查看更多>>摘要：The effectiveness of satellite solar-induced chlorophyll fluorescence (SIF) for drought evaluation was measured in this study. Here we compare the association of SIF with soil moisture (SM), precipitation (PPT), standardized precipitation evapotranspiration index (SPEI), and ratio of evapotranspiration (ET) to monitor the drought stress on crop growth. A severe drought occurrence was observed in 2015 as compared to other years between 2007 and 2017 in Xinjiang of China. In the period of this drought, the changes of SIF and SIF normalized by absorbed photosynthetically active radiation (phi F) were obstinate. SPEI and ET were observed with higher sensitivity and much more constant decline in response to drought than SM and PPT. Moreover, phi F is highly sensitive to drought than SIF, SIF normalized by photosynthetically active radiation (SIFPAR), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI). The results demonstrate that satellite SIF provides deep insight for drought detection.
查看更多>>摘要：Measuring the solution concentration of processes for product quality control in industrial crystallization processes is a hot topic. In recent years, with the development of process analysis technology, various innovative measurement technologies for different situations and operating conditions have been developed. This paper systematically reviews the research on various solution concentration measurement technologies that have been developed for the crystallization process. These measurement technologies are divided into off-line, at-line, online, and in-situ methods based on their measurement modes and principles. The measurement principle, advantages, disadvantages, application field, and development status of each of the existing measurement technologies are illustrated in detail, with a particular focus on measurement methods based on spectroscopy technology, which are very popular for crystallization processes. In addition, this paper presents the problems of solution concentration measurement technology and points out future research directions.
查看更多>>摘要：Information about blood arterial oxygen saturation (SpO(2)) is crucial in critical care settings or home health monitoring during the COVID-19 pandemic. Also, we need to identify the factors that affect the SpO(2) measurement. In this paper, the effect of compression of the cuff during noninvasive blood pressure (NIBP) measurement on the SpO(2) results was investigated. A custom-made system was used for simultaneous measurement of NIBP and SpO(2). The study was conducted on 213 subjects aged between 21 and 93, with a systolic blood pressure of (94 to 194) mmHg, diastolic blood pressure of (52-98) mmHg, and 994 NIBP readings were used for the analysis. During the NIBP measurement, momentary changes in SpO(2) can reach +/- 17% and are in most cases positive (mean 2.9%). The change was not correlated with sex, age, height, body weight, BMI, HR and blood pressure. The obtained results show that frequent NIBP measurements may lead to wrong conclusions about SpO(2). In our study, pressure measurements mainly caused the increase of blood oxygenation level.
查看更多>>摘要：Condition monitoring of mining conveyor is a highly essential task to ensure minimum disruption to the mining operational system. Failure of one or more conveyor components can result in significant operational downtime, economic loss, and safety risks. The current monitoring method still involves subjective measure from maintenance engineers, where at some cases, fault can be left undetected and leads into site incident. Therefore, there is a high demand for real-time condition monitoring technology to detect early fault on conveyor. In this study, the effective application of distributed optical fibre sensor (DOFS) was explored for long distance real-time condition monitoring of mining conveyor. The fault detection framework was developed by integrating and modifying the Isolation Forest algorithm to analyse optical signals for effective detection of defective idlers. Further, the optical signal was analysed to extract the damage progression of defective idler with time and space. The results were used to classify various levels of damage and to set appropriate damage thresholds. Also, software interface, that can be used to set the sensing parameters, to collect, analyse, and visualise the signal in real-time, was developed. Finally, the developed condition monitoring system was used to monitor a 1.6 km long section of a conveyor structure in Western Australia for a period of 10 months. The results and findings from the field monitoring were presented together with automated fault detection framework for condition monitoring of mining conveyor.
查看更多>>摘要：Kalman filter (KF) is the main algorithm in the field of optimal estimation. KF can realize the optimal estimation only under the premise that both the function model and the random model are known. In most practical applications, random models are often unknown, and unknown or biased random models will make filtering suboptimal. Besides, the gross errors will cause the innovation to appear abnormal, making the innovation statistics abnormal. The abnormal innovation statistics will lead to the deviation of the estimated noise covariance information. Therefore, when there are gross errors, how to ensure the random model's correct estimation is a major problem faced by the current random model estimation method. To solve this problem, we propose a robust autocovariance least-squares noise covariance estimation algorithm (RALS). In the new method, we first introduce the robust Kalman filtering method (RKF) based on the chi-square test to resist the influence of abnormal innovation on subsequent epoch state estimation; Then, based on the basic idea of the correlation robustness method, we constructed the correlation robust innovation function sequence model, taking into account the correlation between the innovation sequences, to repair the abnormal innovation statistics, so as to obtain accurate post-test innovation statistics; Subsequently, we use the autocovariance least square method to eliminate the coupling effect between the two types of noise covariance information, to correctly estimate the two types of noise covariance information; Finally, we adopt an iterative strategy to eliminate the influence of a priori random model deviation, to realize the separation of the gross error coupled in the abnormal innovation and the prior random model deviation. Two experimental results show that: compared with the KF, RKF, and ALS methods, the new method has higher noise covariance estimation accuracy and filtering accuracy.
查看更多>>摘要：On account of lacking labeled samples for the bearing fault diagnosis in real engineering applications, transfer learning is widely investigated for transferring diagnosis information. A more challenging but realistic scenario called transfer across different machines (TDM) is investigated in this paper where previous approaches may degenerate greatly with more drastic domain shifts. A joint distribution adaptation-based transfer network with diverse feature aggregation (JDFA) is proposed, where the diverse feature aggregation module is added to enhance feature extraction capability across large domain gaps. Then the joint maximum mean discrepancy between source and target domain samples is adopted to reduce the distribution discrepancy automatically. Extensive TDM transfer learning experiments are conducted. The average accuracy reaches 99.178% that is much higher than state-of-the-art methods, demonstrating the proposed JDFA framework can effectively achieve superior diagnostic performance, and significantly promote fault diagnosis research under TDM scenario in view of applicability and practicability of algorithms.
Swarna, K. S., VVinayagam, ArangarajanAnanth, M. Belsam JebaKumar, P. Venkatesh...
20页
查看更多>>摘要：This paper proposes an ensemble Random Subspace (RS) classifier for discrimination of High Impedance Fault (HIF) in photovoltaic connected power network. The design and simulation of power network is considered in MATLAB/Simulink environment to analyze various faults (HIF, Symmetrical, and unsymmetrical), and non-fault events. In pre-process stage of classification, the features from current signals of different events are extracted by using discrete wavelet transform technique. Then, features are used to learn the RS ensemble and base classifiers (K-nearest neighbor, Logistic regression, and Random tree) to get predictions in classification phase. The classification analysis is carried out under with and without real-time varying solar irradiance, and addition of noise data over the input of classifiers. The proposed RS ensemble classifier, discriminates HIF with higher accuracy and success rate than base classifiers. Further, the effectiveness was verified with evaluation of performance indices which shows the proffered ensemble classifier outperforms base classifiers.
查看更多>>摘要：The fiber-optic external sinusoidal phase-modulating interferometer (ESPMI) is sensitive, accurate, and robust. However, parasitic interference hinders further improvement of its precision, and reduces its adaptability to measurement targets with different characteristics. In this paper, we analyzed the mathematical model of parasitic interference, and proposed a Kalman filter-based method to eliminate the parasitic interference effect in the ESPMI. This method works by filtering out the time-varying offsets in quadrature signals brought by the parasitic interference. Simulations and experiments were conducted to verify the effectiveness of the proposal. The experimental results showed that our proposal had great parasitic interference effect elimination ability. Compared with existing schemes, our method is highly accurate and low-cost with no loss in interferometric range, thus having catalytic effect on the application of ESPMI in related fields.
查看更多>>摘要：The current researches on strain mainly have focused on the surface strain and lacked the monitoring of internal strain, due to the imperfection of real-time monitoring technology for internal strain. In order to obtain the internal strain evolution law in the process of coal sample crack propagation, this paper used a fiber Bragg grating monitoring system to study the internal strain change law of coal samples with different structures during the loading process. The results show that the internal strain is more sensitive than the axial strain when the sample is partially destabilized; in the crack propagation stage, the strain at some measurement points changes abruptly and when the overall instability is approaching, the strains of most measurement points have a sudden change; during the load-holding stage, the axial strain rate of coal samples with holes is greater than that of intact coal samples. This research provides a basis for rock burst strain monitoring based on internal strain field data in the future.
查看更多>>摘要：The paper presents multiple impact damage detections and characterization in a multi-layered carbon fiber reinforced polymer structure using a multi-step combination of the global area and local area damage detection methods. A global area analysis using laser Doppler vibrometer (LDV) with piezoelectric lead zirconate titanate (PZT) actuator-based guided waves (GW) excitation was used to detect and locate the damage. The GW full wavefield (FWF) mode conversion and root mean square (RMS) based LDV studies helped to visualize the impact crack damage (ICD) when tested at varying excitation frequencies. The experimental results were cross-verified numerically using the spectral element method (SEM). The implemented structural health monitoring (SHM) based sectorial elliptical code (SEC) reduced the overall calculation time in damage localization and accurately identified the damage from experimental and numerical data. An infrared thermography (IRT)-based crack identification algorithm was developed in pinpointing the ICD shape. The automatic method removes the influence of high heat sources and highlights the damage zones easily. The accuracy of the localization strategy was successfully verified with non-contact active IRT analysis. The crack size and damage severity were quantified using the ultraviolet radiation (UVR) method with a solution mixture based organic dye. The global area method allows detecting possible damage, validated and quantified with the non-destructive testing (NDT) techniques.
NSTL
Elsevier