Tomasz Rogala

Development Environment for Diagnostic Multimodal Statement Networks

Development of effective diagnostic systems for the recognition of technical conditions of complex objects or processes requires the use of knowledge from multiple sources. Gathering of diagnostic knowledge acquired from diagnostic experiments as well as independent experts in the form of an information system database is one of the most important stages in the process of designing diagnostic systems. The task can be supported through suitable modeling activities and diagnostic knowledge management. Briefly, this paper presents an example of an application of multimodal diagnostic statement networks for the purpose of knowledge representation. Multimodal statement networks allow for approximate diagnostic reasoning based on a knowledge that is imprecise or even contradictory in part. The authors also describe the software environment REx for the development and testing of multimodal statement networks. The environment is a system for integrating knowledge from various sources and from independent domain experts in particular.

Multimodal Statement Networks for Organic Rankine Cycle Diagnostics – Use Case of Diagnostic Modeling

The paper shows an example of application of modeling process of diagnostic knowledge with the use of multimodal statement networks. The goal is to present generic approach to modeling complex diagnostic domains by many independent experts. As an object cogeneration power plant with Organic Rankine Cycle is presented. Implementation of diagnostic model was performed using REx system that enables among other things knowledge management and the application and preliminary evaluation of diagnostic knowledge of gathered knowledge for the purpose of its further incorporation to diagnostic systems.

Indirect Method of Learning Weights of Intuitionistic Statement Networks

The paper presents an indirect method learning weights for intuitionistic statement networks. The intuitionistic statement network is a graphical model which represents some associations between statements in the form of the graph. An important element of this model apart from structure represented by the graph are also its parameters that determine the impact of individual statements to other statements. The values of weights are computed for different values of primary statements associated with external sources of observed data. Primary statement values are represented by intuitionistic values and can represent accurate values, which correspond to logical true or false or they represent approximate values. The presented method allows to identification of weights’ values for every instance of evidence of primary statements regardless whether it is an accurate or an approximate value. The estimation of these weights is performed with the application of substitutional generative model represented by Bayesian network. The presented method allows the identification of weights also based on incomplete data.

Application of the REx5 Expert System to Condition Monitoring of the Simple Liquid Flow Process Control Trainer

This work deals with the problems that are related to the condition monitoring of the selected technical object. For this purpose, specialized software called REx5 was developed. The REx5 is a modular expert system. Inference module of this system allows to conduct the inference process for two types of knowledge representation: the intuitionistic and Bayesian statement networks. All data required for the inference processes are stored in especially designed OPC UA Server with archive database of process data. Another module, designed to manage diagnostic tasks, allows to configure the parameters for condition monitoring of observed technical object. The simple liquid flow process control trainer is the studied object. This trainer allows for various control functions, like the control of the medium level in the tank, control of the flow rate, or control of the medium temperature. Additionally, the trainer is equipped with a data acquisition subsystem. The purpose of this subsystem is the acquisition values of the selected process signals and sharing these data for external systems used by OPC UA Server.

Application of the DiaSter System

The functions of the DiaSter system are realized by means of specialized user packages. These packages cooperate by means of the system software platform. Particular packages can be developed as specialized libraries of modeling, system variables processing and visualization modules or as stand-alone software modules. Individual packages or a specified set of packages can be used to implement particular tasks connected with process modeling, supervisory control, optimization and diagnostics. The functions of individual packages complete each other, giving the possibility to realize even more complex tasks.