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21.
As a highly complex and time-varying process, gas-water two-phase flow is commonly encountered in industries. It has a variety of typical flow states and transition flow states. Accurate identification and monitoring of flow states is not only beneficial to further study of two-phase flow but also helpful for stable operation and economic efficiency of process industry. Combining canonical variate analysis (CVA) and Gaussian mixture model (GMM), a strategy called multi-CVA-GMM is proposed for flow state monitoring in gas-water two-phase flow. CVA is used to extract flow state features from the perspective of correlation between historical data and future data, which solves the cross correlation and temporal correlation of multi-sensor measurement data. GMM calculates the possibility that the current flow state belongs to each typical flow pattern and judges the current flow state by probability indicators. It is conducive to follow-up use of Bayesian inference probability and Mahalanobis distance-based (BID) indicator for flow state monitoring, which avoids repeated traversal of multiple CVA-GMM models and improves the efficiency of the monitoring process. The probability indicators can also be used to analyze transition flow states. The method combining the probabilistic idea of GMM with the deterministic idea of multimodal modeling can accurately identify the current flow state and effectively monitor the evolution of flow state. The multi-CVA-GMM method is validated by using the measured data of the horizontal flow loop of gas-water two-phase flow experimental facility, and its effectiveness is proved. 相似文献
22.
西藏江达县白格村金沙江右岸于2018年10月11日和2018年11月3日先后发生2次大规模滑坡—堰塞湖堵江事件,溃堰洪水对下游拉哇库区不良地质体的稳定性造成不同程度的影响。为保障下游水电站建设安全,对拉哇库区主要不良地质体建立了基于星载InSAR技术、无人机技术和地面传感器实时监测的“天空地”一体化监测预警体系,以多维空间采集技术获取变形信息,通过智能监控平台对信息及时进行处理、分析和可视化呈现,利用平台、短信等方式向相关人员进行分级告警,取得了较好的应用效果。 相似文献
23.
Real‐time monitoring by proton relaxometry of radical polymerization reactions of acrylamide in aqueous solution 下载免费PDF全文
Elton Jorge da Rocha Rodrigues Roberto Pinto Cucinelli Neto Pedro José Oliveira Sebastião Maria Inês Bruno Tavares 《Polymer International》2018,67(6):675-683
The potential of time‐domain nuclear magnetic resonance (TD‐NMR) for the real‐time monitoring of solution radical polymerizations is demonstrated. A model system composed of a redox‐pair initiator system, acrylamide as monomer and water as solvent was investigated. A second‐generation continuous wave free precession technique was employed to measure the longitudinal relaxation time constant (T1) of the samples throughout the polymerization reactions. This parameter was shown to be sensitive to the reactant feed free‐radical enhancement of the water molecule relaxation time, making it a good probe to monitor monomer conversion in real time in an automated, non‐destructive fashion. It was found that the T1 value was better than the transverse relaxation time constant (T2) for describing the evolution of the polymerization reactions, due to its greater sensitivity to paramagnetic effects. The TD‐NMR signal variation observed was linked to the formation, propagation and termination steps of the radical polymerization kinetics scheme. These first results may contribute to the application of real‐time monitoring of radical polymerization reactions employing low‐cost and robust TD‐NMR spectrometers. © 2018 Society of Chemical Industry 相似文献
24.
Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration. 相似文献
25.
Yan Xiong Xianwu Du Mingyu Xiang Hao Wang Weimin Wang Zhengyi Fu 《Journal of the European Ceramic Society》2018,38(12):4167-4172
The densification behaviors of pure B4C and B4C-ZrO2 mixtures were compared during hot pressing. The results showed that in-situ formed ZrB2 effectively enhanced the densification process of B4C-ZrO2 mixtures, more significantly during the intermediate stage. Within the relative density ranging from 0.75 to 0.90, the B4C-15?wt%ZrO2 mixture (B15Z) achieved the maximum densification rate as twice much as that of pure B4C. The stress exponent n>3 indicated plastic deformation was the dominant densification mechanism of B15Z. The viscosities of plastic flow were evaluated using Murray-Rodger-William equation and the viscosity of B15Z was only a quarter of that in pure B4C. The sintering activation energy was calculated to be 305.9?kJ/mol for pure B4C and 197?kJ/mol for B15Z, respectively. It was proposed that the lower viscosity of plastic flow and activation energy accelerated the sliding and propagating motions of plastic flow, by which underlain the enhanced densification behaviors of B4C-ZrO2 mixtures. 相似文献
26.
This study presents a back-analysis of geotechnical parameters on prefabricated vertical drain improved ground at a site in the Mekong Delta. Various time?settlement behaviors that reflected different clay thicknesses and loading patterns were observed. The total surface settlement behavior at several monitoring locations was simulated using an updated exponential method that considered staged construction. The analyzed results were validated by substituting the values into a theoretical solution for radial consolidation. The estimated theoretical behaviors were comparable with the monitored behaviors. The geotechnical parameters were back-analyzed by applying the previously analyzed results to various theoretical and empirical formulas. However, the use of extensometer data that were installed at large intervals produced different values of the geotechnical properties. Furthermore, finite element analysis supported the back-analyzed total settlement behaviors and nearly disregarded the application of the geotechnical properties that were obtained using either surface or subsurface settlement data. However, settlements and excess pore pressures in the sublayers were not successfully predicted even when the geotechnical properties were adjusted. Thus, subsurface instruments that can be installed closely in thick clay deposits are required to reliably reevaluate the variations in geotechnical properties along a certain depth. 相似文献
27.
针对煤矿发生事故后传统救援监控系统无法实时对井下人员进行动态定位,导致矿井救援盲目性大、救援效率差、救援难度大等技术难题,为了进一步提高煤矿救援效率,通过技术研究,设计了一套以通信基站为核心的智能化救援监控系统,分析了该系统结构组成、工作原理,通过在担水沟煤矿井下实际应用效果来看,智能化救援监控系统对人员定位精准度达95%,实现人员动态位置三维成像,救援效率提高至80%以上,有效缩短了煤矿事故救援时间,取得了显著应用成效。 相似文献
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R. Dwaraka 《Machining Science and Technology》2020,24(4):638-662
AbstractThe performance of electrical discharge machining (EDM) primarily depends on the spark quality generated in the inter-electrode gap (IEG) between the tool and workpiece. A method for obtaining accurate information about the spark gap is required to effectively monitor the EDM process. The rise and fall of thermal energy in the discharge zone at a rapid rate during the dielectric breakdown produces high-pressure shock waves. This work explores the suitability of using acoustic emission (AE) generated from these shock waves and the elastic AE waves released on the workpiece due to the induced stress to monitor the performance and spark gap in EDM. The information content of the AE signals acquired at various machining conditions was extracted using AE RMS, spectral energy and peak amplitude. These features were able to well discriminate the machining condition, tool material, workpiece material, flushing pressure, current density, the initial surface roughness of the tool. Additionally, the AE signal features had a good and consistent correlation with the performance parameters, including material removal rate, surface roughness (Ra and Rq) and tool wear. The findings lay the groundwork to develop an effective, non-intrusive in-situ AE-monitoring system for performance and IEG condition in EDM. 相似文献