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In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzzy c-means (FCM) algorithm. The probabilistic constraint imposed on the membership value in the FCM algorithm is relaxed in the possibilistic clustering algorithm. Because of this relaxation, the possibilistic approach is shown in this paper to give more consistent results in the context of the FDI tasks. The possibilistic clustering approach has also been used to detect novel fault scenarios, for which the data was not available while training. Fault signatures that change as a function of the fault intensities are represented as fault lines, which have been shown to be useful to classify faults that can manifest with different intensities. The proposed approach has been validated here through simulations involving a benchmark quadruple tank process and also through experimental case studies on the same setup. For large scale systems, it is proposed to use the possibilistic clustering based approach in the lower dimensional approximations generated by algorithms such as PCA. Towards this end, finally, we also demonstrate the key merits of the algorithm for plant wide monitoring study using a simulation of the benchmark Tennessee Eastman problem. 相似文献
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Ketan P. Detroja Ravindra D. Gudi Sachin C. Patwardhan 《Control Engineering Practice》2012,20(10):1033-1041
Historical data based fault diagnosis methods exploit two key strengths of multivariate statistical approaches, viz.: (i) data compression ability, and (ii) discriminatory ability. It has been shown that correspondence analysis (CA) is superior to principal components analysis (PCA) on both these counts (Detroja, Gudi, Patwardhan, & Roy, 2006a), and hence is more suited for the task of fault detection and isolation (FDI). In this paper, we propose a CA based methodology for fault diagnosis that can facilitate significant data reduction as well as better discrimination. The proposed methodology is based on the principle of distributional equivalence (PDE). The PDE is a property unique to the CA algorithm and can be very useful in analyzing large datasets. The principle, when applied to historical data sets for FDI, can significantly reduce the data matrix size without significantly affecting the discriminatory ability of the CA algorithm. This can significantly reduce computational load during statistical model building. The data reduction ability of the proposed methodology is demonstrated using a simulation case study involving benchmark quadruple tank laboratory process. The proposed methodology when applied to experimental data obtained from the quadruple tank process also demonstrated data reduction capabilities of the principle of distributional equivalence. The above aspect has also been validated for large-scale data sets using the benchmark Tennessee Eastman process simulation case study. 相似文献
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Abhishek Dwivedi Ravindra Gudi Pratim Biswas 《International Journal of Hydrogen Energy》2017,42(36):23227-23241
The consistent rise of CO2 concentration in the atmosphere is known to be significantly detrimental to the environment. Thus, mitigating CO2 has become an urgent necessity. Current methods involving CO2 mitigation can be broadly divided into two major categories which involve (i) CO2 capture and sequestration (CCS) and (ii) CO2 capture and valorization. Since, production of fuels/chemicals is an added feature along with mitigation in CO2 valorization based methods, they could be economically favorable. An energy intensive CO2 capture step is a common drawback of most CO2 valorization methods that aim to mitigate CO2 from major CO2 emission sources (such as industrial flue gases). In this paper we employ and analyze a relatively new process called tri-reforming [1,2] which was developed to directly convert power plant based flue gases to synthesis gas, while avoiding the capture step. This paper is presented as an improvement over a tri-reforming coupled methanol production process as developed by Zhang et al. [3]. The process in Zhang et al. [3] involves utilizing tri-reforming process using flue gas and methane to produce synthesis gas which is then converted to methanol in the next step. The main contributions of this paper to the tri-reforming coupled methanol production process are: (i) proposition of a high pressure tri-reforming step to limit capital costs of the process (ii) establishment of steam input coupled with water separation step as a process improvement whose impact is shown to further amplify at higher tri-reformer pressures. The paper evaluates the process in terms of the profit generating and CO2 valorization potential of the process as reflected by two parameters, gross margin (GM) and NPCV (net percentage of CO2 valorized) respectively. In the proposed approach, higher pressures were utilized in the tri-reforming process to ensure economic feasibility of the process by limiting the reactor volume. The process improvements for the flowsheet containing the steam input combined with water separation (SWS) step over the one without these steps (termed as WSWS) are demonstrated in terms of an increase in GM/NPCV values at various pressures. The results indicate substantial improvements in GM and NPCV values (especially at higher tri-reformer pressures) ranging from 24.30 to 84.96% and 28.80–78.44% respectively in SWS cases over WSWS cases at various pressures. The simulations have been carried out in Aspen Plus V8.4 and are optimized using sensitivity analysis. 相似文献
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R.H. López‐Bañuelos F.J. Moscoso P. Ortega‐Gudiño E. Mendizabal D. Rodrigue R. González‐Núñez 《Polymer Engineering and Science》2012,52(12):2489-2497
In this study, agave fiber/linear medium density polyethylene composites were manufactured by rotational molding. A laboratory scale biaxial machine was used, where the internal air temperature during the processing cycle was measured. Two sizes of agave fibers (50 and 100 mesh) were used separately and mixed together (50/50) at concentrations varying between 0 and 15 wt%. The initial mixtures were obtained by dry blending, rotomolded under different operation conditions (oven temperature, processing cycle time, and rotational speeds), and the final pieces were compared. For each process condition, a complete morphological analysis was performed to relate with mechanical properties in terms of tensile, impact, and flexural strength. The results show that there is an optimum fiber concentration around 10%, and blending fiber sizes gave better tensile properties than using each size alone. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers 相似文献
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Data collected from operating plants can be mined to extract information related to both normal and fault modes of operation. Correspondence analysis (CA), that decomposes a measure of row–column association, to generate the lower dimensional space has been recently proposed [1] for this task. CA represents the association between samples and variables in terms of angle based measures on a biplot. Thus, toward clearer resolution of the faults, polar clustering and classification procedures are necessary. In this paper, we develop a methodology to mine the operating data and build such clusters. We demonstrate the application of this methodology on data generated from simulations and experiments involving representative systems,for detecting parametric changes and resolving sensor and actuator biases. 相似文献
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Facile Preparation of Multifunctional WS2/WOx Nanodots for Chelator‐Free 89Zr‐Labeling and In Vivo PET Imaging 下载免费PDF全文
Liang Cheng Anyanee Kamkaew Sida Shen Hector F. Valdovinos Haiyan Sun Reinier Hernandez Shreya Goel Teng Liu Cyrus R. Thompson Todd E. Barnhart Zhuang Liu Weibo Cai 《Small (Weinheim an der Bergstrasse, Germany)》2016,12(41):5750-5758
While position emission tomography (PET) is an important molecular imaging technique for both preclinical research and clinical disease diagnosis/prognosis, chelator‐free radiolabeling has emerged as a promising alternative approach to label biomolecules or nanoprobes in a facile way. Herein, starting from bottom‐up synthesized WS2 nanoflakes, this study fabricates a unique type of WS2/WOx nanodots, which can function as inherent hard oxygen donor for stable radiolabeling with Zirconium‐89 isotope (89Zr). Upon simply mixing, 89Zr can be anchored on the surface of polyethylene glycol (PEG) modified WS2/WOx (WS2/WOx‐PEG) nanodots via a chelator‐free method with surprisingly high labeling yield and great stability. A higher degree of oxidation in the WS2/WOx‐PEG sample (WS2/WOx (0.4)) produces more electron pairs, which would be beneficial for chelator‐free labeling of 89Zr with higher yields, suggesting the importance of surface chemistry and particle composition to the efficiency of chelator‐free radiolabeling. Such 89Zr‐WS2/WOx (0.4)‐PEG nanodots are found to be an excellent PET contrast agent for in vivo imaging of tumors upon intravenous administration, or mapping of draining lymph nodes after local injection. 相似文献