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Dive into the research topics where Chunming Xia is active.

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Featured researches published by Chunming Xia.


Automatica | 2005

Detecting and isolating multiple plant-wide oscillations via spectral independent component analysis

Chunming Xia; John Howell; Nina F. Thornhill

Disturbances that propagate throughout a plant can have an impact on product quality and running costs. There is thus a motivation for the automated detection of plant-wide disturbances and for the isolation of the sources. A new application of independent component analysis (ICA), multi-resolution spectral ICA, is proposed to detect and isolate the sources of multiple oscillations in a chemical process. Its key feature is that it extracts dominant spectrum-like independent components each of which has a narrow-band peak that captures the behaviour of one of the oscillation sources. Additionally, a significance index is presented that links the sources to specific plant measurements in order to facilitate the isolation of the sources of the oscillations. A case study is presented that demonstrates the ability of spectral ICA to detect and isolate multiple dominant oscillations in different frequency ranges in a large data set from an industrial chemical process.


Journal of Process Control | 2003

Loop status monitoring and fault localisation

Chunming Xia; John Howell

Abstract Loop status monitoring involves the declaration of deterministic trends, such as oscillations and drifting, in loops that are in multi-loop plant configurations. By analysing various time domain statistics pertaining to PI/PID control loops, a trend can be recognised as one of seven categories. The scientific basis for working with the particular statistics is given and the categorisation process is described. These statistics can be combined to produce an Overall Loop Performance Index for each loop, which can be compared to localise a single fault in a multi-loop arrangement. Estimation methods for these time domain statistics are outlined and the performance of the approach is demonstrated on both simulated and real plant data sets.


IFAC Proceedings Volumes | 2002

ANALYSIS OF PLANT-WIDE DISTURBANCES THROUGH DATA-DRIVEN TECHNIQUES AND PROCESS UNDERSTANDING

Nina F. Thornhill; Chunming Xia; John Howell; John W. Cox; Michael A. Paulonis

Plant-wide disturbances can have an impact on product quality and running costs. Thus there is a motivation for automated detection of a plant-wide disturbance and for diagnosis of the root cause. In this article, data-driven techniques are used to analyze plant-wide disturbances caused, for instance, by limit cycle oscillation in a control loop. The control loops participating in the disturbance are detected and displayed on a process schematic. Other numerical signatures derived from the data trends are utilized for the diagnosis of the root cause. The outcome is a visual display that integrates process understanding and data-driven analysis.


Chinese Journal of Chemical Engineering | 2007

Isolation of whole-plant multiple oscillations via non-negative spectral decomposition

Chunming Xia; Jianrong Zheng; John Howell

Abstract Constrained spectral non-negative matrix factorization (NMF) analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources. The technique is described and demonstrated by analyzing data from both simulated and real plant data of a chemical process plant. Results show that the proposed approach can map multiple oscillatory sources onto the most appropriate control loops, and has superior performance in terms of reconstruction accuracy and intuitive understanding compared with spectral independent component analysis (ICA).


IFAC Proceedings Volumes | 2001

Loop Status Statistics

Chunming Xia; John Howell

Abstract The filtering of loop status statistics is proposed as a way of avoiding chatter on plant schematics, whose control loop icons change when the status of a loop is deemed to have changed on the basis of a closed loop assessment. A loop status statistic is created by assigning different real number values to various qualitative descriptions of loop status. Loop status time series pertaining to individual control loops can then be filtered before inverse conversion back to the original descriptions. This is tested on both PI & PID control loops.


IFAC Proceedings Volumes | 2003

Isolating Multiple Sources of Plant-Wide Oscillations via Independent Component Analysis

Chunming Xia; John Howell

Abstract Constrained spectral ICA analysis on perturbed controller outputs is proposed to isolate multiple sources of plant-wide oscillations. The technique is described and applied to data pertaining to a simulated case study and to real data obtained from an industrial chemical plant. Results demonstrate its ability to isolate the sources of multiple oscillations at the loop level.


international conference on control applications | 2002

PI loop status monitoring

Chunming Xia; John Howell

Loop status monitoring involves the declaration of deterministic trends, such as oscillations and drifting in process plant control loops. By analysing various time domain statistics associated with PI control loops, a trend can be associated as one of seven categories. The basis for doing this is given and the categorisation process is described.


IFAC Proceedings Volumes | 2001

Controller Output Based, Single Number Statistics for Loop Status Monitoring

Chunming Xia; John Howell

Abstract Two single number statistics are proposed that, when used with others, enable the basic characteristics of a closed loop trend to be categorised to indicate that the loop is in one of a number of different statuses: well-behaved & in steady state, undergoing a short-term transient, cycling at a fundamental frequency similar to the natural frequency of the loop, cycling at a relatively low fundamental frequency, or undergoing a trend that is disturbed in some non-stationary manner. These classifications are slightly different for PI and PID controllers, only the former is discussed here. As is normal with CLP approaches, only the outer loop of each cascade control system is considered because of the continual set-point changes that arise in its inner loop.


conference on industrial electronics and applications | 2013

Finding direction of oscillation propagation using non-parametric transfer entropy method

Liang Zhang; Chunming Xia; Jiabin Cao; Jianrong Zheng

Disturbances in process plants may usually widely propagate because of the interconnection in process equipment. It is necessary to get the correct source of loop oscillations. The application of transfer entropy method has now been proved effective. These existing methods need too much process knowledge and the results are affected by different parameters. In this work, spectral independent component analysis (Spectral ICA) are used to select the oscillatory process loop variables and reduce the number of variables which need to be analyzed by transfer entropy method, then a normalized transfer entropy method with non-parametric is used to isolate the root-cause of plant-wide and identify the propagation paths. The successful application of the methods has been demonstrated through two cases.


international conference on control engineering and communication technology | 2012

Physical-Based Modeling of Nonlinearities in Process Control Valves

Liang Zhang; Chunming Xia; Jiabin Cao; Jianrong Zheng

Nonlinear failures of control valves, such as valve stiction, are often encountered in process industries. This degrades the performance of control loops and systems. Modeling and analyzing various valve nonlinearities are essential for either failure prevention or fault diagnosis. The physical model of a typical pneumatic control valve is introduced and modeled by using object-oriented AMESim toolkit in this paper. Four common failures of valves are simulated by setting specific physical model parameters. Operating data are obtained through the closed-loop feedback control system simulations. Nonlinearities are successfully verified with the existing nonlinear testing methods. The testing results validate the proposed physical model. The generated faulty operation data can be utilized for further study, especially for control valve fault diagnosis and nonlinear compensations.

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John Howell

East China University of Science and Technology

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Jianrong Zheng

East China University of Science and Technology

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Liang Zhang

East China University of Science and Technology

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Jiabin Cao

East China University of Science and Technology

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John Howell

East China University of Science and Technology

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John W. Cox

Eastman Chemical Company

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