Smearing Effect Research Articles

Vortex-like kinematic signal, spirals, and beam smearing effect in the HD 142527 disk

Vortices are one of the most promising mechanisms to locally concentrate millimeter dust grains and allow the formation of planetesimals through gravitational collapse. The outer disk around the binary system HD 142527 is known for its large horseshoe structure with azimuthal contrasts of ~3–5 in the gas surface density and of ~50 in the dust. Using 13CO and C18O J = 3–2 transition lines, we detect kinematic deviations to the Keplerian rotation, which are consistent with the presence of a large vortex around the dust crescent, as well as a few spirals in the outer regions of the disk. Comparisons with a vortex model suggest velocity deviations up to 350 m s−1 after deprojection compared to the background Keplerian rotation, as well as an extension of ±40 au radially and ~200° azimuthally, yielding an azimuthal-to-radial aspect ratio of ~5. Another alternative for explaining the vortex-like signal implies artificial velocity deviations generated by beam smearing in association with variations of the gas velocity due to gas pressure gradients at the inner and outer edges of the circumbinary disk. The two scenarios are currently difficult to differentiate and, for this purpose, would probably require the use of multiple lines at a higher spatial resolution. The beam smearing effect, due to the finite spatial resolution of the observations and gradients in the line emission, should be common in observations of protoplanetary disks and may lead to misinterpretations of the gas velocity, in particular around ring-like structures.

Dynamic Process Monitoring Based on Variational Bayesian Canonical Variate Analysis

Fault detection and fault identification are consecutive steps of multivariate statistical process monitoring. In recent years, increasing attention has been paid to process dynamics. For dynamic process modeling, canonical variate analysis (CVA) extracts process dynamics effectively. However, process noises are not well analyzed in traditional CVA and corresponding fault identification methods are less studied. To solve these issues, a variational Bayesian CVA (VBCVA) model is proposed for dynamic process monitoring. Through a probabilistic perspective, the inevitable noises in realistic industrial processes can be captured in the new model. Moreover, the proposed model is further extended in the variational Bayesian framework to overcome the common problems in probabilistic methods. Besides, an improved fault identification approach based on fault relevance is introduced, which avoids the smearing effect caused by data reconstruction. Finally, the feasibility of the proposed process monitoring scheme is verified on the TE benchmark and a real wastewater treatment process.

Non-uniformity calibration method of space-borne area CCD for directional polarimetric camera

The directional polarimetric camera (DPC) is a polarization sensor with the characteristics of ultra-wide-angle and low-distortion imaging. The multi-angle polarization information is helpful to obtain the spatial distribution of target radiation, and multiple data fusion relies on the non-uniformity calibration of image plane. The non-uniformity consists of many factors such as lens, detector assembly, spatial stray light, etc. The single correction method can not distinguish the error source effectively. In consideration of the in-flight operation mode of DPC based on the adjustment of exposure time, the non-uniformity correction method of the detector based on multi parameters is proposed. Through the electro-optical performance measurement system of the CCD detector, the sensitive factors such as temperature, dark current, exposure time and spectral response are obtained. After a series of preprocessing of the image including removal of dark signal, removal of smearing effect and temperature compensation, the non-uniformity calibration based on multi-parameters is imposed on the detector. The low-frequency unbalanced response difference of the image surface is eliminated, and the high-frequency difference is effectively suppressed. The experimental results show that the photo response non-uniformity of 95% full well single frame data is reduced from 2.86% to 0.36%. After correction, the data noise is shown as shot noise, and the detector has good ability of dynamic range adjustment. The non-uniformity calibration by the proposed method can offer data support for the instrumental calibration and in-flight fast calculation, and provide effective reference for the subsequent polarization remote sensing instruments.

Analysis of elastic viscoplastic consolidation of sand drain foundations with exponential seepages

Studies have shown that the pore seepage in soft clay deviates from Darcy's law, with the compressibility and permeability of the soil demonstrating obvious nonlinear characteristics during the consolidation process. These factors will affect the sand drain foundation consolidation process. In order to explore the consolidation mechanism of sand drain foundation in saturated clay, this paper introduces the UH model considering the time effect to describe the nonlinear deformation relation of the soil skeleton under the Barron free strain assumption and introduces the exponential seepage equation as an alternative to Darcy's law. Additionally, the impact of the permeability coefficient and the smearing effect is considered which is used to re-derive the conventional sand drain consolidation equation, and then the finite difference method is adopted to give the implicit numerical solutions of the equation. By comparing with literature results, the validity of the method developed in this paper is verified. Then, the effects of the soil nonlinearity, construction disturbance, and external load on the sand drain foundation nonlinear consolidation process are studied as a function of time. The current results reveal that due to the viscous effect of soil, the pore pressure near the undrained boundary of the sand drain foundation during the pre-loading period increases. The above phenomenon is more evident when considering the non-Darcy seepage; meanwhile, the consolidation rate of the sand drain foundation also becomes increasingly slow. Moreover, the decrease of the permeability coefficient in the smear zone can significantly reduce the dissipation rate of the overall pore pressure of the sand drain foundation, while the increase of the external load accelerates foundation consolidation.

Search for intermittent X-ray pulsations from neutron stars in low-mass X-ray binaries

Abstract We present the results of our extensive binary orbital motion corrected pulsation search for 13 low-mass X-ray binaries. These selected sources exhibit burst oscillations in X-rays with frequencies ranging from 45 to 1 122 Hz and have a binary orbital period varying from 2.1 to 18.9 h. We first determined episodes that contain weak pulsations around the burst oscillation frequency by searching all archival Rossi X-ray Timing Explorer data of these sources. Then, we applied Doppler corrections to these pulsation episodes to discard the smearing effect of the binary orbital motion and searched for recovered pulsations at the second stage. Here we report 75 pulsation episodes that contain weak but coherent pulsations around the burst oscillation frequency. Furthermore, we report eight new episodes that show relatively strong pulsations in the binary orbital motion corrected data.

Small-angle neutron scattering geometry with ring-shaped collimation for compact neutron sources

The neutron flux of compact neutron sources is low, making it difficult to measure scattered neutrons with a typical small-angle neutron scattering (SANS) geometry. A SANS geometry with ring-shaped collimation (r-SANS) was thus developed to enable experiments to be conducted using low-flux neutron source facilities. In this geometry, circumferentially collimated beams hit a sample. The scattered neutron flux is high on the ring center axis because scattered neutrons with each nominal scattering angle are superimposed on each point of the ring center axis. A 3He point detector – which has higher neutron counting efficiency compared with that of a typical scintillation detector and can also properly distinguish neutron from gamma events – is set on the ring center axis. These features make high signal-to-noise ratio experiments possible. Monte Carlo (MC) simulation studies were conducted to evaluate the characteristics of the r-SANS geometry. The results show that oblique incident neutrons, which cause cross-talk, asymmetrically smear the scattering intensity, and increase the incident neutrons. To study the feasibility of the r-SANS geometry, it was tested at the Kyoto University Accelerator-driven Neutron Source (KUANS), which is one of the most compact and has one of the lowest neutron fluxes among available neutron beam facilities. Two types of porous silica gels, HC-N and C-500HG, were examined. As reference, the same samples were analyzed using a small-angle X-ray scattering (SAXS) spectrometer under a typical pinhole geometry. To consider the smearing effect of the r-SANS geometry, MC simulations with SAXS experimental results were conducted. The simulated results agree with the r-SANS experimental results. In addition, to show the consistency of the scattering model parameters determined via r-SANS with those via SAXS, a parameter survey was performed, revealing reasonable agreement between the results.

A sequential model-based approach for gas turbine performance diagnostics

The gradual degradation of gas turbine components is an inevitable result of engine operation, impacting engine availability, reliability, and operating cost. Gas path analysis plays an essential role in engine fault diagnosis. Accurate and fast diagnosis of multiple simultaneously degraded components has always posed a challenge, especially when the number of available measurements is limited. This paper proposes a novel performance diagnostic method that partitions the engine diagnosis into a series of steps to remove the “smearing effect” and reduce the matrix dimensions in the iterative diagnostic algorithm. An engine performance model of a triple-shaft gas turbine has been developed and validated against commercial software, in order to assess the accuracy and computational performance of the proposed method. The advantage of the proposed method lies in its capability to detect the severity of engine component degradation, such as compressor fouling and turbine erosion, with greater accuracy and computational efficiency than other model-based methods that use the same number of measurements. The newly developed method provides an accurate diagnosis with a reduced set of measurements. The method can deal effectively with the presence of random noise in the measurements and carries a significantly lower computation burden in comparison to existing methods. The proposed method could be used as a tool for supporting condition monitoring systems for improved gas turbine reliability and energy efficiency.

Subsecond pulsations in hard X-rays of August 19, 1998 flare according to BATSE/CGRO data

Detection of ms-spikes and quasi-periodic subsecond pulsations in the solar flare hard X-rays (HXR) provides useful information about the processes of electron acceleration and transport along the flare magnetic loops. To search for HXR pulsations, we processed the data of BATSE/CGRO spectrometer of high temporal resolution 0.016 s and 0.064 s. BATSE energy range is about 20 keV – 1 MeV. For the powerful solar flare SOL1998:08:19T21:39:21 the FFT and wavelet analysis show the presence of HXR pulsations of ∼ 0.16 – 0.25 s. BATSE data was analyzed for six energy channels 28.8 – 33.1 keV, 37.1 – 41.8 keV, 46.6 – 55.8 keV, 55.8 – 64.6 keV, 64.6 – 74.0 keV, and 74.0 – 99.1 keV. HXR-spikes were detected. The spike shape is practically triangular with FWHM about 100 ms. Simulations of kinetic models of electron transport with short pulses showed that when the duration of an individual spike is longer than ∼ 80 ms, the smearing of pulses practically does not occur. Injection pulses shorter than 30 – 60 ms are not preserved even with a strongly anisotropic distribution of accelerated electrons and a small gradient of the magnetic field. In the isotropic case at the looptop, the decay phase of the X-ray spikes becomes longer compared to the rising phase which is not true for the anisotropic case. The pulse smearing effect is stronger for the softer energy spectra of accelerated electrons.

A multi‐block NMF model for non‐Gaussian process monitoring based on the adaptive partition non‐negative matrix factorization and Bayesian inference

AbstractNon‐negative matrix factorization (NMF) is a novel technique for dimension‐reduction, which can be used to process data of non‐Gaussian and Gaussian efficiently. A global NMF model is inappropriate for the whole process, since it neglects the local information and monitoring results are often hard to be interpreted. On the basis of adaptive partition non‐negative matrix factorization (APNMF) and Bayesian inference, a multi‐block NMF model for non‐Gaussian process monitoring is put forward to detect and isolate the faults effectively. Using APNMF method, the original variables in different fault states can be adaptively divided into multiple sub‐blocks, and on this basis, the NMF monitoring model of each sub‐block is formed. Then, two new statistics are constructed by Bayesian inference to supply an intuitive display. Finally, a weighted reconstruction‐based contribution (RBC) plot method is presented to reduce the smearing effect and find out the main causes of these faults. This method makes full use of the local and global information of process data and improves the effectiveness of process monitoring. The validity and feasibility of the proposed method will be proved by an example of a numerical process, a Tennessee Eastman (TE) benchmark process and a continuous stirred‐tank reactor (CSTR) process.

Temperature-dependent electron-phonon spectral function and the intrinsic resistivity of a metal: A case study of monolayer Ti2N

The transport spectral function of electron-phonon ($e$-ph) interaction in the double $\ensuremath{\delta}$-function approximation (DDFA) is extensively employed to calculate the intrinsic resistivity (arising from $e$-ph scattering) of metallic materials in recent works of first-principles calculations. In contrast, a more fundamental transport spectral function with the Fermi smearing effect due to finite temperature ($T$) and nonzero phonon frequency is less involved. In this work, we perform first-principles calculations of the intrinsic resistivity of ${\mathrm{Ti}}_{2}\mathrm{N}$ monolayer, a potential MXene material, by employing the two kinds of spectral function. We find that the spectral function with the DDFA fails to describe correctly the temperature dependence of the intrinsic resistivity of ${\mathrm{Ti}}_{2}\mathrm{N}$ monolayer at $T>250$ K, much lower than the Debye temperature. The underlying reason is that ${\mathrm{Ti}}_{2}\mathrm{N}$ monolayer has a multisheet Fermi surface formed by several bands, and some band edges are very close to the Fermi surface. Our results suggest that the transport spectral function with the Fermi smearing effect, instead of the one with the DDFA, is always adequate for studying the intrinsic resistivity of realistic materials on the level of first-principles calculations. In addition, we give a brief remark on the intrinsic resistivity of ${\mathrm{Ti}}_{2}\mathrm{N}$ monolayer, in contrast with other typical two-dimensional materials, which is significant from the viewpoint of application of such an MXene material.

Between-class difference analysis based multidimensional RBC for multivariate fault isolation of industrial processes

In industrial process, fault isolation technology will identify the major variables leading to faults. The existing fault isolation methods usually have ”smearing effect”, cannot identify the sensitivity of the fault variables, or require too much calculation. To solve the above problems, a multivariate fault isolation method based on between-class difference analysis and multidimensional reconstruction-based contribution (RBC) is proposed in this work. First, two kind of strategies, i.e.,principle component analysis (PCA) and fisher discriminant analysis (FDA), are adopted to obtain the sensitivity indicators of the variables to the faults respectively, which is taken as the basis for determining the optimal reconstruction direction. Then, multidimensional RBC is used to determine the number of fault variables based on the selected reconstruction direction. Finally, the primary and secondary fault variables are isolated according to their fault sensitivities. The feasibility of the proposed method is illustrated by a numerical simulation example and TE process. To sum up, the proposed method provide sensitivity information of each fault variables and high diagnosis rate. Moreover, it also has an advantage in reducing the calculation time because of the preliminary selection of fault variables.

Correlated coupled matrix tensor factorization method for simultaneous EEG-fMRI data fusion

ObjectiveFusion of EEG and fMRI data provides complementary information about the brain functions. Thus, data fusion should employ all dimensions of data to both extract the shared information, and distinguish the dissimilarities of modalities. MethodIn this paper, we present a new method based on Advanced Coupled Matrix Tensor Factorization (ACMTF) method to decompose the EEG tensor and fMRI matrix. We remove the confining assumption of identical shared components in ACMTF and propose an approach to maximize the correlation between the shared components of EEG and fMRI in the common mode, while removing the smearing effect of hemodynamic response function on fMRI data. This method is called Correlated Coupled Matrix Tensor Factorization (CCMTF). ResultsTwo simulation scenarios with different noise levels are performed to evaluate the performance of the proposed CCMTF method. The simulation results show 30.1% and 23.38% average improvement of Match Scores over ACMTF and N-way Partial Least Square (N-PLS) methods, respectively. The proposed method is also applied to a real dataset of an auditory oddball paradigm. The results of real data analysis show that the proposed CCMTF method estimates the unknown number of shared components between EEG and fMRI data, with p-value less than 0.007. Conclusion and SignificanceIn addition to the improvement of match score, the simulation results demonstrate that CCMTF has higher performance than ACMTF for fusion of EEG and fMRI datasets in case of extracting the dissimilarities in modalities, and it provides more convenient interpretation of the results, in comparison to N-PLS method.

Morphing of an adaptive shock control bump using pressurized chambers

The dawn of research on shock and boundary layer interaction control dates back to the 1970s, when humped transonic aerofoils were first studied as a means to improve the performance of supercritical aerofoil technology at off-design conditions. Since then, shock control bumps have been found to be promising devices for such kind of flow control. They have a smearing effect on the shock wave structure achieved through isentropic pre-compression of the flow upstream of the main shock and can significantly lower wave drag without incurring unacceptable viscous losses. However, their performance is strongly dependent on a set of geometrical parameters which must be adjusted according to the ever-changing flight conditions. A concept for an adaptive shock control bump is therefore presented. The proposed actuation mechanism aims at a compact, lightweight and simple structure which could be integrated into the spoiler region of near-future aircraft without major design changes required. Numerical optimization of a simplified analytical model of the structure is used to investigate the shock control bump adaptation to various aerodynamic target shapes. Compromises between geometrical conformity and both structural and actuation related requirements are studied. Furthermore, an outlook is given on design issues related to three-dimensional effects on a finite span shock control bump.

Operating State Reconstruction in Cyber Physical Smart Grid for Automatic Attack Filtering

Eliminating the erroneous state bias from cyberattack is essential to ensure the real-time control and secure operation of the smart grid, especially when cyberattack flourishes in recent years. For this reason, this article, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">for the first time</i> , proposes a new operating state reconstruction scheme to automatically filter out possible cyberattacks in smart grid. This scheme consists of an attack separation method, a state forecasting algorithm, and a state recovery approach. Based on P-Q decomposition, the attack separation method takes into account the network parameter perturbations and prediction uncertainties to analytically estimate the regular deviation of each state, thereby identifying potential abnormal states. Then, a particle filtering-based state forecasting algorithm is developed to evaluate the original operating level of the detected contaminated states. Finally, we propose a fast bilinear state recovery approach to mitigate the smearing effect of undetected contaminated states due to cyberattacks. The proposed state reconstruction method can not only detect cyberattacks but also realize automatic correction of the erroneous states, thus mitigating the devastating impact of attack on smart grids. The feasibility and effectiveness of our reconstruction scheme are extensively validated on IEEE standard 9-, 14-, 30-, 57- and 118-bus power systems. The obtained results show that the proposed scheme exhibits strong robustness, high stability, and promising performance for automatic attack filtering, indicating a great potential for implementations in deep cyber-penetrated smart grids.

Inverse modeling of SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and NO&amp;lt;sub&amp;gt;&amp;lt;i&amp;gt;x&amp;lt;/i&amp;gt;&amp;lt;/sub&amp;gt; emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts

Abstract. Top-down emission estimates provide valuable up-to-date information on pollution sources; however, the computational effort and spatial resolution of satellite products involved with developing these emissions often require them to be estimated at resolutions that are much coarser than is necessary for regional air quality forecasting. This work thus introduces several approaches to downscaling coarse-resolution (2∘×2.5∘) posterior SO2 and NOx emissions for improving air quality assessment and forecasts over China in October 2013. As in Part 1 of this study, these 2∘×2.5∘ posterior SO2 and NOx emission inventories are obtained from GEOS-Chem adjoint modeling with the constraints of OMPS SO2 and NO2 products retrieved at 50 km×50 km at nadir and ∼190km×50km at the edge of ground track. The prior emission inventory (MIX) and the posterior GEOS-Chem simulations of surface SO2 and NO2 concentrations at coarse resolution underestimate observed hot spots, which is called the coarse-grid smearing (CGS) effect. To mitigate the CGS effect, four methods are developed: (a) downscale 2∘×2.5∘ GEOS-Chem surface SO2 and NO2 concentrations to the resolution of 0.25∘×0.3125∘ through a dynamic downscaling concentration (MIX-DDC) approach, which assumes that the 0.25∘×0.3125∘ simulation using the prior MIX emissions has the correct spatial distribution of SO2 and NO2 concentrations but a systematic bias; (b) downscale surface NO2 simulations at 2∘×2.5∘ to 0.05∘×0.05∘ according to the spatial distribution of Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NL) observations (e.g., NL-DC approach) based on correlation between VIIRS NL intensity with TROPOspheric Monitoring Instrument (TROPOMI) NO2 observations; (c) downscale posterior emissions (DE) of SO2 and NOx to 0.25∘×0.3125∘ with the assumption that the prior fine-resolution MIX inventory has the correct spatial distribution (e.g., MIX-DE approach); and (d) downscale posterior NOx emissions using VIIRS NL observations (e.g., NL-DE approach). Numerical experiments reveal that (a) using the MIX-DDC approach, posterior SO2 and NO2 simulations improve on the corresponding MIX prior simulations with normalized centered root mean square error (NCRMSE) decreases of 63.7 % and 30.2 %, respectively; (b) the posterior NO2 simulation has an NCRMSE that is 17.9 % smaller than the prior when they are both downscaled through NL-DC, and NL-DC is able to better mitigate the CGS effect than MIX-DDC; (c) the simulation at 0.25∘×0.3125∘ using the MIX-DE approach has NCRMSEs that are 58.8 % and 14.7 % smaller than the prior 0.25∘×0.3125∘ MIX simulation for surface SO2 and NO2 concentrations, respectively, but the RMSE from the MIX-DE posterior simulation is slightly larger than that from the MIX-DDC posterior simulation for both SO2 and NO2; (d) the NL-DE posterior NO2 simulation also improves on the prior MIX simulation at 0.25∘×0.3125∘, but it is worse than the MIX-DE posterior simulation; (e) in terms of evaluating the downscaled SO2 and NO2 simulations simultaneously, using the posterior SO2 and NOx emissions from joint inverse modeling of both species is better than only using one (SO2 or NOx) emission from corresponding single-species inverse modeling and is similar to using the posterior emissions of SO2 and NOx emission inventories respectively from single-species inverse modeling. Forecasts of surface concentrations for November 2013 using the posterior emissions obtained by applying the posterior MIX-DE emissions for October 2013 with the monthly variation information derived from the prior MIX emission inventory show that (a) the improvements of forecasting surface SO2 concentrations through MIX-DE and MIX-DDC are comparable; (b) for the NO2 forecast, MIX-DE shows larger improvement than NL-DE and MIX-DDC; (c) NL-DC is able to better decrease the CGS effect than MIX-DE but shows larger NCRMSE; (d) the forecast of surface O3 concentrations is improved by MIX-DE downscaled posterior NOx emissions. Overall, for practical forecasting of air quality, it is recommended to use satellite-based observation already available from the last month to jointly constrain SO2 and NO2 emissions at coarser resolution and then downscale these posterior emissions at finer spatial resolution suitable for regional air quality modeling for the present month.

Locally linear back-propagation based contribution for nonlinear process fault diagnosis

This paper proposes a novel locally linear backpropagation based contribution (LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder (AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution (RBC), the propagation of fault information is described by using back-propagation (BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well, and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

Effects of Nb-vacancies and hydrostatic pressures on the electron density of states of Nb3Sn from first-principles calculations

The electron density of states is a key parameter to determine the superconducting critical properties of Nb3Sn and its composite wires, which usually work in extreme radiation environments and are subject to strong thermal and electromagnetic loads. In this paper, the contributions of Nb-vacancies and hydrostatic pressure on DOS are investigated by the first-principles calculations based on the density functional theory. The results indicate that Nb-vacancies impose strong influences both on the amplitude and distribution of the electron DOS, namely, the smearing effect and broadening effect, however, hydrostatic pressure only affects slightly the amplitude of DOS with energy in a relatively large range of pressure. The calculations also demonstrate that the combined action of Nb-vacancies and hydrostatic pressure can be superposed linearly. Furthermore, Nb-vacancies suppress the N(EF) of Nb3Sn seriously, and hydrostatic pressure results in an approximately linear decrease of N(EF) with increasing pressure, which coincides well with experimental data.

Human Location Classification for Outdoor Environment

Outdoor localisation can offer great capabilities in security and perimeter surveillance applications. The localisation of people become more challenges when involving with the nonlinear environment. GPS and CCTV are two localisation techniques usually use to localise human in an outdoor environment. However, they have weaknesses which result in low localisation accuracy. Therefore, the application of Device-free localisation (DFL), together with the Internet of things (IoT) is more appropriate due to their capability to detect the human body in all environmental conditions, and there is no problem losing signals as faced by GPS. This system offers excellent potential in humans localisation because humans can be detected wirelessly without any tracking device attached. In developing the DFL system, the main concern is the localisation accuracy. Although the existing DFL system gives significant result to the localisation, the accuracy is still low due to the large variation in RSSI values. Hence, a Radio Tomographic Imaging-based ANN classification (RTI-ANN) approach is proposed to increase the localisation accuracy. This Artificial Neural Network (ANN) is designed to learn the Radio Tomography imaging (RTI) input for classification purpose. Even though the RTI gives a good result to the localisation, however, it suffers from smearing effect. To eliminates this smearing area and background noise, pre-processing of the RTI image is required. Thus, extracting the valuable information technique from the RTI image has been proposed. By extracting the valuable information data from the RTI image, about 61% to 66% of the smearing noise is removed depending on the size of the RTI image. Only data directly associated with human attenuation used for training and learning of ANN. The experimental results show ANN system can localise human in the right zone for a given dataset.

Generalized grouped contributions for hierarchical fault diagnosis with group Lasso

In process industries, it is necessary to conduct fault diagnosis after abnormality is found, with the aim to identify root cause variables and further provide instructive information for maintenance. Contribution plots along with multivariate statistical process monitoring are standard tools towards this goal, which, however, suffer from the smearing effect and high diagnostic complexity on large-scale processes. In fact, process variables tend to be naturally grouped, and in this work, a novel fault identification strategy based on group Lasso penalty along with a hierarchical fault diagnosis scheme is proposed by leveraging group information among variables. By introducing the group Lasso as a regularization approach, groups of irrelevant variables tend to yield exactly zero contributions collectively, which help find the exact root cause, alleviate the smearing effect, and furnish clear diagnostic information for process practitioners. For online computational convenience, an efficient numerical solution strategy is also presented. Besides, it turns out that the proposed approach also applies to dynamic monitoring models with lagged measurements augmented, thereby enjoying widespread generality. Its effectiveness is evaluated on both the Tennessee Eastman benchmark process and a pilot-scale experiment apparatus.

Time Frequency Representation Enhancement via Frequency Matching Linear Transform for Bearing Condition Monitoring under Variable Speeds

Instantaneous frequency (IF) of shaft rotation is pivotal for bearing fault diagnosis under variable speed operations. However, shaft IF cannot always be measured as tachometers are not allowed to be installed in every case due to design reasons and cost concerns. Extracting the shaft IF ridge from time frequency representation (TFR) of vibration signals, therefore, becomes an alternative. Linear transform (LT), such as short time Fourier transform (STFT), has been widely adopted for such a purpose. Nevertheless, the accuracy of extracted IF ridges relies on the readability of TFR. Unfortunately, readability of TFR from STFT is often impaired by the smearing effect caused by non-synchronous frequencies between bases and signal components and limited time frequency resolution capability, which in turn adversely influences the accuracy of IF ridge extraction. To accurately extract IF ridges from vibration signals, this paper focuses on the first factor, which causes the smearing problem, and proposes a method named frequency matching linear transform (FMLT) to enhance the TFR, where transforming bases with frequencies varying with the shaft IF are constructed to alleviate the smearing effects. To construct the transforming bases with frequencies synchronous with shaft IF, a fast path optimization (FPO) algorithm, which generates all possible optimization paths among amplitude peaks and thereby ensures the continuity of extracted IF ridges, is adopted for IF pre-estimation. The TFR with improved readability can be subsequently obtained via FMLT, paving the way for accurate IF ridge extraction. Then, multiple IF ridges can be iteratively extracted using the FPO algorithm. The accuracy of extracted IF ridges before and after TFR enhancement is compared, indicating that the proposed FMLT can enhance the readability of TFR and lead to more accurate IF ridge extraction for bearing condition monitoring.