric gas sensors enormous efforts have been made to enhance both the selectivity and the sensitivity of such devices, and to reduce their operational power requirements. This development has involved the exploration of response mechanisms, the selection of the most appropriate oxide compositions, the fabrication of two-phase ‘hetero-structures’, the addition of metallic catalyst particles and the optimisation of the manner in which the materials are presented to the gas—the structure and the nanostructure of the sensing elements. Far more of the scientific literature has been devoted to seeking such improvements in metal oxide gas sensors than has been directed at all other solid-state gas sensors together. Recent progress in the research and development of metal oxide gas sensor technology is surveyed in this invited review. The advances that have been made are quite spectacular and the results of individual pieces of work are drawn together here so that trends can be seen. Emerging features include: the significance of n-type/p-type switching, the enhancement of sensing performance of materials through the incorporation of secondary components and the advantages of interrogating sensors with alternating current rather than direct current.
206 citations
169 citations
TL;DR: A novel method called adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis, and the analysis results confirm that the proposed method has more accurate and robust performance than other intelligent methods.
Abstract: The working condition of rolling bearing usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of the dependence on manual feature design, the deep CNN model is initialized. Secondly, to adapt to different signal characteristics, the main parameters of deep CNN model are determined with particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to analyze the rolling bearing vibration signals collected from an experimental setup and electrical locomotive, and the analysis results confirm that the proposed method has more accurate and robust performance than other intelligent methods.
140 citations
TL;DR: In this article, the authors used high-resolution tomographies of two very different granular materials (spherical and very angular) as ground truth to study the metrology of detecting interparticle contacts and measuring their orientation.
Abstract: In the mechanics of granular materials, interparticle contacts play a major role. These have been historically difficult to study experimentally, but the advent of x-ray microtomography allows the identification of all the thousands of individual particles needed for representative mechanical testing. This paper studies the metrology of detecting interparticle contacts and measuring their orientation from such images. Using synthetic images of spheres and high-resolution tomographies of two very different granular materials (spherical and very angular) as ground truths, we find that these measurements are far from trivial. For example, if a physically correct threshold is used to separate particles from pores there is a systematic over-detection of contacts. We propose a method of improvement that is effective for non-angular particles. When contact orientations are measured from the pixels that make up the contact area, standard watershed approaches make significant systematic errors. We confirm and build upon previous results showing the improvement in orientation measurement using a refined notion of particle separation. Building on this solid basis, future work should focus on a link between contact topology and measurement error, as well as evaluating the use of local surface normals for orientation measurement.
83 citations
TL;DR: This work reports for the first time the use of convolutional neural networks (CNNs) and fully connected neural networks for performing end-to-end PIV and presents tests on real-world data that prove ANNs can be used not only with synthetic images but also with more noisy, imperfect images obtained in a real experimental setup.
Abstract: Traditional programs based on feature engineering are underperforming on a steadily increasing number of tasks compared with artificial neural networks (ANNs), in particular for image analysis. Image analysis is widely used in fluid mechanics when performing particle image velocimetry (PIV) and particle tracking velocimetry (PTV), and therefore it is natural to test the ability of ANNs to perform such tasks. We report for the first time the use of convolutional neural networks (CNNs) and fully connected neural networks (FCNNs) for performing end-to-end PIV. Realistic synthetic images are used for training the networks and several synthetic test cases are used to assess the quality of each network's predictions and compare them with state-of-the-art PIV software. In addition, we present tests on real-world data that prove ANNs can be used not only with synthetic images but also with more noisy, imperfect images obtained in a real experimental setup. While the ANNs we present have slightly higher root mean square error than state-of-the-art cross-correlation methods, they perform better near edges and allow for higher spatial resolution than such methods. In addition, it is likely that one could with further work develop ANNs which perform better that the proof-of-concept we offer.
83 citations
TL;DR: In this paper, the differences between measurements made using various optical and non-optical technologies, including confocal and focus-variation microscopy, coherence scanning interferometry and x-ray computed tomography, are examined.
Abstract: The challenges of measuring the surface topography of metallic surfaces produced by additive manufacturing are investigated. The differences between measurements made using various optical and non-optical technologies, including confocal and focus-variation microscopy, coherence scanning interferometry and x-ray computed tomography, are examined. As opposed to concentrating on differences which may arise through computing surface texture parameters from measured topography datasets, a comparative analysis is performed focussing on investigation of the quality of the topographic reconstruction of a series of surface features. The investigation is carried out by considering the typical surface features of a metal powder-bed fusion process: weld tracks, weld ripples, attached particles and surface recesses. Results show that no single measurement technology provides a completely reliable rendition of the topographic features that characterise the metal powder-bed fusion process. However, through analysis of measurement discrepancies, light can be shed on where instruments are more susceptible to error, and why differences between measurements occur. The results presented in this work increase the understanding of the behaviour and performance of areal topography measurement, and thus promote the development of improved surface characterisation pipelines.
82 citations
TL;DR: There is a rich—yet underexplored—research landscape for the practical applications of MIT, and the aim of this review is to provide a non-exhaustive overview of this landscape.
Abstract: Magnetic induction tomography (MIT) is a tomographic technique capable of imaging the passive electromagnetic properties of an object. It has the advantages of being contact-less and non-invasive, as the process involves interrogating the electromagnetic field of the imaging subject. As such, the potential applications of MIT are broad, with various domains of operation including biomedicine, industrial process tomography and non-destructive evaluation. Consequently, there is a rich—yet underexplored—research landscape for the practical applications of MIT. The aim of this review is to provide a non-exhaustive overview of this landscape. The fundamental principles of MIT are discussed, alongside the instrumentation and techniques necessary to obtain and interpret MIT measurements.
78 citations
73 citations
TL;DR: In this article, the authors summarise different approaches used in wide-field TCSPC detection, and discuss their merits for different applications, with emphasis on fluorescence lifetime imaging.
Abstract: Time-correlated single photon counting (TCSPC) is a widely used, robust and mature technique to measure the photon arrival time in applications such as fluorescence spectroscopy and microscopy, LIDAR and optical tomography. In the past few years there have been significant developments with wide-field TCSPC detectors, which can record the position as well as the arrival time of the photon simultaneously. In this review, we summarise different approaches used in wide-field TCSPC detection, and discuss their merits for different applications, with emphasis on fluorescence lifetime imaging.
TL;DR: In this paper, the authors developed a theory of single-beam (or parallel two-beam) magnetometers and showed that it is possible to achieve good sensitivity beyond their usual frequency range by tuning the magnetic field.
Abstract: Here, optically pumped magnetometers (OPM) can be used in various applications, from magnetoencephalography to magnetic resonance imaging and nuclear quadrupole resonance (NQR). OPMs provide high sensitivity and have the significant advantage of non-cryogenic operation. To date, many magnetometers have been demonstrated with sensitivity close to 1 fT, but most devices are not commercialized. Most recently, QuSpin developed a model of OPM that is low cost, high sensitivity, and convenient for users, which operates in a single-beam configuration. Here we developed a theory of single-beam (or parallel two-beam) magnetometers and showed that it is possible to achieve good sensitivity beyond their usual frequency range by tuning the magnetic field. Experimentally we have tested and optimized a QuSpin OPM for operation in the frequency range from DC to 1.7 kHz, and found that the performance was only slightly inferior despite the expected decrease due to deviation from the spin-exchange relaxation-free regime.
TL;DR: The VESUVIO spectrometer at the ISIS pulsed neutron and muon source is a unique instrument amongst those available at neutron facilities as discussed by the authors, accessing values of energy and wavevector transfer above tens of eV and ${\mathring{\rm A}}-1}$, respectively, and where deep inelastic neutron scattering experiments are routinely performed.
Abstract: The VESUVIO spectrometer at the ISIS pulsed neutron and muon source is a unique instrument amongst those available at neutron facilities. This is the only inverted-geometry neutron spectrometer accessing values of energy and wavevector transfer above tens of eV and ${\mathring{\rm A}}^{-1}$ , respectively, and where deep inelastic neutron scattering experiments are routinely performed. As such, the procedure at the base of the technique has been previously described in an article published by this journal (Mayers and Reiter 2012 Meas. Sci. Technol. 23 045902). The instrument has recently witnessed an upsurge of interest due to a new trend to accommodate, within a single experiment, neutron diffraction and transmission measurements in addition to deep inelastic neutron scattering. This work presents a broader description of the instrument following these recent developments. In particular, we assess the absolute intensity and two-dimensional profile of the incident neutron beam and the capabilities of the backscattering diffraction banks. All results are discussed in the light of recent changes to the moderator viewed by the instrument. We find that VESUVIO has to be considered a high-resolution diffractometer as much as other diffractometers at ISIS, with a resolution as high as $2\times 10^{-3}$ in backscattering. Also, we describe the extension of the wavelength range of the instrument to include lower neutron energies for diffraction measurements, an upgrade that could be readily applied to other neutron instruments as well.
TL;DR: Results of this numerical simulation show the sparsity and concentration of the VTFR are better than those of short-time Fourier transform, continuous wavelet transform, Hilbert–Huang transform and Wigner–Ville distribution techniques.
Abstract: Rolling element bearings are one of the main elements in rotating machines, whose failure may lead to a fatal breakdown and significant economic losses. Conventional vibration-based diagnostic methods are based on the stationary assumption, thus they are not applicable to the diagnosis of bearings working under varying speeds. This constraint limits the bearing diagnosis to the industrial application significantly. A hybrid approach to fault diagnosis of roller bearings under variable speed conditions is proposed in this work, based on computed order tracking (COT) and variational mode decomposition (VMD)-based time frequency representation (VTFR). COT is utilized to resample the non-stationary vibration signal in the angular domain, while VMD is used to decompose the resampled signal into a number of band-limited intrinsic mode functions (BLIMFs). A VTFR is then constructed based on the estimated instantaneous frequency and instantaneous amplitude of each BLIMF. Moreover, the Gini index and time-frequency kurtosis are both proposed to quantitatively measure the sparsity and concentration measurement of time-frequency representation, respectively. The effectiveness of the VTFR for extracting nonlinear components has been verified by a bat signal. Results of this numerical simulation also show the sparsity and concentration of the VTFR are better than those of short-time Fourier transform, continuous wavelet transform, Hilbert–Huang transform and Wigner–Ville distribution techniques. Several experimental results have further demonstrated that the proposed method can well detect bearing faults under variable speed conditions.
TL;DR: The results indicate that successful instantaneous precise RTK positioning is feasible while using L1 GPS and E1 Galileo data, and that the SF 4-system model is competitive to DF GPS even when residual ionospheric delays are present.
Abstract: With the combination of the emerging GNSSs, single-frequency (SF) precise RTK positioning becomes possible. In this contribution we evaluate such low-cost ublox receiver and antenna performance when combining real data of four CDMA systems, namely L1 GPS, E1 Galileo, L1 QZSS, and B1 BDS. Comparisons are made to more expensive dual-frequency (DF) GPS receivers and antennas. The formal and empirical ambiguity success rates and positioning precisions will first be evaluated while making use of L1 + E1, so as to investigate whether instantaneous SF RTK is possible without the need of B1 BDS or L1 QZSS. This follows by an analysis of the SF 4-system model performance when the residual ionosphere can be ignored and modeled as a function of the baseline length, respectively. The analyses are conducted for a location in Dunedin, New Zealand, and compared to Perth, Australia with the better visibility of BDS and QZSS. The results indicate that successful instantaneous precise RTK positioning is feasible while using L1 GPS and E1 Galileo data, and that the SF 4-system model is competitive to DF GPS even when residual ionospheric delays are present. We finally demonstrate that when the impact from the ionosphere increases and more than one epoch is needed for successful ambiguity resolution, the SF 4-system model performance can still remain competitive to the DF GPS receivers. This is particularly true in Perth with more satellites and when higher than customary elevation cut-off angles need to be used to avoid low-elevation multipath.
TL;DR: In this article, a portable wear debris sensor with ferrite cores for online machine health monitoring is presented, which is capable of detecting wear debris in real time with a high throughput of 750 ml min−1; the measured debris concentration is in good agreement with the actual concentration.
Abstract: Detecting wear debris and measuring the increasing number of wear debris in lubrication oil can indicate abnormal machine wear well ahead of machine failure, and thus are indispensable for online machine health monitoring. A portable wear debris sensor with ferrite cores for online monitoring is presented. The sensor detects wear debris by measuring the inductance change of two planar coils wound around a pair of ferrite cores that make the magnetic flux denser and more uniform in the sensing channel, thereby improving the sensitivity of the sensor. Static testing results showed this wear debris sensor is capable of detecting 11 µm and 50 µm ferrous debris in 1 mm and 7 mm diameter fluidic pipes, respectively; such a high sensitivity has not been achieved before. Furthermore, a synchronized sampling method was also applied to reduce the data size and realize real-time data processing. Dynamic testing results demonstrated that the sensor is capable of detecting wear debris in real time with a high throughput of 750 ml min−1; the measured debris concentration is in good agreement with the actual concentration.
TL;DR: A signal selection scheme for fault-emphasized diagnostics based upon two order tracking techniques is proposed and proves to be effective for planetary gearbox fault diagnosis.
Abstract: The planetary gearbox, due to its unique mechanical structures, is an important rotating machine for transmission systems. Its engineering applications are often in non-stationary operational conditions, such as helicopters, wind energy systems, etc. The unique physical structures and working conditions make the vibrations measured from planetary gearboxes exhibit a complex time-varying modulation and therefore yield complicated spectral structures. As a result, traditional signal processing methods, such as Fourier analysis, and the selection of characteristic fault frequencies for diagnosis face serious challenges. To overcome this drawback, this paper proposes a signal selection scheme for fault-emphasized diagnostics based upon two order tracking techniques. The basic procedures for the proposed scheme are as follows. (1) Computed order tracking is applied to reveal the order contents and identify the order(s) of interest. (2) Vold–Kalman filter order tracking is used to extract the order(s) of interest—these filtered order(s) constitute the so-called selected vibrations. (3) Time domain statistic indicators are applied to the selected vibrations for faulty information-emphasized diagnostics. The proposed scheme is explained and demonstrated in a signal simulation model and experimental studies and the method proves to be effective for planetary gearbox fault diagnosis.
TL;DR: In this paper, an optimized ensemble local mean decomposition (OELMD) method is proposed to determine an optimum set of ELMD parameters for vibration signal analysis, where an error index termed the relative root-mean-square error (Relative RMSE) is used to evaluate the decomposition performance with a certain amplitude of the added white noise.
Abstract: Mechanical transmission systems have been widely adopted in most of industrial applications, and issues related to the maintenance of these systems have attracted considerable attention in the past few decades. The recently developed ensemble local mean decomposition (ELMD) method shows satisfactory performance in fault detection of mechanical components for preventing catastrophic failures and reducing maintenance costs. However, the performance of ELMD often heavily depends on proper selection of its model parameters. To this end, this paper proposes an optimized ensemble local mean decomposition (OELMD) method to determinate an optimum set of ELMD parameters for vibration signal analysis. In OELMD, an error index termed the relative root-mean-square error (Relative RMSE) is used to evaluate the decomposition performance of ELMD with a certain amplitude of the added white noise. Once a maximum Relative RMSE, corresponding to an optimal noise amplitude, is determined, OELMD then identifies optimal noise bandwidth and ensemble number based on the Relative RMSE and signal-to-noise ratio (SNR), respectively. Thus, all three critical parameters of ELMD (i.e. noise amplitude and bandwidth, and ensemble number) are optimized by OELMD. The effectiveness of OELMD was evaluated using experimental vibration signals measured from three different mechanical components (i.e. the rolling bearing, gear and diesel engine) under faulty operation conditions.
TL;DR: The aim of the presented work is to propose a characterization method of erythrocyte-derived EV using atomic force microscopy (AFM), minimizing external sources of uncertainty on the shape and size of the tip, thus allowing standardization of EV measurement.
Abstract: Extracellular vesicles (EV) are small biological entities released from cells into body fluids. EV are recognized as mediators in intercellular communication and influence important physiological processes. It has been shown that the concentration and composition of EV in body fluids may differ from healthy subjects to patients suffering from particular disease. So, EV have gained a strong scientific and clinical interest as potential biomarkers for diagnosis and prognosis of disease. Due to their small size, accurate detection and characterization of EV remain challenging. The aim of the presented work is to propose a characterization method of erythrocyte-derived EV using atomic force microscopy (AFM). The vesicles are immobilized on anti-CD235a-modified mica and analyzed by AFM under buffer liquid and dry conditions. EV detected under both conditions show very similar sizes namely similar to 30 nm high and similar to 90 nm wide. The size of these vesicles remains stable over drying time as long as 7 d at room temperature. Since the detected vesicles are not spherical, EV are characterized by their height and diameter, and not only by the height as is usually done for spherical nanoparticles. In order to obtain an accurate measurement of EV diameters, the geometry of the AFM tip was evaluated to account for the lateral broadening artifact inherent to AFM measurements. To do so, spherical polystyrene (PS) nanobeads and EV were concomitantly deposited on the same mica substrate and simultaneously measured by AFM under dry conditions. By applying this procedure, direct calibration of the AFM tip could be performed together with EV characterization under identical experimental conditions minimizing external sources of uncertainty on the shape and size of the tip, thus allowing standardization of EV measurement
TL;DR: An adaptive sparse deconvolution (ASD) method is proposed to overcome limitations in handling guided wave signals, and shows better performance in handling the echo overlap problem in the guided wave signal.
Abstract: In guided wave pipeline inspection, echoes reflected from closely spaced reflectors generally overlap, meaning useful information is lost. To solve the overlapping problem, sparse deconvolution methods have been developed in the past decade. However, conventional sparse deconvolution methods have limitations in handling guided wave signals, because the input signal is directly used as the prototype of the convolution matrix, without considering the waveform change caused by the dispersion properties of the guided wave. In this paper, an adaptive sparse deconvolution (ASD) method is proposed to overcome these limitations. First, the Gaussian echo model is employed to adaptively estimate the column prototype of the convolution matrix instead of directly using the input signal as the prototype. Then, the convolution matrix is constructed upon the estimated results. Third, the split augmented Lagrangian shrinkage (SALSA) algorithm is introduced to solve the deconvolution problem with high computational efficiency. To verify the effectiveness of the proposed method, guided wave signals obtained from pipeline inspection are investigated numerically and experimentally. Compared to conventional sparse deconvolution methods, e.g. the -norm deconvolution method, the proposed method shows better performance in handling the echo overlap problem in the guided wave signal.
TL;DR: In this paper, the technical characteristics and operating principles of the Nortek Vectrino Profiler and reviews previously reported user experiences are discussed. And a series of experiments are then presented that investigate instrument behaviour and performance, with a particular focus on variations within the profile.
Abstract: This paper compiles the technical characteristics and operating principles of the Nortek Vectrino Profiler and reviews previously reported user experiences. A series of experiments are then presented that investigate instrument behaviour and performance, with a particular focus on variations within the profile. First, controlled tests investigate the sensitivity of acoustic amplitude (and Signal-to-Noise Ratio, SNR) and pulse-to-pulse correlation coefficient, R 2, to seeding concentration and cell geometry. Second, a novel methodology that systematically shifts profiling cells through a single absolute vertical position investigates the sensitivity of mean velocities, SNR and noise to: (a) emitted sound intensity and the presence (or absence) of acoustic seeding; and (b) varying flow rates under ideal acoustic seeding conditions. A new solution is derived to quantify the noise affecting the two perpendicular tristatic systems of the Vectrino Profiler and its contribution to components of the Reynolds stress tensor. Results suggest that for the Vectrino Profiler: 1. optimum acoustic seeding concentrations are ~3000 to 6000 mg L−1; 2. mean velocity magnitudes are biased by variable amounts in proximal cells but are consistently underestimated in distal cells; 3. noise varies parabolically with a minimum around the 'sweet spot', 50 mm below the transceiver; 4. the receiver beams only intersect at the sweet spot and diverge nearer to and further from the transceiver. This divergence significantly reduces the size of the sampled area away from the sweet spot, reducing data quality; 5. the most reliable velocity data will normally be collected in the region between approximately 43 and 61 mm below the transceiver.
TL;DR: A spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotmporal behaviors in a bridge network and demonstrates increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.
Abstract: The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.
TL;DR: The results suggest that in absence of any disparity, the stereo PIV uncertainty prediction method is more sensitive to the planar uncertainty estimates than to the angle uncertainty, although the latter is not negligible for non-zero disparity.
Abstract: Particle image velocimetry (PIV) measurements are subject to multiple elemental error sources and thus estimating overall measurement uncertainty is challenging. Recent advances have led to a posteriori uncertainty estimation methods for planar two-component PIV. However, no complete methodology exists for uncertainty quantification in stereo PIV. In the current work, a comprehensive framework is presented to quantify the uncertainty stemming from stereo registration error and combine it with the underlying planar velocity uncertainties. The disparity in particle locations of the dewarped images is used to estimate the positional uncertainty of the world coordinate system, which is then propagated to the uncertainty in the calibration mapping function coefficients. Next, the calibration uncertainty is combined with the planar uncertainty fields of the individual cameras through an uncertainty propagation equation and uncertainty estimates are obtained for all three velocity components. The methodology was tested with synthetic stereo PIV data for different light sheet thicknesses, with and without registration error, and also validated with an experimental vortex ring case from 2014 PIV challenge. Thorough sensitivity analysis was performed to assess the relative impact of the various parameters to the overall uncertainty. The results suggest that in absence of any disparity, the stereo PIV uncertainty prediction method is more sensitive to the planar uncertainty estimates than to the angle uncertainty, although the latter is not negligible for non-zero disparity. Overall the presented uncertainty quantification framework showed excellent agreement between the error and uncertainty RMS values for both the synthetic and the experimental data and demonstrated reliable uncertainty prediction coverage. This stereo PIV uncertainty quantification framework provides the first comprehensive treatment on the subject and potentially lays foundations applicable to volumetric PIV measurements.
TL;DR: An integrated dimensionality reduction method combining feature selection and feature extraction techniques is proposed to yield a more sensitive and lower dimensional feature set, which not only reduces the computation burden for fault diagnosis but also improves the separability of the samples by integrating the label information.
Abstract: Aiming at improving the accuracy of planetary gearbox fault diagnosis, an integrated scheme based on dimensionality reduction method and deep belief networks (DBNs) is presented in this paper. Firstly, the acquired vibration signals are decomposed into mono-component called intrinsic mode functions (IMFs) through ensemble empirical mode decomposition (EEMD), and then Teager–Kaiser energy operator (TKEO) is used to track the instantaneous amplitude (IA) and instantaneous frequency (IF) of a mono-component amplitude modulation (AM) and frequency modulation (FM) signal. Secondly, a high dimensional feature set is constructed through extracting statistical features from six different signal groups. Then, an integrated dimensionality reduction method combining feature selection and feature extraction techniques is proposed to yield a more sensitive and lower dimensional feature set, which not only reduces the computation burden for fault diagnosis but also improves the separability of the samples by integrating the label information. Further, the low dimensional feature set is fed into DBNs classifier to identify the fault types using the optimal parameters selected by particle swarm optimization algorithm (PSO). Finally, two independent cases study of planetary gearbox fault diagnosis are carried out on test rig, and the results show that the proposed method provides higher accuracy in comparison with the existing methods.
TL;DR: In this article, a Greenough-type stereomicroscope arrangement is firstly applied for this situation by using the two totally separated and coaxial optical paths of the stereo-microscope.
Abstract: Fringe projection profilometry has become a widely used method in 3D shape measurement and 3D data acquisition for the features of flexibility, noncontactness, and high accuracy. By combining fringe projection setup with microscopic optics, the fringe pattern can be projected and imaged within a small area, making it possible for measuring 3D surfaces of micro-components. In this paper, a Greenough-type stereomicroscope arrangement is firstly applied for this situation by using the two totally separated and coaxial optical paths of the stereomicroscope. The calibration framework of the stereomicroscope-based system is proposed, which enables high-accuracy calibration of the optical setup for quantitative measurement with the effect of lens distortion eliminated. In the process of 3D reconstruction, depth information is firstly retrieved through the phase-height relation calibrated by a nonlinear fitting algorithm, and the transverse position can be subsequently obtained by solving the equations derived from the calibrated model of the camera. Experiments of both calibration and measurements are conducted and the results reveal that our system is capable of conducting fully automated 3D measurements with a depth accuracy of approximately 4 μm in a volume of approximately 8(L) mm × 6(W) mm × 3(H) mm.
TL;DR: The integrity monitoring availability was assessed and found to meet the target value where the position errors were bounded by the protection level, which was also less than an alert level, indicating the effectiveness of the proposed approach.
Abstract: Continuous and trustworthy positioning is a critical capability for advanced driver assistance systems (ADAS). To achieve continuous positioning, methods such as global navigation satellite systems real-time kinematic (RTK), Doppler-based positioning, and positioning using low-cost inertial measurement unit (IMU) with car speedometer data are combined in this study. To ensure reliable positioning, the system should have integrity monitoring above a certain level, such as 99%. Achieving this level when combining different types of measurements that have different characteristics and different types of errors is a challenge. In this study, a novel integrity monitoring approach is presented for the proposed integrated system. A threat model of the measurements of the system components is discussed, which includes both the nominal performance and possible fault modes. A new protection level is presented to bound the maximum directional position error. The proposed approach was evaluated through a kinematic test in an urban area in Japan with a focus on horizontal positioning. Test results show that by integrating RTK, Doppler with IMU/speedometer, 100% positioning availability was achieved. The integrity monitoring availability was assessed and found to meet the target value where the position errors were bounded by the protection level, which was also less than an alert level, indicating the effectiveness of the proposed approach.
TL;DR: In this article, an enhanced method based on the "two-spheres" concept and on a new measurement strategy is defined and validated, and a selection of the factors influencing the results are evaluated through experimental and simulation analyses.
Abstract: In recent years, x-ray computed tomography has been successfully applied as an innovative coordinate measurement technology for dimensional metrology. An important characteristic to be evaluated when testing the metrological performances of computed tomography systems is the metrological structural resolution for dimensional measurements, which describes the size of the smallest structure that can still be measured within error limits to be specified. The 'two-spheres' concept allows for the investigation of the metrological structural resolution by using a simple reference standard consisting of two touching spheres with the same nominal diameter. This work is aimed at defining and validating an enhanced method based on the 'two-spheres' concept and on a new measurement strategy. Advantages in using this method are discussed and a selection of the factors influencing the results are evaluated through experimental and simulation analyses.