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تعداد صفحات این فایل: ۱۷ صفحه

بخشی از مقاله انگلیسیعنوان انگلیسی:On the use of multi-agent systems for the monitoring of industrial systems~~en~~

Abstract

The objective of the current paper is to present an intelligent system for complex process monitoring, based on artificial intelligence technologies. This system aims to realize with success all the complex process monitoring tasks that are: detection, diagnosis, identification and reconfiguration. For this purpose, the development of a multi-agent system that combines multiple intelligences such as: multivariate control charts, neural networks, Bayesian networks and expert systems has became a necessity. The proposed system is evaluated in the monitoring of the complex process Tennessee Eastman process.

۱ Introduction

The process monitoring is a critical task in all industrial plant. It can be realized by the use of three principal approaches (Venkatasubramanian et al. 2003): (1) the analytical methods based on mathematics models. These methods compare the real-system outputs to the mathematical model outputs, (2) the methods based on knowledge (Stamatis 2003; Dhillon 2005) that use the human knowledge [risk analysis, failures modes effects and critically analysis (FMECA), decision trees], and (3) the databased methods that focus on statistic development of the process. The last kind of the method uses, generally, the control charts [(Page 1954), cumulative SUM (CUSUM) (Roberts 1959)] or exponentially weighted moving average (EWMA) (Alt et al. 1985) for the fault detection in the industrial process.

Currently, the manufacturing processes become more and more complex and multivariate. In these systems, the operator recuperates a vast data amount to be analysed. The high volume of data and the big number of process variables make the operator task fastidious. To avoid such problems, the data-based methods are more suitable for the process monitoring. The multivariate control charts [Hotelling T2 control chart, multivariate CUSUM (MCUSUM), multivariate EWMA (MEWMA)] have been used for the control of multivariate process and have proved their adequacy to reduce the complexity of such process monitoring. Moreover, the monitoring of a multivariate process is a complex task, and it can be devised into four subtasks which are: the detection of abnormal situation, the diagnosis of the faults, the identification of variables that involved in the faults and finally the reconfiguration of the process (Venkatasubramanian et al. 2003).

Many researches have used the control charts for process monitoring (Yu-Chang et al. 2015; Xia 2015; Ehsan and Sadigh 2014; Vijayababu and Rukmini 2014; Assareh et al. 2013). To identify the variables that make an out-ofcontrol in T2, a decomposition of the statistic T2 into independent terms has been suggested by Jing et al. (2008). The ‘‘MYT approach’’ has been applied by Mani and Cooper (1999) for the variables identification. The ‘‘MYT approach’’ has a big disadvantage which is the number of T2 decompositions. For a process with p variables, the number of decompositions is p!. To reduce this number and to identify the relationship among the variables, the Bayesian networks have been applied for variables identification by Friedman (2000), Li et al. (2006), Li and Shi (2007), Sylvain (2007).

In this paper, we regroup all the tasks of the multivariate process monitoring in one approach. Our contribution is to determine the best combination of multivariate control charts, neural networks, Bayesian networks, expert systems. The result of this research is a multi-agent system that applied to a multivariate process monitoring. This multi-agent system uses: multivariate control chart for abnormal detection, neural network for faults diagnosis, Bayesian network for variables identification and expert system for reconfiguration task.

The rest of this paper is organized as follows: the process monitoring approach is presented in ‘‘The proposed multi-agent system’’ section with the monitoring algorithm. In ‘‘Application of the proposed model on the Tennessee Eastman process’’ section, a case study of simulated Tennessee Eastman process (TEP) (Downs and Vogel 1993) is employed to illustrate the validity of the proposed approach, including the detection by multivariate control charts executor agent (MCCEA), diagnosis by diagnosis artificial neural network agent (DANNA), identification by Identification Bayesian network agent (IBNA) and the reconfiguration by reconfiguration agent (RA). Finally, conclusions and future works are suggested.

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