Derivation of diagnostic models based on formalized process knowledge

© IFAC.Industrial systems are vulnerable to faults. Early and accurate detection and diagnosis in production systems can minimize down-time, increase the safety of the plant operation, and reduce manufacturing costs. Knowledge- and model-based approaches to automated fault detection and diagnosis have been demonstrated to be suitable for fault cause analysis within a broad range of industrial processes and research case studies. However, the implementation of these methods demands a complex and error-prone development phase, especially due to the extensive efforts required during the derivation of models and their respective validation. In an effort to reduce such modeling complexity, this paper presents a structured causal modeling approach to supporting the derivation of diagnostic models based on formalized process knowledge. The method described herein exploits the Formalized Process Description Guideline VDI/VDE 3682 to establish causal relations among key-process variables, develops an extension of the Signed Digraph model combined with the use of fuzzy set theory to allow more accurate causality descriptions, and proposes a representation of the resulting diagnostic model in CAEX/AutomationML targeting dynamic data access, portability, and seamless information exchange

Hybrid fuzzy and sliding-mode control for motorised tether spin-up when coupled with axial vibration

A hybrid fuzzy sliding mode controller is applied to the control of motorised tether spin-up coupled with an axial oscillation phenomenon. A six degree of freedom dynamic model of a motorised momentum exchange tether is used as a basis for interplanetary payload exchange. The tether comprises a symmetrical double payload configuration, with an outrigger counter inertia and massive central facility. It is shown that including axial elasticity permits an enhanced level of performance prediction accuracy and a useful departure from the usual rigid body representations, particularly for accurate payload positioning at strategic points. A special simulation program has been devised in MATLAB and MATHEMATICA for a given initial condition data case

Razvoj proširenja xquery interpretera baziran na fazi logici sa prioritetima

In many real-world applications, information is often imprecise and uncertain. With the popularity of web-based applications, huge amounts of data are available on the web, and XML (eXtensible Markup Language) has become the de facto standard for data exchange over the internet. The XQuery is the language for querying XML data. However, XML and XQuery suffer from incapability of representing and manipulating imprecise and uncertain data. Consequently, this work represents fuzzy data in XML documents and extends XQuery language as providing a more flexible XQuery language by using the fuzzy set theory. In this thesis, an extension of the XQuery query, called Fuzzy XQuery is described. It allows users to define priority, threshold and fuzzy expressions in their queries. Users also can predefine linguistic terms to use them in querying. An algorithm for calculating the global constraint satisfaction degree using the Generalized Prioritized Fuzzy Constraint Satisfaction Problem (GPFCSP) is introduced. Furthermore, Fuzzy XQuery Interpreter (FXI) is implemented allowing execution of fuzzy XQuery queries based on open source technologies and native XML open- source database. Additionally, innovative methods for computing fuzzy set compatibility and introducing order over fuzzy sets have been implemented, which give serious improvements in computational performance compared to previous implementations

On the similarity relation within fuzzy ontology components

Ontology reuse is an important research issue. Ontology merging, integration, mapping, alignment and versioning are some of its subprocesses. A considerable research work has been conducted on them. One common issue to these subprocesses is the problem of defining similarity relations among ontologies components. Crisp ontologies become less suitable in all domains in which the concepts to be represented have vague, uncertain and imprecise definitions. Fuzzy ontologies are developed to cope with these aspects. They are equally concerned with the problem of ontology reuse. Defining similarity relations within fuzzy context may be realized basing on the linguistic similarity among ontologies components or may be deduced from their intentional definitions. The latter approach needs to be dealt with differently in crisp and fuzzy ontologies. This is the scope of this paper.ou

Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources

Nowadays, the proliferation of heterogeneous data sources provided by different research and innovation projects and initiatives is proliferating more and more and presents huge opportunities. These developments create an increase in the number of different data sources, which could be involved in the process of decisionmaking for a specific purpose, but this huge heterogeneity makes this task difficult. Traditionally, the expert systems try to integrate all information into a main database, but, sometimes, this information is not easily available, or its integration with other databases is very problematic. In this case, it is essential to establish procedures that make a metadata distributed integration for them. This process provides a “mapping” of available information, but it is only at logic level. Thus, on a physical level, the data is still distributed into several resources. In this sense, this chapter proposes a distributed rule engine extension (DREE) based on edge computing that makes an integration of metadata provided by different heterogeneous data sources, applying then a mathematical decomposition over the antecedent of rules. The use of the proposed rule engine increases the efficiency and the capability of rule-based expert systems, providing the possibility of applying these rules over distributed and heterogeneous data sources, increasing the size of data sets that could be involved in the decision-making process

An intelligent system for risk classification of stock investment projects

The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange

Using intelligent optimization methods to improve the group method of data handling in time series prediction

In this paper we show how the performance of the basic algorithm of the Group Method of Data Handling (GMDH) can be improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The new improved GMDH is then used to predict currency exchange rates: the US Dollar to the Euros. The performance of the hybrid GMDHs are compared with that of the conventional GMDH. Two performance measures, the root mean squared error and the mean absolute percentage errors show that the hybrid GMDH algorithm gives more accurate predictions than the conventional GMDH algorithm

The Semantic Web Paradigm for a Real-Time Agent Control (Part I)

For the Semantic Web point of view, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. Adding logic to the Web, the means to use rules to make inferences, choose courses of action and answer questions, is the actual task for the distributed IT community. The real power of Intelligent Web will be realized when people create many programs that collect Web content from diverse sources, process the information and exchange the results with other programs. The first part of this paper is an introductory of Semantic Web properties, and summarises agent characteristics and their actual importance in digital economy. The second part presents the predictability of a multiagent system used in a learning process for a control problem.Semantic Web, agents, fuzzy knowledge, evolutionary computing