Many continuous industrial processes operate in different steady states with different grades or products. The switching between two steady states is called transition. transition consists of a series of operation cha...
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Many continuous industrial processes operate in different steady states with different grades or products. The switching between two steady states is called transition. transition consists of a series of operation changes that should be carried out in proper order, within certain magnitudes and time region. Since faulty operation may lead to increase in inferior products or even hazard events, monitoring of the transition is desired. In this work, a transition identification and monitoring scheme is proposed based on slow feature analysis. Two monitoring statistics which represent the location of the trajectory and the speed of transition are proposed. Besides, operating faults are generated based on the guidewords of hazard and operability analysis (HAZOP). Using a numerical case and the mode 4-to-2 transition of the Tennessee-Eastman process in which catastrophic failures exist, the effectiveness of the proposed method is validated. In addition to missed detection rate and false alarm rate, two performance indexes known as detection time (DT) and rescue time (RT) are introduced. The advantages of proposed method are benchmarked against the stage-based sub principle component analysis(sub-PCA) and the global preserving statistics slow feature analysis(GSSFA). (C) 2020 Published by Elsevier Ltd.
Modern industries are facing diverse market demands and exhibit typical multimode characteristics. In this context, not only common process characteristics such as nonlinearity, non-Gaussianity, and multiscale become ...
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Modern industries are facing diverse market demands and exhibit typical multimode characteristics. In this context, not only common process characteristics such as nonlinearity, non-Gaussianity, and multiscale become more prominent and complex but also faults, especially small ones, are easily masked by normal process behaviors such as transition operations, which pose great challenges to traditional process monitoring methods. To address these challenges, this article makes full use of the capability of kernel entropy component analysis (KECA) in capturing cluster structure and extracting data features and proposes a monitoring scheme applicable to multimode processes. The scheme is formulated under a multimodel framework with Bayesian fusion. First, an improved fuzzy $C$ -means (IFCM) clustering method based on KECA is presented for mode division and identification. Then, multiscale KECA (MSKECA) is developed for multivariate statistical modeling, and angle variance index (AVI) is designed for global-scale monitoring. Finally, a Bayesian inference probability (BIP) index that integrates the results of each unsupervised MSKECA-AVI monitoring model is constructed to determine the process status. The case study on the Tennessee Eastman process (TEP) with operational variability shows that the proposed scheme is effective, especially for small-magnitude faults and transition operations, and performs better overall than some state-of-the-art methods.
Modern process industries are adapting to the global competitiveness by equipping themselves to be flexible and producing products of different grades. This paper proposes an offline monitoring scheme which is suitabl...
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ISBN:
(纸本)9783952426937
Modern process industries are adapting to the global competitiveness by equipping themselves to be flexible and producing products of different grades. This paper proposes an offline monitoring scheme which is suitable for such a dynamic environment where process transitions occur in a multi-grade continuous process. Modeling of every possible grade change is required for proper fault detection and isolation. The proposed technique uses Multiway Principal Component Analysis (MPCA) to eliminate the necessity to develop an individual model for monitoring every grade change performed in the process and is illustrated on an industrial case study of paper machine, a typical example of a production line where multiple grade changes occur naturally.
Chemical companies face a major transition as the current workforce ages and the number of retirements accelerates resulting in many years of lost operating experience. It will be challenging to maintain and improve t...
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Chemical companies face a major transition as the current workforce ages and the number of retirements accelerates resulting in many years of lost operating experience. It will be challenging to maintain and improve the reliability and safety of operations as some of the best operators leave the workforce. Faults due to operator errors can be addressed by improving the operator's training and increasing the robustness of the control system to operator variability. In this article, we demonstrate how operator variability can be detected as a first step toward measuring and improving the control system's robustness. The characterization of a typical operator procedure caused transition, a reflux pump switch, and the resulting control system response was analyzed using the dynamic process simulation tool Honeywell UniSim Design. The best operator response and potential errors for other daily operator activities such as pump switches, reboiler switches, and similar operational transitions can be obtained offline by studying process history. (c) 2014 American Institute of Chemical Engineers Process Saf Prog 33: 333-338, 2014
Performance degradation of integrated circuits due to aging effects, such as Negative Bias Temperature Instability (NBTI), is becoming a great concern for current and future CMOS technology. In this paper, we propose ...
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Performance degradation of integrated circuits due to aging effects, such as Negative Bias Temperature Instability (NBTI), is becoming a great concern for current and future CMOS technology. In this paper, we propose two monitoring and masking approaches that detect late transitions due to NBTI degradation in the combinational part of critical data paths and guarantee the correctness of the provided output data by adapting the clock frequency. Compared to recently proposed alternative solutions, one of our approaches (denoted as Low Area and Power (LAP) approach) requires lower area overhead and lower, or comparable, power consumption, while exhibiting the same impact on system performance, while the other proposed approach (denoted as High Performance (HP) approach) allows us to reduce the impact on system performance, at the cost of some increase in area and power consumption.
Three - way data collected from batch processes and from transitions of continuous processes are dynamic in nature; the process variables in such processes are both auto correlated and cross correlated. Empirical mode...
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Three - way data collected from batch processes and from transitions of continuous processes are dynamic in nature; the process variables in such processes are both auto correlated and cross correlated. Empirical models developed for the statistical process control of these processes should be capable of capturing the auto and cross correlation of the process variables. Statistical process control checks deviations from a nominal behaviour. Therefore for the statistical process control of batch processes and transitions we should look at deviations of process variable trajectories from their nominal trajectories and from their nominal auto/cross correlations. This paper addresses issues related to modelling three way data collected from such processes using projection methods, such as principal component analysis (PCA) and partial least squares (PLS).
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