Vulnerability of water distribution networks with real-life pipe failure statistics

Reasons for occasional, random pipe bursts in water distribution networks (WDNs) might come from numerous factors (e.g. pH value of the soil, the pipeline material). Still, the isolation of the damaged section is inevitable. While the corresponding area is segregated by closing the isolation valves, there is a shortfall in drinking water service. This paper analyses the vulnerability of segments of WDNs from the viewpoint of the consumers that is the product of the failure rate and the relative demand loss. Real pipe failure database, pipe material and pipe age data are used to increase the accuracy of the failure rate estimation for 27 real-life WDNs from Hungary. The vulnerability analysis revealed the highly exposed nature of the local vulnerabilities; the distribution of local vulnerability values follows a power-law distribution. This phenomenon is also found by investigating the artificial WDNs from the literature using N rule in terms of isolation valve layout, namely the ky networks, with similar results.

Technology for restoring functional dependencies to determine reliability parameters

The problem of determining the properties of the object by analyzing the numerical and qualitative characteristics of a discrete sample is considered. A method has been developed to determine the probability of trouble-free operation of electronic systems for the case if the interpolation fields are different between several interpolation nodes. A method has been developed to determine the probability of trouble-free operation if the interpolation polynomial is the same for the entire interpolation domain. It is shown that local interpolation methods give more accurate results, in contrast to global interpolation methods. It is shown that in the case of global interpolation it is possible to determine the value of the function outside the given values by extrapolation methods, which makes it possible to predict the probability of failure. It is shown that the use of approximation methods to determine the probability of trouble-free operation reduces the error of the second kind. A method for analyzing the qualitative characteristics of functional dependences has been developed, which allows us to choose the optimal interpolation polynomial. With sufficient statistics, using the criteria of consent, it is possible to build mathematical models for the analysis of failure statistics of electronic equipment. Provided that the volume of statistics is not large, such statistics may not be sufficient and the application of consent criteria will lead to unsatisfactory results. Another approach is to use an approximation method that is applied to statistical material that was collected during testing or controlled operation. In this regard, it is extremely important to develop a method for determining the reliability of electronic systems in case of insufficiency of the collected statistics of failures of electronic equipment.

ESTIMATION OF LOADING CAPACITY OF A DETAIL SUBJECTED TO CYCLIC LOAD

A method for assessing the current load capacity of a component of a complex mechanical system based on the failure statistics of similar parts and the actual loading history is proposed. A hidden semi-Markov model of a fatigue crack is considered. As a criterion for the load capacity, the safety factor is considered as a random variable, the distribution function of which is found by the proposed method. The scaling of the approach from detail to the system as a whole is assumed using artificial intelligence methods.

Application of intelligent analysis to identify defective vehicle components

The article shows the possibility of using intelligent analysis in a vehicle service when assessing the vehicle reliability. It was hypothesized that the use of association rules in diagnostics can increase the speed of repairs and the quality of customer service, allowing to identify the nodes that are highly likely to be faulty at the same time. For this, a knowledge base was built from the patterns obtained by applying association rules to the vehicle failure statistics. An application was implemented, which, on its basis, issues recommendations to the repair worker to check certain nodes based on the already identified defective nodes entered into the program. The proposed technique, together with the developed software tool, will optimize the diagnostic processes.

Predicting anomaly conditions of energy equipment using neural networks

In modern conditions for complex thermal power facilities, the issue of developing methods for predicting equipment failures is especially relevant. Methods based on the intellectualization of diagnostic systems and allowing to obtain predictive models based on the use of both current data received in real time from measuring equipment and retrospective information are considered promising. Intellectualization of the system in terms of the ability to learn allows to quickly adjust the parameters of forecasting models under changing conditions of equipment operation, to determine new deadlines for scheduled repairs and minimize equipment downtime. A limitation of the use of methods is the incompleteness of failure statistics, ie when equipment failures are rare or non-existent. Such diagnostics of energy equipment, especially thermal power facilities, contributes to a more environmentally friendly production.