Information Entropy Of System Research Articles

Graphodynamic System with Information Entropy Control as a Means of Control of Production Systems

Ensuring the quality of products and increasing the level of safety in the operation of production systems are the main tasks of all industries. The second task is the most relevant for technogenic systems of increased danger, which include, among other things, the production systems of the oil and gas industry. The management of production systems and the solution of selected tasks is carried out by the decision maker, using the parameters obtained during the diagnosis and monitoring of the system. The formation of a complex of diagnostic parameters of the production system is implemented by the decision maker, taking into account the loss of information, characterized by incomplete control of the parameters. When identifying the state of the production system, the decision maker must take into account not only the values of diagnostic parameters, but also the value of the information entropy of the system that they form, since "entropy characterizes the uncertainty of control, i.e. its management quality". Thus, the identification of the state of the production system and its management are only possible with the mandatory control of the information entropy of the system by the decision maker. The latter confirms the relevance of the synthesis of control systems with control of the state of their information entropy. Consider the production system as a graphodynamic system. The data characterizing its input, output, internal state and control action will be presented in the form of graphs. Through the analysis of diagnostic parameters, the decision maker identifies the state of the production system. A certain time is spent on obtaining and analyzing diagnostic parameters, which in some cases can be significant and lead to an increase in the value of the information entropy of the controlled system, which leads to a mismatch between the actual state of the system and its image processed by the decision maker. In the conditions of big production systems, taking into account funding constraints or other reasons, the frequency of monitoring the diagnostic parameters of the system may be violated. This leads to a significant increase in the information entropy of the controlled system and, accordingly, to a low quality of its control. The proposed method for controlling a collector-beam system for collecting products from a gas condensate field makes it possible to improve the quality of its control by implementing control of its state of information entropy. The developed graph-dynamic model of data storage of a collector-beam system for collecting gas condensate field products allows the decision maker to analyze its state in any period of operation.

Information entropy of systems of technical diagnostics of locomotives

Information entropy of systems of technical diagnostics of locomotives

Determination of the amount of entropy of non-recoverable elements of the technical system

A mathematical model for determining the amount of information applied to non-recoverable elements of the technical system is proposed. The quantitative indicators of the model are the probability of failure-free operation; frequency of failure; failure rate and mean time to the first failure. One of the criteria for assessing the reliability of the system adopted information entropy, which is considered as a measure of uncertainty of the outcome of a random event. The model allows to express the reliability indicators through information entropy for the analysis of the state of elements and the system. Multiplicative and additive properties of entropy growth in the case of aging and testing of system elements for resistance to failures are considered. An integral part of the model is a method for calculating the information entropy of systems consisting of non-recoverable elements. The method is based on the Shannon’s model to determine the amount of information and allows to calculate the entropy for both operable and non-operable states of the system. The peculiarity of the method is as follows: 1) the probabilities of the operable state and the probability of failure in the case of considering the flow process of system states without taking into account the individual properties of the element are used to determine the information entropy. In this case, the result of the calculation allows to monitor the manifestation of randomness in the opposite states of the system; 2) it uses the individual properties of the element characterized by private (or own) entropy. With this approach to the calculations, the obtained quantitative values of entropy allow to monitor the growth rate of the states of each element and system as a whole. The method is tested on the presented example.

An Entropy Based Bayesian Network Framework for System Health Monitoring

Oil pipeline network system health monitoring is important primarily due to the high cost of failure consequences. Optimal sensor selection helps provide more effective system health information from the perspective of economic and technical constraints. Optimization models confront different issues. For instance, many oil pipeline system performance models are inherently nonlinear, requiring nonlinear modelling. Optimization also confronts modeling uncertainties. Oil pipeline systems are among the most complicated and uncertain dynamic systems, as they include human elements, complex failure mechanisms, control systems, and most importantly component interactions. In this paper, an entropy-based Bayesian network optimization methodology for sensor selection and placement under uncertainty is developed. Entropy is a commonly used measure of information often been used to characterize uncertainty, particularly to quantify the effectiveness of measured signals of sensors in system health monitoring contexts. The entropy based Bayesian network optimization outlined herein also incorporates the effect that sensor reliability has on system information entropy content, which can also be related to the sensor cost. This approach is developed further by incorporating system information entropy and sensor costs in order to evaluate the performance of sensor combinations. The paper illustrates the approach using a simple oil pipeline network example. The so-called particle swarm optimization algorithm is used to solve the multi-objective optimization model, establishing the Pareto frontier.

Мониторинг информационной энтропии в задачах описания больших технических систем

The complexity of control of large technical systems coupled with the time-consuming process of obtaining and processing of the factual data from the operational documentation contributes to the situation with discrepancies in the real state of the system and the status reflected in the documentation. This reduces the information entropy of the system and the number of incorrect management decisions. Thus, adoption of control solutions for large and complex technical systems is only possible, if they meet the conditions of the admissible value of the entropy. The task of monitoring of the information entropy of large complex technical systems should be solved with an automated control of quantifying the information entropy of the system, which leads to conclusion about a possibility of making control solutions based on the stored factual data. The paper describes the method of a qualimetric evaluation of the information entropy of large technical systems based on operational models of a semistructured content. This method is based on an automated analysis of a group of decision-makers of the factual content associated with the operational documentation generated during operation of the identified system. Use of the semistructured content model implements a universal interface with access to the factual data of the electronic documents, which reduces the number of the processing errors and increases the processing speed of the factual content. The latter reduces the total information entropy of the driven system.

The Application of Minimum Spanning Tree Clustering in the Ontology Mapping Pretreatment

Screening candidates is the key step to improve the efficiency of ontology mapping. Minimum spanning tree clustering is one of the important ways of graph clustering algorithm. Defining the related concepts and methods first, according to the characteristics of the ontology file itself, Select graph clustering of minimum spanning tree clustering algorithm, To screening candidates of participate in the concept of mapping, Aiming at the deficiency and improvement of objective function in the algorithm, based on the system information entropy instead of the complicated calculation of similarity to supervise the clustering. To reduce the computational scale and improve the efficiency.