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Intelligent Monitoring by Interfacing Multivariate Statistical Monitoring and Knowledge-Based Systems
Abstract
An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariable process operation. The real-time KBS developed in G2 is used with multivariate SPM methods based on canonical variate state space (CVSS) process models. Fault detection is based on T² charts of state variables, contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T² values, and G2 Diagnostic Assistant (GDA) is used to diagnose the source causes of abnormal process behavior. The MSPM modules developed in Matlab are linked with G2. This setup extends the statistical process control library of GDA significantly and permits the use of MSPM tools for autocorrelated data and multivariable processes. The structure of the integrated system is described and its performance is illustrated by simulation studies.
... Since performance of the expert system is highly dependent on the correctness and completeness of the information stored in the knowledge base, updates to the knowledge base is necessary should the industrial process changes. The inference engine provides inference mechanisms to direct use of the knowledge, and the mechanisms typically include backward and forward chaining, hypothesis testing, heuristic search methods, and meta-rules (Prasad et al., 1998;Norvilas et al., 2000;Rao et al., 2000). Finally, the user interface translates user input into a computer understandable language and presents conclusions and explanations to the user. ...
... Currently, expert systems have been adopted in many industrial applications, including equipment maintenance, diagnosis and control, plant safety, and other areas in engineering. For example, Srihari (1989) discussed a framework of knowledge-based system in industrial applications, using it for the tasks of diagnosis, supervision, and control; Xia and Rao (1999a, b) built an expert system for operation support of pulp and paper manufacturing industries; Sun et al. (2000) and Uraikul et al. (2000) developed an expert system for optimizing natural gas pipeline network operations; Kritpiphat et al. (1998) implemented an expert system for intelligent monitoring and control of municipal water supply and distribution; Norvilas et al. (2000) developed an intelligent process monitoring and fault diagnosis environment by interfacing knowledge-based systems with multivariate statistical process monitoring techniques; Rao et al. (2000) developed an intelligent system for operation support for a boiler system and a chemical pulping process; Viharos and Monostori (2001) developed a hybrid system combining expert system and simulation for optimizing process chains and production planning; Wang et al. (1998Wang et al. ( , 2000 described the combination of expert system with neural networks for fault diagnosis of a transformer; and Prasad et al. (1998) applied the technology for constructing an operations support system for diagnosis and maintenance of a fluidized catalytic cracking unit and a paraxylene production unit. Fig. 1 is a schematic of a typical expert system architecture in process system applications (Tzafestas and Verbruggen, 1995). ...
Complex processes involve many process variables, and operators faced with the tasks of monitoring, control, and diagnosis of these processes often find it difficult to effectively monitor the process data, analyse current states, detect and diagnose process anomalies, or take appropriate actions to control the processes. The complexity can be rendered more manageable provided important underlying trends or events can be identified based on the operational data (Rengaswamy and Venkatasubramanian, 1992. An Integrated Framework for Process Monitoring, Diagnosis, and Control Using Knowledge-based Systems and Neural Networks. IFAC, Delaware, USA, pp. 49–54.). To assist plant operators, decision support systems that incorporate artificial intelligence (AI) and non-AI technologies have been adopted for the tasks of monitoring, control, and diagnosis. The support systems can be implemented based on the data-driven, analytical, and knowledge-based approach (Chiang et al., 2001. Fault Detection and Diagnosis in Industrial Systems. Springer, London, Great Britain). This paper presents a literature survey on intelligent systems for monitoring, control, and diagnosis of process systems. The main objectives of the survey are first, to introduce the data-driven, analytical, and knowledge-based approaches for developing solutions in intelligent support systems, and secondly, to present research efforts of four research groups that have done extensive work in integrating the three solutions approaches in building intelligent systems for monitoring, control and diagnosis. The four main research groups include the Laboratory of Intelligent Systems in Process Engineering (LISPE) at Massachusetts Institute of Technology, the Laboratory for Intelligent Process Systems (LIPS) at Purdue University, the Intelligent Engineering Laboratory (IEL) at the University of Alberta, and the Department of Chemical Engineering at University of Leeds. The paper also gives some comparison of the integrated approaches, and suggests their strengths and weaknesses.
Industrial continuous processes may have a large number of process variables and are usually operated for extended periods at fixed operating points under closed-loop control, yielding process measurements that are autocorrelated, cross-correlated, and collinear. A statistical process monitoring (SPM) method based on multivariate statistics and system theory is introduced to monitor the variability of such processes. The statistical model that describes the in-control variability is based on a canonical-variate (CV) state-space model that is an equivalent representation of a vector autoregressive moving-average time-series model. The CV state variables obtained from the state-space model are linear combinations of the past process measurements that explain the variability of the future measurements the most. Because of this distinctive feature, the CV state variables are regarded as the principal dynamic directions A T2 statistic based on the CV state variables is used for developing an SPM procedure. Simple examples based on simulated data and an experimental application based on a high-temperature short-time milk pasteurization process illustrate advantages of the proposed SPM method.
Statistical process control methods for monitoring processes with multivariate measurements in both the product quality variable space and the process variable space are considered. Traditional multivariate control charts based on X2 and T2 statistics are shown to be very effective for detecting events when the multivariate space is not too large or ill-conditioned. Methods for detecting the variable(s) contributing to the out-of-control signal of the multivariate chart are suggested. Newer approaches based on principal component analysis and partial least squares are able to handle large ill-conditioned measurement space; they also provide diagnostics which can point to possible assignable causes for the event. The me hods are illustrated on a simulated process of a high pressure low density polyethylene reactor, and examples of their application to a variety of industrial processes are referenced.
Very general reduced order filtering and modeling problems are phased in terms of choosing a state based upon past information to optimally predict the future as measured by a quadratic prediction error criterion. The canonical variate method is extended to approximately solve this problem and give a near optimal reduced-order state space model. The approach is related to the Hankel norm approximation method. The central step in the computation involves a singular value decomposition which is numerically very accurate and stable. An application to reduced-order modeling of transfer functions for stream flow dynamics is given.
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